Prof. Axel Wismüller Profile

Prof. Axel Wismüller

Professor of Radiology

SPIE Involvement:

Conference Program Committee | Author

Publications (99)

Proceedings Article | 4 October 2024 Presentation + Paper

Functional connectivity-based classification of autism spectrum disorder using mutual connectivity analysis with local models

Akhil Kasturi, Ali Vosoughi, Nathan Hadjiyski, Larry Stockmaster, Axel Wismüller

Proceedings Volume 13118, 1311805 (2024) https://doi.org/10.1117/12.3027895

KEYWORDS: Magnetic resonance imaging, Brain, Medical imaging, Photonics, Biomedical applications, Functional imaging, Functional magnetic resonance imaging, Biological research

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Proceedings Article | 4 October 2024 Presentation + Paper

Leveraging a memory-driven transformer for efficient radiology report generation from chest x-rays to establish a quantitative metric

Nathan Hadjiyski, Akhil Kasturi, Ali Vosoughi, Axel Wismüller

Proceedings Volume 13118, 1311804 (2024) https://doi.org/10.1117/12.3027803

KEYWORDS: Education and training, Radiology, Performance modeling, Transformers, Data modeling, Chest imaging, Machine learning, Lung, X-rays, Feature extraction

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Proceedings Article | 4 October 2024 Presentation

Artificial intelligence in medical imaging: from bench to bedside

Axel Wismüller

Proceedings Volume PC13118, PC131180K (2024) https://doi.org/10.1117/12.3025553

KEYWORDS: Artificial intelligence, Radiology, Medical imaging, Clinical practice, Medicine, Time metrology, Quality measurement, Pathology, Image analysis, Diagnostics

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Proceedings Article | 3 April 2024 Poster + Paper

Anatomical landmark detection in chest x-ray images using transformer-based networks

Akhil Kasturi, Ali Vosoughi, Nathan Hadjiyski, Larry Stockmaster, William Sehnert, Axel Wismüller

Proceedings Volume 12927, 129272Q (2024) https://doi.org/10.1117/12.3006881

KEYWORDS: Medical imaging, Anatomy, Biomedical applications, Chest imaging, Transformers, Image segmentation

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Proceedings Article | 2 April 2024 Presentation + Paper

Classification of endotracheal tube position in chest x-rays images

Akhil Kasturi, Ali Vosoughi, Nathan Hadjiyski, Larry Stockmaster, William Sehnert, Axel Wismüller

Proceedings Volume 12930, 129300L (2024) https://doi.org/10.1117/12.3006899

KEYWORDS: Medical imaging, Biomedical applications, Chest imaging, Artificial intelligence, Education and training

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Proceedings Article | 2 April 2024 Presentation + Paper

Segmentation of catheter tubes and lines in chest x-rays using deep learning models

Akhil Kasturi, Ali Vosoughi, Nathan Hadjiyski, Larry Stockmaster, William Sehnert, Axel Wismüller

Proceedings Volume 12930, 129300I (2024) https://doi.org/10.1117/12.3006908

KEYWORDS: Image segmentation, Medical imaging, Chest imaging, Biomedical applications, Transformers

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Proceedings Article | 2 April 2024 Paper

Enhancing graph attention neural network performance for marijuana consumption classification through large-scale Augmented Granger Causality (lsAGC) analysis of functional MR images

Ali Vosoughi, Akhil Kasturi, Axel Wismüller

Proceedings Volume 12930, 129300O (2024) https://doi.org/10.1117/12.3008187

KEYWORDS: Brain, Neural networks, Magnetic resonance imaging, Image classification, Functional magnetic resonance imaging, Analytical research, Neuroimaging, Network security, Dynamical systems, Dimension reduction

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In the present research, the effectiveness of large-scale Augmented Granger Causality (lsAGC) as a tool for gauging brain network connectivity was examined to differentiate between marijuana users and typical controls by utilizing resting-state functional Magnetic Resonance Imaging (fMRI). The relationship between marijuana consumption and alterations in brain network connectivity is a recognized fact in scientific literature. This study probes how lsAGC can accurately discern these changes. The technique used integrates dimension reduction with the augmentation of source time-series in a model that predicts time-series, which helps in estimating the directed causal relationships among fMRI time-series. As a multivariate approach, lsAGC uncovers the connection of the inherent dynamic system while considering all other time-series. A dataset of 60 adults with an ADHD diagnosis during childhood, drawn from the Addiction Connectome Preprocessed Initiative (ACPI), was used in the study. The brain connections assessed by lsAGC were utilized as classification attributes. A Graph Attention Neural Network (GAT) was chosen to carry out the classification task, particularly for its ability to harness graph-based data and recognize intricate interactions between brain regions, making it appropriate for fMRI-based brain connectivity data. The performance was analyzed using a five-fold cross-validation system. The average accuracy achieved by the correlation coefficient method was roughly 52.98%, with a 1.65 standard deviation, whereas the lsAGC approach yielded an average accuracy of 61.47%, with a standard deviation of 1.44. A random guess method yielded an average accuracy of about 47.05%, with a standard deviation of around 6.25. The study indicates that lsAGC, when paired with a Graph Attention Neural Network, has the potential to serve as a novel biomarker for pinpointing marijuana users, offering a superior and consistent classification strategy over traditional functional connectivity techniques, including the random guess method. The suggested method enhances the body of knowledge in the field of neuroimaging-based classification and emphasizes the necessity to consider directed causal connections in brain network connectivity analysis when studying marijuana’s effects on the brain.

Proceedings Article | 2 April 2024 Presentation + Paper

Graph attention transformers and large-scale granger causality to classify marijuana consumption from functional MR images

Ali Vosoughi, Akhil Kasturi, Axel Wismüller

Proceedings Volume 12930, 1293014 (2024) https://doi.org/10.1117/12.3008188

KEYWORDS: Brain, Transformers, Magnetic resonance imaging, Image classification, Functional magnetic resonance imaging, Data modeling, Neural networks, Cross validation, Neuroimaging, Network security

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In this study, we explore the potential of large-scale Granger Causality (lsGC) estimated brain network connectivity as a biomarker for classifying marijuana users from typical controls using resting-state functional Magnetic Resonance Imaging (fMRI). It is well-established in the literature that marijuana use is associated with alterations in brain network connectivity, and we investigate whether lsGC can effectively capture such changes. The lsGC method, a multivariate approach based on dimension reduction and predictive time-series modeling, allows for estimating directed causal relationships among fMRI time series, considering the interdependence of time series within the underlying dynamic system. We employ a dataset consisting of 60 adult subjects with a childhood diagnosis of ADHD from the Addiction Connectome Preprocessed Initiative (ACPI) database. Brain connections estimated using lsGC are extracted as features for classification. We utilize a Graph Attention Neural Network (GAT) to accomplish the classification task. The GAT model is specifically chosen for its ability to leverage graph-based data and capture complex interactions between brain regions, making it well-suited for handling fMRI-based brain connectivity data. To assess the performance of our approach, we employ a cross-validation scheme with five-fold cross-validation. The mean accuracy computed for the correlation coefficient method is approximately 53.78%, with a standard deviation of about 4.80, while the mean accuracy for our approach, lsGC, is approximately 64.89%, with a standard deviation of 1.10. The findings suggest that lsGC, in conjunction with a Graph Attention Neural Network, holds promise as a potential biomarker for identifying marijuana users, providing a more effective and reliable classification approach than conventional functional connectivity measures. The proposed methodology offers a valuable contribution to neuroimaging-based classification studies and highlights the importance of considering directed causal relationships in brain network connectivity analysis when investigating the impact of marijuana use on the brain.

Proceedings Article | 28 September 2023 Presentation + Paper

Leveraging large-scale Granger causality and neural networks to measure the level of consciousness in DoC patients

Ali Vosoughi, Theresa Raiser, Tina Luther, Lina Willacker, Martin Rosenfelder, Akhil Kasturi, Jacobo Diego Sitt, Dragana Manasova, Angela Comanducci, Andreas Bender, Axel Wismüller

Proceedings Volume 12655, 1265507 (2023) https://doi.org/10.1117/12.2677317

KEYWORDS: Magnetic resonance imaging, Consciousness, Medical imaging, Neural networks, Matrices, Functional imaging

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Proceedings Article | 28 September 2023 Presentation + Paper

Detecting landmarks in anatomical medical images using transformer-based networks

Akhil Kasturi, Ali Vosoughi, Nathan Hadjiyski, Larry Stockmaster, William Sehnert, Axel Wismüller

Proceedings Volume 12655, 1265508 (2023) https://doi.org/10.1117/12.2678538

KEYWORDS: Deep learning, Transformers, Medical imaging, Biomedical applications, Image segmentation, Chest imaging, Education and training

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Proceedings Article | 10 April 2023 Presentation + Paper

Large-scale Augmented Granger Causality (lsAGC) for discovery of causal brain connectivity networks in schizophrenia patients using functional MRI neuroimaging

Axel Wismüller, Ali Vosoughi, Akhil Kasturi, Nathan Hadjiyski

Proceedings Volume 12468, 1246817 (2023) https://doi.org/10.1117/12.2654502

KEYWORDS: Medical imaging, Functional imaging, Matrices, Machine learning, Dimension reduction

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Proceedings Article | 10 April 2023 Presentation + Paper

Tracking the impact of global iodinated contrast agent shortage on radiology: analysis of CT exam volumes at a major US healthcare

Axel Wismüller, Jerome Avondo, Larry Stockmaster, Akhil Kasturi, Ali Vosoughi

Proceedings Volume 12468, 124680X (2023) https://doi.org/10.1117/12.2654552

KEYWORDS: Magnetic resonance imaging, Computed tomography, Biomedical optics, Medical imaging, Medicine, Radiology, Photonics, Contrast agents, Biomedical applications, Functional imaging

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Proceedings Article | 10 April 2023 Presentation + Paper

Identification of schizophrenia patients using large-scale Extended Granger Causality (lsXGC) in functional MR imaging

Axel Wismüller, Ali Vosoughi, Akhil Kasturi, Nathan Hadjiyski

Proceedings Volume 12468, 1246816 (2023) https://doi.org/10.1117/12.2654499

KEYWORDS: Machine learning, Machine vision, Dimension reduction, Dynamical systems, Medical imaging

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Proceedings Article | 10 April 2023 Presentation + Paper

Large-Scale Granger Causality (lsGC) for classification of schizophrenia using functional MRI

Axel Wismüller, Ali Vosoughi, Akhil Kasturi, Nathan Hadjiyski

Proceedings Volume 12468, 1246818 (2023) https://doi.org/10.1117/12.2654504

KEYWORDS: Medical imaging, Matrices, Machine learning, Statistical signal processing, Digital signal processing

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Proceedings Article | 7 April 2023 Presentation + Paper

Classification of schizophrenia using large-scale kernelized Granger causality (lsKGC) and functional MR imaging

Ali Vosoughi, Akhil Kasturi, Nathan Hadjiyski, Axel Wismüller

Proceedings Volume 12465, 124650A (2023) https://doi.org/10.1117/12.2654506

KEYWORDS: Computer aided detection, Machine vision, Dimension reduction, Digital signal processing, Medical imaging, Matrices

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Proceedings Article | 7 April 2023 Poster + Paper

Cross modal global local representation learning from radiology reports and x-ray chest images

Nathan Hadjiyski, Ali Vosoughi, Axel Wismüller

Proceedings Volume 12465, 1246531 (2023) https://doi.org/10.1117/12.2654520

KEYWORDS: Machine learning, Deep learning, Machine vision, Artificial intelligence

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Proceedings Article | 12 October 2022 Presentation

Artificial intelligence in radiology: from machine learning to clinical application

Axel Wismüller

Proceedings Volume 12204, 122040C (2022) https://doi.org/10.1117/12.2635955

KEYWORDS: Radiology, Machine learning, Artificial intelligence

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Proceedings Article | 27 May 2022 Presentation + Paper

Self-supervised learning in medical imaging: anomaly detection in MRI using autoencoders

Axel Wismüller, M. Ali Vosoughi

Proceedings Volume 12102, 1210206 (2022) https://doi.org/10.1117/12.2622233

KEYWORDS: Magnetic resonance imaging, Medical imaging, Neural networks, Computer vision technology, Machine learning, Machine vision

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Proceedings Article | 27 May 2022 Presentation + Paper

Re-defining radiology quality assurance (QA): artificial intelligence (AI)-based QA by restricted investigation of unequal scores (AQUARIUS)

Axel Wismüller, Larry Stockmaster, M. Ali Vosoughi

Proceedings Volume 12101, 121010E (2022) https://doi.org/10.1117/12.2622234

KEYWORDS: Radiology, Artificial intelligence, Computed tomography, Head, Image analysis, Medicine, Medical imaging, Image processing, Scanners, Safety

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Proceedings Article | 4 April 2022 Presentation + Paper

Large-scale Kernelized Granger Causality (lsKGC) for inferring topology of directed graphs in brain networks

M. Ali Vosoughi, Axel Wismüller

Proceedings Volume 12036, 1203603 (2022) https://doi.org/10.1117/12.2613459

KEYWORDS: Magnetic resonance imaging, Medical imaging, Functional magnetic resonance imaging, Functional imaging, Computer aided diagnosis and therapy, Machine learning, Statistical inference, Probability theory, Information theory

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Proceedings Article | 4 April 2022 Presentation + Paper

Exploring directed network connectivity in complex systems using large-scale augmented Granger causality (lsAGC)

Axel Wismüller, M. Ali Vosoughi, Adora DSouza, Anas Abidin

Proceedings Volume 12033, 120330O (2022) https://doi.org/10.1117/12.2611301

KEYWORDS: Magnetic resonance imaging, Functional magnetic resonance imaging, Brain, Medical imaging, Functional imaging, Computer aided diagnosis and therapy, Machine learning, Statistical inference

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Proceedings Article | 4 April 2022 Presentation + Paper

Inferring functional relations from synthetic fMRI data using large-scale Nonlinear Granger Causality (lsNGC)

Axel Wismüller, M. Ali Vosoughi, Adora DSouza, Anas Abidin

Proceedings Volume 12036, 120360A (2022) https://doi.org/10.1117/12.2613389

KEYWORDS: Magnetic resonance imaging, Medical imaging, Brain, Functional imaging, Neural networks, Computer aided diagnosis and therapy, Information theory, Machine learning, Statistical inference, Networks

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Proceedings Article | 4 April 2022 Presentation + Paper

Investigation of large-scale extended Granger causality (lsXGC) on synthetic functional MRI data

Axel Wismüller, M. Ali Vosoughi, Adora DSouza, Anas Abidin

Proceedings Volume 12036, 1203609 (2022) https://doi.org/10.1117/12.2613380

KEYWORDS: Functional magnetic resonance imaging, Medical imaging, Functional imaging, Computer aided diagnosis and therapy, Machine learning, Statistical inference, Networks, Information theory

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Proceedings Article | 15 February 2021 Presentation + Paper

Investigating Covid-19 pandemic-induced effects on detection of emergent clinical imaging findings by large-scale tracking of utilization and reading results for AI-based image analysis services

Axel Wismüller, Larry Stockmaster, M. Ali Vosoughi

Proceedings Volume 11600, 116000T (2021) https://doi.org/10.1117/12.2582307

KEYWORDS: Magnetic resonance imaging, Medicine, Artificial intelligence, Medical imaging, Biomedical optics, Photonics, Head, Computed tomography, Brain, Biological research

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Proceedings Article | 15 February 2021 Presentation + Paper

Classification of schizophrenia from functional MRI using large-scale extended Granger causality

Axel Wismüller, M. Ali Vosoughi

Proceedings Volume 11597, 115971G (2021) https://doi.org/10.1117/12.2582039

KEYWORDS: Magnetic resonance imaging, Functional magnetic resonance imaging, Data modeling, Brain, Receivers, Medical research, Feature selection, Feature extraction, Dynamical systems, Dimension reduction

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Proceedings Article | 15 February 2021 Presentation + Paper

Large-scale extended Granger causality for classification of marijuana users from functional MRI

M. Ali Vosoughi, Axel Wismüller

Proceedings Volume 11600, 116000D (2021) https://doi.org/10.1117/12.2582310

KEYWORDS: Magnetic resonance imaging, Functional magnetic resonance imaging, Brain, Receivers, Feature selection, Feature extraction, Dynamical systems, Dimension reduction, Databases, Data modeling

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Proceedings Article | 15 February 2021 Presentation + Paper

Network connectivity analysis in complex systems using large-scale non-linear Granger causality (lsNGC)

Axel Wismüller, Adora DSouza, M. Ali Vosoughi, Anas Abidin

Proceedings Volume 11600, 1160008 (2021) https://doi.org/10.1117/12.2582265

KEYWORDS: Network security, Complex systems, Functional magnetic resonance imaging, Statistical analysis, Analytical research, Matrices, Neuroimaging, Magnetic resonance imaging, Dimension reduction

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Proceedings Article | 15 February 2021 Presentation + Paper

Large-scale augmented Granger causality (lsAGC) for connectivity analysis in complex systems: from computer simulations to functional MRI (fMRI)

Axel Wismüller, M. Ali Vosoughi

Proceedings Volume 11600, 1160009 (2021) https://doi.org/10.1117/12.2582152

KEYWORDS: Functional magnetic resonance imaging, Complex systems, Magnetic resonance imaging, Computer simulations, Analytical research, Signal to noise ratio, Matrices, Systems modeling, Receivers, Network security

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Proceedings Article | 16 March 2020 Presentation + Paper

Attention-deficit/hyperactivity disorder prediction using graph convolutional networks

Seyed Saman Saboksayr, John Foxe, Axel Wismüller

Proceedings Volume 11314, 113141U (2020) https://doi.org/10.1117/12.2551364

KEYWORDS: Brain, Convolution, Data modeling, Functional magnetic resonance imaging, Magnetic resonance imaging, Radiology, Signal processing, Matrices

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Proceedings Article | 16 March 2020 Presentation + Paper

Large-scale Extended Granger Causality (lsXGC) for classification of Autism Spectrum Disorder from resting-state functional MRI

Axel Wismüller, John Foxe, Paul Geha, Seyed Saman Saboksayr

Proceedings Volume 11314, 113141Y (2020) https://doi.org/10.1117/12.2550027

KEYWORDS: Functional magnetic resonance imaging, Brain, Magnetic resonance imaging, Feature selection, Principal component analysis, Neuroscience, Dimension reduction

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It has been shown in the literature that Autism Spectrum Disorder (ASD) is associated with changes in brain network connectivity. Therefore, we investigate, if it is possible to capture any significant difference between brain connections of healthy subjects and ASD patients using resting-state fMRI time-series. To this end, we have developed large-scale Extended Granger Causality (lsXGC), which combines dimension reduction with source time-series augmentation and uses predictive time-series modeling for estimating directed causal relationships among resting-state fMRI time-series. This method is a multivariate approach, since it is capable of identifying the influence of each time-series on any other time-series in the presence of all other time-series of the underlying dynamic system. Here, we investigate whether this model can serve as a biomarker for classifying ASD patients from typical controls using a subset of 59 subjects of the Autism Brain Imaging Data Exchange II (ABIDE II) data repository. In this study, we use brain connections as features for classification and estimate them by lsXGC. As a reference method, we compare our results with cross-correlation, which is typically used in the literature as a standard measure of functional connectivity. After feature extraction, we perform feature selection by Kendall’s Tau rank correlation coefficient followed by classification using a Support Vector Machine (SVM). In order to evaluate the diagnostic accuracy of lsXGC, we compare its classification performance with cross-correlation. Within a cross-validation scheme of 100 different training/test data splits, we obtain a mean accuracy range of [0.7,0.81] and a mean Area Under the Receiver Operator Characteristic Curve (AUC) range of [0.78,0.85] across all tested numbers of features for lsXGC, which is significantly better than results obtained with cross-correlation namely mean accuracy of [0.57,0.61] and mean AUC of [0.54,0.59], which clearly demonstrates the applicability of lsXGC as a potential biomarker for ASD.

Proceedings Article | 28 February 2020 Presentation + Paper

Classification of attention-deficit/hyperactivity disorder from resting-state functional MRI with mutual connectivity analysis

Seyed Saman Saboksayr, Adora DSouza, John Foxe, Axel Wismüller

Proceedings Volume 11317, 1131703 (2020) https://doi.org/10.1117/12.2549997

KEYWORDS: Magnetic resonance imaging, Functional magnetic resonance imaging, Brain, Medical imaging, Computer aided diagnosis and therapy

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Proceedings Article | 28 February 2020 Paper

A prospective randomized clinical trial for measuring radiology study reporting time on Artificial Intelligence-based detection of intracranial hemorrhage in emergent care head CT

Axel Wismüller, Larry Stockmaster

Proceedings Volume 11317, 113170M (2020) https://doi.org/10.1117/12.2552400

KEYWORDS: Artificial intelligence, Radiology, Head, Computed tomography, Clinical trials, Information technology, Imaging systems, Image analysis, Telecommunications, Diagnostics

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The quantitative evaluation of Artificial Intelligence (AI) systems in a clinical context is a challenging endeavor, where the development and implementation of meaningful performance metrics is still in its infancy. Here, we propose a scientific concept, Artificial Intelligence Prospective Randomized Observer Blinding Evaluation (AI-PROBE) for quantitative clinical performance evaluation of radiology AI systems within prospective randomized clinical trials. Our evaluation workflow encompasses a study design and a corresponding radiology Information Technology (IT) infrastructure that randomly blinds radiologists with regards to the presence of positive reads as provided by AI-based image analysis systems. To demonstrate the applicability of our AI-evaluation framework, we present a first prospective randomized clinical trial on investigating the effect of automatic identification of Intra-Cranial Hemorrhage (ICH) in emergent care head CT scans on radiology study Turn-Around Time (TAT) in a clinical environment. Here, we acquired 620 consecutive non-contrast head CT scans from CT scanners used for inpatient and emergency room patients at a large academic hospital over a time period of 14 consecutive days. Immediately following image acquisition, scans were automatically analyzed for the presence of ICH using commercially available software (Aidoc, Tel Aviv, Israel). Cases identified as positive for ICH by AI (ICH-AI+) were automatically flagged in the radiologists' reading worklists, where flagging was randomly switched off with a probability of 50%. Study TAT was measured automatically as the time difference between study completion and first clinically communicated study reporting, with time stamps for these events automatically retrieved from various radiology IT systems. TATs for flagged cases (73 ± 143 min) were significantly lower than TATs for non-flagged (132 ± 193 min) cases (p<0.05, one-sided t-test), where 105 of the 122 ICH-AI+ cases were true positive reads. Total sensitivity, specificity, and accuracy over all analyzed cases were 95.0%, 96.7%, and 96.4%, respectively. We conclude that automatic identification of ICH reduces study TAT for ICH in emergent care head CT settings, which carries the potential for improving clinical management of ICH by accelerating clinically indicated therapeutic interventions. In a broader context, our results suggest that our AI-PROBE framework can contribute to a systematic quantitative evaluation of AI systems in a clinical workflow environment with regards to clinically meaningful performance measures, such as TAT or diagnostic accuracy metrics.

Proceedings Article | 15 March 2019 Presentation + Paper

Detecting connectivity changes in autism spectrum disorder using large-scale Granger causality

Anas Abidin, Adora Dsouza, Axel Wismüller

Proceedings Volume 10949, 109490M (2019) https://doi.org/10.1117/12.2513023

KEYWORDS: Time series analysis, Brain, Functional magnetic resonance imaging, Statistical analysis, Magnetic resonance imaging, Neuroimaging, Principal component analysis

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Proceedings Article | 15 March 2019 Presentation + Paper

Investigating a quantitative radiomics approach for brain tumor classification

Anas Zainul Abidin, Irfaan Dar, Adora D'Souza, Edward Lin, Axel Wismüller

Proceedings Volume 10953, 109530B (2019) https://doi.org/10.1117/12.2512995

KEYWORDS: Tumors, Brain, Magnetic resonance imaging, Machine learning, Feature extraction, Wavelets, Computer aided diagnosis and therapy

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Proceedings Article | 15 March 2019 Presentation + Paper

Classification of autism spectrum disorder from resting-state fMRI with mutual connectivity analysis

Adora DSouza, Anas Abidin, Axel Wismüller

Proceedings Volume 10953, 109531D (2019) https://doi.org/10.1117/12.2512983

KEYWORDS: Functional magnetic resonance imaging, Brain, Feature selection, Biological research, Machine learning, Radiology

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Proceedings Article | 13 March 2019 Paper

Automated identification of thoracic pathology from chest radiographs with enhanced training pipeline

Adora DSouza, Anas Abidin, Axel Wismüller

Proceedings Volume 10950, 109503F (2019) https://doi.org/10.1117/12.2512600

KEYWORDS: Pathology, Chest imaging, Network architectures, Radiology, Computer aided diagnosis and therapy, Emphysema, Machine learning

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Proceedings Article | 12 March 2018 Presentation + Paper

Investigating directed functional connectivity between the resting state networks of the human brain using mutual connectivity analysis

Anas Zainul Abidin, Adora D'Souza, Udaysankar Chockanathan, Giovanni Schifitto, Axel Wismüller

Proceedings Volume 10578, 105780A (2018) https://doi.org/10.1117/12.2293863

KEYWORDS: Brain, Functional magnetic resonance imaging, Statistical analysis, Neural networks, Magnetic resonance imaging, Neuroimaging

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Proceedings Article | 12 March 2018 Presentation + Paper

Resilient modular small-world directed brain networks in healthy subjects with large-scale Granger causality analysis of resting-state functional MRI

Udaysankar Chockanathan, Anas Abidin, Adora DSouza, Giovanni Schifitto, Axel Wismüller

Proceedings Volume 10578, 105780B (2018) https://doi.org/10.1117/12.2293877

KEYWORDS: Brain, Neuroimaging, Magnetic resonance imaging, Spatial resolution, Algorithm development, Biological research, Visualization, Sensors, Sensor networks, Functional magnetic resonance imaging

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Proceedings Article | 12 March 2018 Paper

Investigating large-scale Granger causality analysis in presence of noise and varying sampling rate

Adora DSouza, Anas Abidin, Udaysankar Chockanathan, Lutz Leistritz, Axel Wismüller

Proceedings Volume 10578, 105780C (2018) https://doi.org/10.1117/12.2293900

KEYWORDS: Functional magnetic resonance imaging, Radiology, Brain, Magnetic resonance imaging, Principal component analysis, Modeling

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Proceedings Article | 2 March 2018 Presentation + Paper

Regional autonomy changes in resting-state functional MRI in patients with HIV associated neurocognitive disorder

Adora DSouza, Anas Abidin, Udaysankar Chockanathan, Axel Wismüller

Proceedings Volume 10574, 105740Y (2018) https://doi.org/10.1117/12.2293757

KEYWORDS: Functional magnetic resonance imaging, Brain, Magnetic resonance imaging, Neurodegeneration, Neuroimaging, Radiology, Machine learning

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Proceedings Article | 2 March 2018 Presentation + Paper

MRI tumor segmentation with densely connected 3D CNN

Lele Chen, Yue Wu, Adora DSouza, Anas Abidin, Axel Wismüller, Chenliang Xu

Proceedings Volume 10574, 105741F (2018) https://doi.org/10.1117/12.2293394

KEYWORDS: Tumors, Magnetic resonance imaging, Image segmentation, 3D modeling, Convolution, Performance modeling, Brain, Neuroimaging, Feature extraction, 3D metrology

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Proceedings Article | 27 February 2018 Presentation + Paper

Identification and functional characterization of HIV-associated neurocognitive disorders with large-scale Granger causality analysis on resting-state functional MRI

Udaysankar Chockanathan, Adora DSouza, Anas Abidin, Giovanni Schifitto, Axel Wismüller

Proceedings Volume 10575, 105750Z (2018) https://doi.org/10.1117/12.2293888

KEYWORDS: Brain, Magnetic resonance imaging, Machine learning, Data modeling, Functional magnetic resonance imaging, Biological research, Autoregressive models, Dimension reduction, Radiology, Systems modeling

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Proceedings Article | 13 March 2017 Paper

Investigating changes in resting-state connectivity from functional MRI data in patients with HIV associated neurocognitive disorder using MCA and machine learning

Adora DSouza, Anas Abidin, Axel Wismüller

Proceedings Volume 10137, 101371C (2017) https://doi.org/10.1117/12.2254189

KEYWORDS: Functional magnetic resonance imaging, Brain, Magnetic resonance imaging, Machine learning, Quantization, Neurological disorders, Neuroimaging, Brain mapping, Matrices, Control systems, Data acquisition, Radiology, Diagnostics

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Proceedings Article | 13 March 2017 Presentation + Paper

Using large-scale Granger causality to study changes in brain network properties in the Clinically Isolated Syndrome (CIS) stage of multiple sclerosis

Anas Abidin, Udaysankar Chockanathan, Adora DSouza, Matilde Inglese, Axel Wismüller

Proceedings Volume 10137, 101371B (2017) https://doi.org/10.1117/12.2254395

KEYWORDS: Brain, Functional magnetic resonance imaging, Magnetic resonance imaging, Statistical analysis, Neuroimaging, Brain mapping, Radiology, Biomedical optics, Pathophysiology, Model-based design

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Proceedings Article | 3 March 2017 Presentation + Paper

Classification of micro-CT images using 3D characterization of bone canal patterns in human osteogenesis imperfecta

Anas Abidin, John Jameson, Robert Molthen, Axel Wismüller

Proceedings Volume 10134, 1013413 (2017) https://doi.org/10.1117/12.2254421

KEYWORDS: Binary data, Computer aided diagnosis and therapy, Biological research, Bone, 3D image processing, Image classification, Image resolution, Computed tomography, Tomography

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Proceedings Article | 3 March 2017 Presentation + Paper

Characterizing cartilage microarchitecture on phase-contrast x-ray computed tomography using deep learning with convolutional neural networks

Botao Deng, Anas Abidin, Adora D'Souza, Mahesh Nagarajan, Paola Coan, Axel Wismüller

Proceedings Volume 10134, 1013412 (2017) https://doi.org/10.1117/12.2254645

KEYWORDS: Neurons, Convolutional neural networks, Cartilage, Neural networks, X-ray computed tomography, Phase contrast, Tissues, Machine learning, Visualization, Medical imaging, Image classification, Feature extraction

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Proceedings Article | 24 February 2017 Paper

Identifying HIV associated neurocognitive disorder using large-scale Granger causality analysis on resting-state functional MRI

Adora DSouza, Anas Abidin, Lutz Leistritz, Axel Wismüller

Proceedings Volume 10133, 101330M (2017) https://doi.org/10.1117/12.2254690

KEYWORDS: Brain, Functional magnetic resonance imaging, Magnetic resonance imaging, Quantization, Machine learning, Nervous system, Data analysis, Matrices, Radiology, Neuroimaging

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Proceedings Article | 29 March 2016 Paper

Large-scale Granger causality analysis on resting-state functional MRI

Adora D'Souza, Anas Zainul Abidin, Lutz Leistritz, Axel Wismüller

Proceedings Volume 9788, 97880L (2016) https://doi.org/10.1117/12.2217264

KEYWORDS: Functional magnetic resonance imaging, Brain, Magnetic resonance imaging, Image segmentation, Time series analysis, Brain imaging, Brain mapping, Image processing, Visualization, Principal component analysis, Modeling, Radiology, Systems modeling, Biological research

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Proceedings Article | 29 March 2016 Paper

Investigating changes in brain network properties in HIV-associated neurocognitive disease (HAND) using mutual connectivity analysis (MCA)

Anas Zainul Abidin, Adora D'Souza, Mahesh Nagarajan, Axel Wismüller

Proceedings Volume 9788, 97881W (2016) https://doi.org/10.1117/12.2217317

KEYWORDS: Brain, Functional magnetic resonance imaging, Magnetic resonance imaging, Time series analysis, Neural networks, Brain imaging, Brain mapping, Radiology, Model-based design, Neurons, Biomedical optics, Control systems

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Proceedings Article | 29 March 2016 Paper

Detecting altered connectivity patterns in HIV associated neurocognitive impairment using mutual connectivity analysis

Anas Zainul Abidin, Adora D'Souza, Mahesh Nagarajan, Axel Wismüller

Proceedings Volume 9788, 97880N (2016) https://doi.org/10.1117/12.2217315

KEYWORDS: Functional magnetic resonance imaging, Brain, Statistical analysis, Time series analysis, Neuroimaging, Neurological disorders, Magnetic resonance imaging, Error analysis, Functional imaging, Data modeling, Radiology, Control systems, Biological research, Biomedical optics

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Proceedings Article | 29 March 2016 Paper

Mutual connectivity analysis (MCA) using generalized radial basis function neural networks for nonlinear functional connectivity network recovery in resting-state functional MRI

Adora D'Souza, Anas Zainul Abidin, Mahesh Nagarajan, Axel Wismüller

Proceedings Volume 9788, 97880K (2016) https://doi.org/10.1117/12.2216900

KEYWORDS: Functional magnetic resonance imaging, Neural networks, Magnetic resonance imaging, Detection and tracking algorithms, Brain, Complex systems, Brain imaging, Brain mapping, Image processing, Image segmentation, Data modeling, Biological research, Radiology, Independent component analysis, Statistical analysis

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Proceedings Article | 24 March 2016 Paper

Assessing vertebral fracture risk on volumetric quantitative computed tomography by geometric characterization of trabecular bone structure

Walter Checefsky, Anas Abidin, Mahesh Nagarajan, Jan Bauer, Thomas Baum, Axel Wismüller

Proceedings Volume 9785, 978508 (2016) https://doi.org/10.1117/12.2216898

KEYWORDS: Bone, X-ray computed tomography, Computed tomography, Minerals, Data acquisition, Calibration, Image processing, Machine learning, Image segmentation, Radiology

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Proceedings Article | 19 March 2015 Paper

Volumetric characterization of human patellar cartilage matrix on phase contrast x-ray computed tomography

Anas Abidin, Mahesh Nagarajan, Walter Checefsky, Paola Coan, Paul Diemoz, Susan Hobbs, Markus Huber, Axel Wismüller

Proceedings Volume 9417, 94171F (2015) https://doi.org/10.1117/12.2082084

KEYWORDS: Cartilage, Feature extraction, Phase contrast, X-ray computed tomography, Visualization, Tissues, Binary data, Radiology, Matrices, Statistical analysis

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Proceedings Article | 17 March 2015 Paper

Characterizing trabecular bone structure for assessing vertebral fracture risk on volumetric quantitative computed tomography

Mahesh Nagarajan, Walter Checefsky, Anas Abidin, Halley Tsai, Xixi Wang, Susan Hobbs, Jan Bauer, Thomas Baum, Axel Wismüller

Proceedings Volume 9417, 94171E (2015) https://doi.org/10.1117/12.2082059

KEYWORDS: Bone, Computed tomography, Calibration, Radiology, Minerals, Matrices, Neural networks, Failure analysis, Image segmentation, Scanners

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Proceedings Article | 17 March 2015 Paper

Functional connectivity analysis in resting state fMRI with echo-state networks and non-metric clustering for network structure recovery

Axel Wismüller, Adora DSouza, Anas Abidin, Xixi Wang, Susan Hobbs, Mahesh Nagarajan

Proceedings Volume 9417, 94171M (2015) https://doi.org/10.1117/12.2082106

KEYWORDS: Functional magnetic resonance imaging, Neurons, Brain, Magnetic resonance imaging, Visualization, Radiology, Neural networks, Linear filtering, Analytical research, Data acquisition

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Proceedings Article | 17 March 2015 Paper

Investigating the use of mutual information and non-metric clustering for functional connectivity analysis on resting-state functional MRI

Xixi Wang, Mahesh Nagarajan, Anas Abidin, Adora DSouza, Susan Hobbs, Axel Wismüller

Proceedings Volume 9417, 94171N (2015) https://doi.org/10.1117/12.2082565

KEYWORDS: Functional magnetic resonance imaging, Brain, Magnetic resonance imaging, Brain mapping, Associative arrays, Radiology, Signal attenuation, Dimension reduction, Biological research, Neuroimaging

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Proceedings Article | 17 March 2015 Paper

Nonlinear functional connectivity network recovery in the human brain with mutual connectivity analysis (MCA): convergent cross-mapping and non-metric clustering

Axel Wismüller, Anas Abidin, Adora D'Souza, Xixi Wang, Susan Hobbs, Lutz Leistritz, Mahesh Nagarajan

Proceedings Volume 9417, 94170M (2015) https://doi.org/10.1117/12.2082124

KEYWORDS: Functional magnetic resonance imaging, Brain, Shape memory alloys, Magnetic resonance imaging, Radiology, Data acquisition, Visualization, Neuroimaging, Biological research, Analytical research

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Proceedings Article | 9 April 2014 Paper

Using anisotropic 3D Minkowski functionals for trabecular bone characterization and biomechanical strength prediction in proximal femur specimens

Mahesh Nagarajan, Titas De, Eva-Maria Lochmüller, Felix Eckstein, Axel Wismüller

Proceedings Volume 9038, 903820 (2014) https://doi.org/10.1117/12.2044352

KEYWORDS: Bone, Anisotropy, Head, Biomedical optics, Binary data, Medical imaging, Radiology, 3D image processing, Biological research, Computed tomography

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Proceedings Article | 9 April 2014 Paper

Investigating the use of texture features for analysis of breast lesions on contrast-enhanced cone beam CT

Xixi Wang, Mahesh Nagarajan, David Conover, Ruola Ning, Avice O'Connell, Axel Wismueller

Proceedings Volume 9038, 903822 (2014) https://doi.org/10.1117/12.2042397

KEYWORDS: Breast, Tissues, Mammography, 3D image processing, Feature extraction, Computed tomography, Machine learning, Image classification, Image segmentation, Biological research

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Proceedings Article | 13 March 2014 Paper

Phase contrast imaging X-ray computed tomography: quantitative characterization of human patellar cartilage matrix with topological and geometrical features

Mahesh Nagarajan, Paola Coan, Markus Huber, Paul Diemoz, Axel Wismüller

Proceedings Volume 9038, 903811 (2014) https://doi.org/10.1117/12.2042395

KEYWORDS: Cartilage, Feature extraction, Machine learning, Phase contrast, X-ray computed tomography, Visualization, Tissues, Binary data, Biomedical optics, Radiology

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Proceedings Article | 13 March 2014 Paper

Method and device for intraoperative imaging of lumpectomy specimens to provide feedback to breast surgeon for prompt re-excision during the same procedure

Andrzej Krol, Susan Hemingway, Kara Kort, Gustavo de la Rosa, Ravi Adhikary, Deepa Masrani, David Feiglin, Avice O’Connell, Mahesh Nagarajan, Chien-Chun Yang, Axel Wismüller

Proceedings Volume 9038, 90381G (2014) https://doi.org/10.1117/12.2043919

KEYWORDS: Breast, Tissues, Surgery, Digital breast tomosynthesis, Optical spheres, Imaging devices, Foam, Stereoscopy, Mammography, Tumors

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Breast conserving therapy (BCT) of breast cancer is now widely accepted due to improved cosmetic outcome and improved patients’ quality of life. One of the critical issues in performing breast-conserving surgery is trying to achieve microscopically clear surgical margins while maintaining excellent cosmesis. Unfortunately, unacceptably close or positive surgical margins occur in at least 20-25% of all patients undergoing BCT requiring repeat surgical excision days or weeks later, as permanent histopathology routinely takes days to complete. Our aim is to develop a better method for intraoperative imaging of non-palpable breast malignancies excised by wire or needle localization. Providing non-deformed three dimensional imaging of the excised breast tissue should allow more accurate assessment of tumor margins and consequently allow further excision at the time of initial surgery thus limiting the enormous financial and emotional burden of additional surgery. We have designed and constructed a device that allows preservation of the excised breast tissue in its natural anatomic position relative to the breast as it is imaged to assess adequate excision. We performed initial tests with needle-guided lumpectomy specimens using micro-CT and digital breast tomosynthesis (DBT). Our device consists of a plastic sphere inside a cylindrical holder. The surgeon inserts a freshly excised piece of breast tissue into the sphere and matches its anatomic orientation with the fiducial markers on the sphere. A custom-shaped foam is placed inside the sphere to prevent specimen deformation due to gravity. DBT followed by micro-CT images of the specimen were obtained. We confirmed that our device preserved spatial orientation of the excised breast tissue and that the location error was lower than 10mm and 10 degrees. The initial obtained results indicate that breast lesions containing microcalcifications allow a good 3D imaging of margins providing immediate intraoperative feedback for further excision as needed at the initial operation.

Proceedings Article | 13 March 2014 Paper

Predicting the biomechanical strength of proximal femur specimens with Minkowski functionals and support vector regression

Chien-Chun Yang, Mahesh Nagarajan, Markus Huber, Julio Carballido-Gamio, Jan Bauer, Thomas Baum, Felix Eckstein, Eva-Maria Lochmüller, Thomas Link, Axel Wismüller

Proceedings Volume 9038, 903810 (2014) https://doi.org/10.1117/12.2041782

KEYWORDS: Bone, Head, Computed tomography, Machine learning, Feature extraction, Biomedical optics, Radiology, Biological research, Failure analysis, 3D image processing

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Proceedings Article | 13 March 2014 Paper

Pair-wise clustering of large scale Granger causality index matrices for revealing communities

Axel Wismüller, Mahesh Nagarajan, Herbert Witte, Britta Pester, Lutz Leistritz

Proceedings Volume 9038, 90381R (2014) https://doi.org/10.1117/12.2044340

KEYWORDS: Matrices, Autoregressive models, Principal component analysis, Magnetic resonance imaging, Data processing, Biological research, Biomedical optics, Distance measurement, Image processing, Associative arrays

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SPIE Journal Paper | 4 February 2014

Improving bone strength prediction in human proximal femur specimens through geometrical characterization of trabecular bone microarchitecture and support vector regression

Chien-Chun Yang, Mahesh Nagarajan, Markus Huber, Julio Carballido-Gamio, Jan Bauer, Thomas Baum, Felix Eckstein, Eva-Maria Lochmüller, Sharmila Majumdar, Thomas Link, Axel Wismüller

JEI, Vol. 23, Issue 01, 013013, (February 2014) https://doi.org/10.1117/12.10.1117/1.JEI.23.1.013013

KEYWORDS: Bone, Dual energy x-ray absorptiometry, Head, Feature extraction, Neck, Machine learning, Computed tomography, Tissues, Minerals, Performance modeling

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Proceedings Article | 29 March 2013 Paper

Introducing anisotropic Minkowski functionals and quantitative anisotropy measures for local structure analysis in biomedical imaging

Axel Wismüller, Titas De, Eva Lochmüller, Felix Eckstein, Mahesh Nagarajan

Proceedings Volume 8672, 86720I (2013) https://doi.org/10.1117/12.2007192

KEYWORDS: Anisotropy, Bone, Head, Neck, Biomedical optics, Medical imaging, Biological research, Image processing, Visualization, Feature extraction

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Proceedings Article | 29 March 2013 Paper

Characterizing healthy and osteoarthritic knee cartilage on phase contrast CT with geometric texture features

Mahesh Nagarajan, Paola Coan, Markus Huber, Paul Diemoz, Christian Glaser, Axel Wismüller

Proceedings Volume 8672, 86721J (2013) https://doi.org/10.1117/12.2006255

KEYWORDS: Cartilage, Statistical analysis, Feature extraction, Computed tomography, Machine learning, Phase contrast, Visualization, Fuzzy logic, Tissues, Biomedical optics

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Proceedings Article | 29 March 2013 Paper

Predicting the biomechanical strength of proximal femur specimens through high dimensional geometric features and support vector regression

Chien-Chun Yang, Mahesh Nagarajan, Markus Huber, Julio Carballido-Gamio, Jan Bauer, Thomas Baum, Felix Eckstein, Eva Lochmüller, Sharmila Majumdar, Thomas Link, Axel Wismüller

Proceedings Volume 8672, 86721K (2013) https://doi.org/10.1117/12.2006265

KEYWORDS: Bone, Biological research, Head, Biomedical optics, Failure analysis, Machine learning, Feature extraction, Radiology, Computed tomography, Minerals

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Proceedings Article | 16 April 2012 Paper

Characterization of healthy and osteoarthritic chondrocyte cell patterns on phase contrast CT images of the knee cartilage matrix

Mahesh Nagarajan, Paola Coan, Markus Huber, Chien-Chun Yang, Christian Glaser, Maximilian Reiser, Axel Wismüller

Proceedings Volume 8317, 831720 (2012) https://doi.org/10.1117/12.910919

KEYWORDS: Cartilage, Feature extraction, Computed tomography, Machine learning, Visualization, Fuzzy logic, Statistical analysis, Phase contrast, Tissues, Image classification

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Proceedings Article | 23 February 2012 Paper

Predicting the biomechanical strength of proximal femur specimens with bone mineral density features and support vector regression

Markus Huber, Chien-Chun Yang, Julio Carballido-Gamio, Jan Bauer, Thomas Baum, Mahesh Nagarajan, Felix Eckstein, Eva Lochmüller, Sharmila Majumdar, Thomas Link, Axel Wismüller

Proceedings Volume 8315, 83151W (2012) https://doi.org/10.1117/12.911402

KEYWORDS: Bone, Minerals, Head, Feature extraction, Computing systems, Computed tomography, Computer aided diagnosis and therapy, Failure analysis, Statistical analysis, Image segmentation

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To improve the clinical assessment of osteoporotic hip fracture risk, recent computer-aided diagnosis systems explore new approaches to estimate the local trabecular bone quality beyond bone density alone to predict femoral bone strength. In this context, statistical bone mineral density (BMD) features extracted from multi-detector computed tomography (MDCT) images of proximal femur specimens and different function approximations methods were compared in their ability to predict the biomechanical strength. MDCT scans were acquired in 146 proximal femur specimens harvested from human cadavers. The femurs' failure load (FL) was determined through biomechanical testing. An automated volume of interest (VOI)-fitting algorithm was used to define a consistent volume in the femoral head of each specimen. In these VOIs, the trabecular bone was represented by statistical moments of the BMD distribution and by pairwise spatial occurrence of BMD values using the gray-level co-occurrence (GLCM) approach. A linear multi-regression analysis (MultiReg) and a support vector regression algorithm with a linear kernel (SVRlin) were used to predict the FL from the image feature sets. The prediction performance was measured by the root mean square error (RMSE) for each image feature on independent test sets; in addition the coefficient of determination R² was calculated. The best prediction result was obtained with a GLCM feature set using SVRlin, which had the lowest prediction error (RSME = 1.040±0.143, R² = 0.544) and which was significantly lower that the standard approach of using BMD.mean and MultiReg (RSME = 1.093±0.133, R² = 0.490, p<0.0001). The combined sets including BMD.mean and GLCM features had a similar or slightly lower performance than using only GLCM features. The results indicate that the performance of high-dimensional BMD features extracted from MDCT images in predicting the biomechanical strength of proximal femur specimens can be significantly improved by using support vector regression.

Proceedings Article | 17 March 2011 Paper

Modified total variation norm for the maximum a posteriori ordered subsets expectation maximization reconstruction in fan-beam SPECT brain perfusion imaging

Andrzej Krol, Zhaoxia Yang, Yuesheng Xu, Axel Wismüller, David Feiglin

Proceedings Volume 7961, 796140 (2011) https://doi.org/10.1117/12.877802

KEYWORDS: Single photon emission computed tomography, Neuroimaging, Brain, Optical spheres, Collimators, Tomography, Spatial resolution, Cerebral blood flow, Single photon, 3D modeling

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Proceedings Article | 9 March 2011 Paper

Quantitative analysis of tumor matrix patterns through statistical and topological texture features

Mahesh Nagarajan, Xiaoxing Han, Markus Huber, Thomas Foster, Edward Brown, Axel Wismüller

Proceedings Volume 7965, 79651E (2011) https://doi.org/10.1117/12.878619

KEYWORDS: Tumors, Second-harmonic generation, Collagen, Feature extraction, Machine learning, Image classification, Fuzzy logic, Statistical analysis, Quantitative analysis, Harmonic generation

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Proceedings Article | 9 March 2011 Paper

Estimating local scaling properties for the classification of interstitial lung disease patterns

Markus Huber, Mahesh Nagarajan, Gerda Leinsinger, Lawrence Ray, Axel Wismueller

Proceedings Volume 7963, 79631Q (2011) https://doi.org/10.1117/12.878253

KEYWORDS: Lung, Image classification, Tissues, Computed tomography, CT reconstruction, Chest, Machine learning, Computer aided diagnosis and therapy, Seaborgium, Matrices

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Proceedings Article | 9 March 2011 Paper

Lesion classification on breast MRI through topological characterization of morphology over time

Mahesh Nagarajan, Markus Huber, Thomas Schlossbauer, Lawrence Ray, Andrzej Krol, Axel Wismüller

Proceedings Volume 7963, 79631U (2011) https://doi.org/10.1117/12.877742

KEYWORDS: Image classification, Magnetic resonance imaging, Breast, Feature extraction, Fuzzy logic, Statistical analysis, Binary data, Matrices, Data acquisition, Radiology

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Proceedings Article | 5 March 2011 Paper

Texture feature selection with relevance learning to classify interstitial lung disease patterns

Markus Huber, Kerstin Bunte, Mahesh Nagarajan, Michael Biehl, Lawrence Ray, Axel Wismueller

Proceedings Volume 7963, 796318 (2011) https://doi.org/10.1117/12.877894

KEYWORDS: Lung, Image classification, Prototyping, Distance measurement, Feature selection, Computed tomography, Stochastic processes, Quantization, Feature extraction, Matrices

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Proceedings Article | 9 March 2010 Paper

Prediction of biomechanical trabecular bone properties with geometric features using MR imaging

Markus Huber, Sarah Lancianese, Imoh Ikpot, Mahesh Nagarajan, Amy Lerner, Axel Wismüller

Proceedings Volume 7626, 76261O (2010) https://doi.org/10.1117/12.845022

KEYWORDS: Bone, Magnetic resonance imaging, In vivo imaging, Machine learning, Feature extraction, Tissues, Image analysis, Binary data, Magnetism, Neural networks

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Proceedings Article | 9 March 2010 Paper

Classification of interstitial lung disease patterns with topological texture features

Markus Huber, Mahesh Nagarajan, Gerda Leinsinger, Lawrence Ray, Axel Wismüller

Proceedings Volume 7624, 762410 (2010) https://doi.org/10.1117/12.844318

KEYWORDS: Lung, Image classification, Tissues, Computed tomography, Chest, Machine learning, Computer aided diagnosis and therapy, Statistical analysis, CT reconstruction, Matrices

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Proceedings Article | 9 March 2010 Paper

Analysis of breast lesions on contrast-enhanced magnetic resonance images using high-dimensional texture features

Mahesh Nagarajan, Markus Huber, Thomas Schlossbauer, Gerda Leinsinger, Axel Wismueller

Proceedings Volume 7624, 76241G (2010) https://doi.org/10.1117/12.844552

KEYWORDS: Statistical analysis, Breast, Magnetic resonance imaging, Fuzzy logic, Image classification, Feature extraction, Matrices, Error analysis, Magnetism, Yield improvement

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Proceedings Article | 9 March 2010 Paper

Model-free functional MRI analysis for detecting low-frequency functional connectivity in the human brain

Axel Wismueller, Oliver Lange, Dorothee Auer, Gerda Leinsinger

Proceedings Volume 7624, 76241M (2010) https://doi.org/10.1117/12.843014

KEYWORDS: Functional magnetic resonance imaging, Distance measurement, Brain, Magnetic resonance imaging, Evolutionary algorithms, Image segmentation, Data analysis, Neuroimaging, Brain mapping, Fuzzy logic

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Proceedings Article | 30 April 2009 Paper

Application and evaluation of a motion compensation technique to breast MRI

Frank Steinbrücker, Anke Meyer-Bäse, Axel Wismüller, Thomas Schlossbauer

Proceedings Volume 7347, 73470J (2009) https://doi.org/10.1117/12.818559

KEYWORDS: Magnetic resonance imaging, Breast, Tumors, Tissues, Visualization, Statistical analysis, Feature extraction, Image classification, Breast cancer, Optical flow

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Proceedings Article | 28 February 2009 Paper

The exploration machine: a novel method for analyzing high-dimensional data in computer-aided diagnosis

Axel Wismüller

Proceedings Volume 7260, 72600G (2009) https://doi.org/10.1117/12.813892

KEYWORDS: Visualization, Magnetic resonance imaging, Brain mapping, Computer aided diagnosis and therapy, Data modeling, Associative arrays, Biological research, Biomedical optics, Machine learning, Principal component analysis

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Proceedings Article | 27 February 2009 Paper

A computational framework for exploratory data analysis in biomedical imaging

Axel Wismueller

Proceedings Volume 7262, 726206 (2009) https://doi.org/10.1117/12.813894

KEYWORDS: Biomedical optics, Visualization, Magnetic resonance imaging, Brain mapping, Data analysis, Data modeling, Biological research, Associative arrays, Machine learning, Neuroimaging

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Proceedings Article | 23 April 2008 Paper

Dependent component analysis applied to lesions' characterization in breast MRI

Anke Meyer-Bäse, Oliver Lange, Thomas Schlossbauer, Axel Wismueller

Proceedings Volume 6979, 69790D (2008) https://doi.org/10.1117/12.777288

KEYWORDS: Magnetic resonance imaging, Breast, Image segmentation, Independent component analysis, Wavelets, Sensors, Neural networks, Feature extraction, Tissues, Diagnostics

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Proceedings Article | 15 April 2008 Paper

Intelligent computer-aided diagnosis system for breast MRI combining kinetic and morphological aspects

Axel Wismüller, Anke Meyer-Bäse, Oliver Lange

Proceedings Volume 6961, 69610K (2008) https://doi.org/10.1117/12.777241

KEYWORDS: Intelligence systems, Breast, Magnetic resonance imaging, Computing systems, Computer aided diagnosis and therapy, Imaging systems, Detection theory, Information operations, Neural networks, Classification systems

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Proceedings Article | 12 March 2008 Paper

Computer-aided segmentation and 3D analysis of in vivo MRI examinations of the human vocal tract during phonation

Axel Wismüller, Johannes Behrends, Phil Hoole, Gerda Leinsinger, Anke Meyer-Baese, Maximilian Reiser

Proceedings Volume 6916, 69160T (2008) https://doi.org/10.1117/12.770836

KEYWORDS: Magnetic resonance imaging, 3D modeling, Image segmentation, In vivo imaging, Data acquisition, Data modeling, 3D image processing, Mouth, Modeling, 3D acquisition

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Proceedings Article | 1 May 2007 Paper

Small mammographic lesions evaluation based on neural gas network

Oliver Lange, Anke Meyer-Baese, Axel Wismueller

Proceedings Volume 6560, 65600V (2007) https://doi.org/10.1117/12.719314

KEYWORDS: Magnetic resonance imaging, Breast, Image segmentation, Neural networks, Mammography, Computer aided diagnosis and therapy, Diagnostics, Visualization, Silicon carbide, Tissues

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Proceedings Article | 30 March 2007 Paper

Neural network vector quantization improves the diagnostic quality of computer-aided diagnosis in dynamic breast MRI

Axel Wismüller, Anke Meyer-Baese, Gerda Leinsinger, Oliver Lange, Thomas Schlossbauer, Maximilian Reiser

Proceedings Volume 6514, 65141F (2007) https://doi.org/10.1117/12.708819

KEYWORDS: Quantization, Breast, Magnetic resonance imaging, Diagnostics, Neural networks, Mammography, Computer aided diagnosis and therapy, Data analysis, Pattern recognition, Breast cancer

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Proceedings Article | 17 April 2006 Paper

Performance evaluation based on cluster validity indices in medical imaging

Oliver Lange, Anke Meyer-Bäse, Axel Wismüller

Proceedings Volume 6247, 624714 (2006) https://doi.org/10.1117/12.660670

KEYWORDS: Functional magnetic resonance imaging, Medical imaging, Brain, Visualization, Fuzzy logic, Data analysis, Independent component analysis, Data modeling, Model-based design, Detection and tracking algorithms

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Proceedings Article | 17 April 2006 Paper

Detecting low-frequency functional connectivity in fMRI using unsupervised clustering algorithms

Oliver Lange, Anke Meyer-Bäse, Axel Wismüller

Proceedings Volume 6247, 62470M (2006) https://doi.org/10.1117/12.660669

KEYWORDS: Functional magnetic resonance imaging, Matrices, Fuzzy logic, Data analysis, Distance measurement, Data modeling, Error analysis, Brain, Independent component analysis, Neural networks

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SPIE Journal Paper | 1 January 2006

Segmentation and classification of dynamic breast magnetic resonance image data

Axel Wismüller, Anke Meyer-Bäse, Oliver Lange, Thomas Schlossbauer, Maria Kallergi, Maximilian Reiser, Gerda Leinsinger

JEI, Vol. 15, Issue 01, 013020, (January 2006) https://doi.org/10.1117/12.10.1117/1.2178776

KEYWORDS: Image segmentation, Magnetic resonance imaging, Breast, Magnetism, Image classification, Neural networks, Annealing, Quantization, Image visualization, Tissues

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Proceedings Article | 28 March 2005 Paper

Analysis of dynamic cerebral contrast-enhanced perfusion MRI time-series based on unsupervised clustering methods

Oliver Lange, Anke Meyer-Baese, Axel Wismuller, Monica Hurdal

Proceedings Volume 5818, (2005) https://doi.org/10.1117/12.601005

KEYWORDS: Magnetic resonance imaging, Annealing, Fuzzy logic, Neural networks, Brain mapping, Brain, Data analysis, Cerebrovascular diseases, Quantitative analysis, Basal ganglia

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Proceedings Article | 28 March 2005 Paper

Exploratory data analysis methods applied to fMRI

Oliver Lange, Anke Meyer-Base, Uwe Meyer-Base, Axel Wismuller, Monica Hurdal

Proceedings Volume 5803, (2005) https://doi.org/10.1117/12.601004

KEYWORDS: Independent component analysis, Functional magnetic resonance imaging, Brain, Data analysis, Neural networks, Principal component analysis, Annealing, Brain mapping, Visualization, Fuzzy logic

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Proceedings Article | 28 March 2005 Paper

Computer-aided diagnosis in breast MRI based on ICA and unsupervised clustering techniques

Anke Meyer-Baese, Oliver Lange, Axel Wismuller, Gerda Leinsinger

Proceedings Volume 5818, (2005) https://doi.org/10.1117/12.601007

KEYWORDS: Magnetic resonance imaging, Breast, Independent component analysis, Image segmentation, Computer aided diagnosis and therapy, Mammography, Tissues, Annealing, Fuzzy logic, Silicon carbide

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SPIE Journal Paper | 1 May 2004

Fast K-dimensional tree-structured vector quantization encoding method for image compression

Anke Meyer-Bäse, Karsten Jancke, Axel Wismüller, Michael Georgiopoulos

OE, Vol. 43, Issue 05, (May 2004) https://doi.org/10.1117/12.10.1117/1.1683885

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Proceedings Article | 12 April 2004 Paper

Data partitioning and independent component analysis techniques applied to fMRI

Axel Wismueller, Anke Meyer-Base, Oliver Lange, Thomas Otto, Dorothee Auer

Proceedings Volume 5439, (2004) https://doi.org/10.1117/12.542219

KEYWORDS: Independent component analysis, Functional magnetic resonance imaging, Neural networks, Annealing

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Proceedings Article | 12 April 2004 Paper

Model-free functional MRI analysis using improved fuzzy cluster analysis techniques

Oliver Lange, Anke Meyer-Baese, Axel Wismueller, Monica Hurdal, DeWitt Sumners, Dorothee Auer

Proceedings Volume 5421, (2004) https://doi.org/10.1117/12.541778

KEYWORDS: Fuzzy logic, Magnetic resonance imaging, Functional magnetic resonance imaging, Data modeling, Statistical analysis, Evolutionary algorithms, Model-based design, Statistical modeling, Brain imaging, Brain mapping

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Proceedings Article | 12 April 2004 Paper

Tree-dependent and topographic independent component analysis for fMRI analysis

Oliver Lange, Anke Meyer-Base, Axel Wismueller, Monica Hurdal, DeWitt Sumners, Dorothee Auer

Proceedings Volume 5439, (2004) https://doi.org/10.1117/12.541779

KEYWORDS: Independent component analysis, Functional magnetic resonance imaging, Data analysis

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Proceedings Article | 12 April 2004 Paper

Computer-aided diagnosis in breast MRI based on unsupervised clustering techniques

Anke Meyer-Baese, Axel Wismueller, Oliver Lange, Gerda Leinsinger

Proceedings Volume 5421, (2004) https://doi.org/10.1117/12.542249

KEYWORDS: Magnetic resonance imaging, Computer aided diagnosis and therapy, Breast, Data analysis, Mammography, Computing systems, Biomedical optics, Fuzzy logic, Annealing, Neural networks

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Proceedings Article | 4 August 2003 Paper

Model-free functional MRI analysis using cluster-based methods

Thomas Otto, Anke Meyer-Baese, Monica Hurdal, DeWitt Sumners, Dorothee Auer, Axel Wismuller

Proceedings Volume 5103, (2003) https://doi.org/10.1117/12.487254

KEYWORDS: Neural networks, Functional magnetic resonance imaging, Quantization, Data modeling, Visualization, Magnetic resonance imaging, Brain mapping, Model-based design, Brain, Statistical analysis

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Proceedings Article | 1 April 2003 Paper

Model-free functional MRI analysis using transformation-based methods

Thomas Otto, Anke Meyer-Baese, Monica Hurdal, DeWitt Sunmers, Axel Wismuller, Dorothee Auer

Proceedings Volume 5102, (2003) https://doi.org/10.1117/12.487368

KEYWORDS: Independent component analysis, Functional magnetic resonance imaging, Principal component analysis, Data modeling, Brain, Neural networks, Brain mapping, Visualization, Model-based design, Neurons

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Showing 5 of 99 publications

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