With its high sensitivity, dynamic contrast-enhanced MR imaging (DCE-MRI) of the breast is today one of the first-line
tools for early detection and diagnosis of breast cancer, particularly in the dense breast of young women. However, many
relevant findings are very small or occult on targeted ultrasound images or mammography, so that MRI guided biopsy is
the only option for a precise histological work-up [1]. State-of-the-art software tools for computer-aided diagnosis of
breast cancer in DCE-MRI data offer also means for image-based planning of biopsy interventions. One step in the MRI
guided biopsy workflow is the alignment of the patient position with the preoperative MR images. In these images, the
location and orientation of the coil localization unit can be inferred from a number of fiducial markers, which for this
purpose have to be manually or semi-automatically detected by the user.
In this study, we propose a method for precise, full-automatic localization of fiducial markers, on which basis a virtual
localization unit can be subsequently placed in the image volume for the purpose of determining the parameters for
needle navigation. The method is based on adaptive thresholding for separating breast tissue from background followed
by rigid registration of marker templates. In an evaluation of 25 clinical cases comprising 4 different commercial coil
array models and 3 different MR imaging protocols, the method yielded a sensitivity of 0.96 at a false positive rate of
0.44 markers per case. The mean distance deviation between detected fiducial centers and ground truth information that
was appointed from a radiologist was 0.94mm.
Breast cancer diagnosis based on magnetic resonance images (breast MRI) is increasingly being accepted as an
additional diagnostic tool to mammography and ultrasound, with distinct clinical indications.1 Its capability
to detect and differentiate lesion types with high sensitivity and specificity is countered by the fact that visual
human assessment of breast MRI requires long experience. Moreover, the lack of evaluation standards causes
diagnostic results to vary even among experts. The most important MR acquisition technique is dynamic contrast
enhanced (DCE) MR imaging since different lesion types accumulate contrast material (CM) differently. The
wash-in and wash-out characteristic as well as the morphologic characteristic recorded and assessed from MR
images therefore allows to differentiate benign from malignant lesions. In this work, we propose to calculate
second order statistical features (Haralick textures) for given lesions based on subtraction and 4D images and
on parametermaps. The lesions are classified with a linear classification scheme into probably malignant or
probably benign. The method and model was developed on 104 histologically graded lesions (69 malignant and
35 benign). The area under the ROC curve obtained is 0.91 and is already comparable to the performance of a
trained radiologist.
The kinetic characteristics of tissue in dynamic contrast-enhanced magnetic resonance imaging data are an important
source of information for the differentiation of benign and malignant lesions. Kinetic curves measured for each lesion
voxel allow to infer information about the state of the local tissue. As a whole, they reflect the heterogeneity of the vascular
structure within a lesion, an important criterion for the preoperative classification of lesions. Current clinical practice in
analysis of tissue kinetics however is mainly based on the evaluation of the "most-suspect curve", which is only related
to a small, manually or semi-automatically selected region-of-interest within a lesion and does not reflect any information
about tissue heterogeneity.
We propose a new method which exploits the full range of kinetic information for the automatic classification of
lesions. Instead of breaking down the large amount of kinetic information to a single curve, each lesion is considered as a
probability distribution in a space of kinetic features, efficiently represented by its kinetic signature obtained by adaptive
vector quantization of the corresponding kinetic curves. Dissimilarity of two signatures can be objectively measured using
the Mallows distance, which is a metric defined on probability distributions. The embedding of this metric in a suitable
kernel function enables us to employ modern kernel-based machine learning techniques for the classification of signatures.
In a study considering 81 breast lesions, the proposed method yielded an Az value of 0.89±0.01 for the discrimination of
benign and malignant lesions in a nested leave-one-lesion-out evaluation setting.
Intelligent medical systems based on supervised and unsupervised
artificial neural networks are applied to the automatic visualization and classification of suspicious lesions in breast MRI. These systems
represent an important component of future sophisticated
computer-aided diagnosis systems and enable the extraction of spatial and temporal features of dynamic MRI data stemming from patients with confirmed lesion diagnosis. By taking into account the heterogenity of the cancerous tissue, these techniques reveal the malignant, benign and normal kinetic signals and and provide a regional subclassification of pathological breast tissue. Intelligent medical systems are expected to have substantial implications in healthcare politics by contributing to the diagnosis of indeterminate breast lesions by non-invasive imaging.
The exploration and categorization of large and unannotated image collections is a challenging task in the field of image retrieval as well as in the generation of appearance based object representations. In this context the Self-Organizing Map (SOM) has shown to be an efficient and scalable tool for the analysis of image collections based on low level features. Next to commonly employed visualization methods, clustering techniques have been recently considered for the aggregation of SOM nodes into groups in order to facilitate category specific data exploration. In this paper, spectral clustering based on graph theoretic concepts is employed for SOM based data categorization. The results are compared with those from the Neural Gas algorithm and hierarchical agglomerative clustering. Using SOMs trained on an eigenspace representation of the Columbia Object Image Library 20 (COIL20), the correspondence of the cluster data to a semantic reference grouping is calculated. Based on the Adjusted Rand Index it is shown that independent from the number of selected clusters, spectral clustering achieves a significantly higher correspondence to the reference grouping than any of the other methods.
KEYWORDS: Tumors, Magnetic resonance imaging, Breast, 3D image processing, 3D acquisition, Breast cancer, Quantization, Cancer, Data acquisition, Volume rendering
In this paper we apply multiscale entropy (MSE) analysis to data
obtained from magnetic resonance imaging of the female breast.
All cases include lesions that were histologically proven as
malignant tumors. Our results indicate that multiscale entropy
analysis can play an important role in the detection of tumor
tissue when applied to single datasets, but does not allow to
calculate universal morphological features. The performance of
MSE was examined with respect to traditional features such
as difference imaging.
Due to the rapid progress in medical imaging technology, analysis of multivariate image data is receiving increased interest. However, their visual exploration is a challenging task since it requires the integration of information from many different sources which usually cannot be perceived at once by an observer.
Image fusion techniques are commonly used to obtain information from multivariate image data, while psychophysical aspects of data visualization are usually not considered. Visualization is typically achieved by means of device derived color scales. With respect to psychophysical aspects of visualization, more sophisticated color mapping techniques based on device independent (and perceptually uniform) color spaces like CIELUV have been proposed. Nevertheless, the benefit of these techniques is limited by the fact that they require complex color space transformations to account for device characteristics and viewing conditions.
In this paper we present a new framework for the visualization of multivariate image data using image fusion and color mapping techniques. In order to overcome problems of consistent image presentations and color space transformations, we propose perceptually optimized color scales based on CIELUV in combination with sRGB (IEC 61966-2-1) color specification. In contrast to color definitions based purely on CIELUV, sRGB data can be used directly under reasonable conditions, without complex transformations and additional information. In the experimental section we demonstrate the advantages of our approach in an application of these techniques to the visualization of DCE-MRI images from breast cancer research.
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