KEYWORDS: Image segmentation, Brain, Neuroimaging, Magnetic resonance imaging, 3D modeling, 3D image processing, Medical imaging, Diagnostics, 3D acquisition, Magnetism
Magnetic Resonance (MR) brain scanning is often planned manually with the goal of aligning the imaging plane with
key anatomic landmarks. The planning is time-consuming and subject to inter- and intra- operator variability. An
automatic and standardized planning of brain scans is highly useful for clinical applications, and for maximum utility
should work on patients of all ages. In this study, we propose a method for fully automatic planning that utilizes the
landmarks from two orthogonal images to define the geometry of the third scanning plane. The corpus callosum (CC) is
segmented in sagittal images by an active shape model (ASM), and the result is further improved by weighting the
boundary movement with confidence scores and incorporating region based refinement. Based on the extracted contour
of the CC, several important landmarks are located and then combined with landmarks from the coronal or transverse
plane to define the geometry of the third plane. Our automatic method is tested on 54 MR images from 24 patients and 3
healthy volunteers, with ages ranging from 4 months to 70 years old. The average accuracy with respect to two
manually labeled points on the CC is 3.54 mm and 4.19 mm, and differed by an average of 2.48 degrees from the
orientation of the line connecting them, demonstrating that our method is sufficiently accurate for clinical use.
Translational, rotational, and scaling invariant (TRSI) pattern recognition is a third-order problem encountered frequently in real-world applications. But neither traditional image processing/pattern recognition algorithms nor artificial neural networks have yet provided satisfactory solutions for this problem after years of study. Recent research has shown that a higher-order neural network (HONN), of order three with built-in invariances, can effectively achieve TRSI pattern recognition. For an N X N image, the memory needed to store the connections is proportional to N6. This huge memory requirement limits the HONNs application to large-scale images. To solve this problem the authors first adapt edge detection and log-spiral mapping algorithms to preprocess the image so that the problem is converted into a second-order one. This reduces the HONN memory requirement to O(N4). Second, the authors modified the second-order HONN architecture to further reduce the memory size to O(N2). Synthetic and real images with resolution 256 X 256 have been used for simulation. The training samples are noise free, and the testing samples are rotated, translated, scaled, or noise-corrupted versions of the training patterns. Simulation results show that this system can indeed achieve TRSI pattern classification. In addition, its high robustness to noise and pattern deformation makes it very useful for real-world applications.
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