Improving the classification of multiple disorders with problem decomposition
Journal of Biomedical Informatics
Kinematic patellar tracking from MR images for knee pain analysis
Computers in Biology and Medicine
Patient oriented and robust automatic liver segmentation for pre-evaluation of liver transplantation
Computers in Biology and Medicine
Liver Segmentation from CT Scans: A Survey
WILF '07 Proceedings of the 7th international workshop on Fuzzy Logic and Applications: Applications of Fuzzy Sets Theory
Liver segmentation from computed tomography scans: A survey and a new algorithm
Artificial Intelligence in Medicine
Applied Soft Computing
Knee MR image segmentation combining contextual constrained neural network and level set evolution
CIBCB'09 Proceedings of the 6th Annual IEEE conference on Computational Intelligence in Bioinformatics and Computational Biology
Engineering Applications of Artificial Intelligence
3D α expansion and graph cut algorithms for automatic liver segmentation from CT images
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part I
A segmentation framework for abdominal organs from CT scans
Artificial Intelligence in Medicine
Genetic regulatory network-based symbiotic evolution
Expert Systems with Applications: An International Journal
Fully automatic kidneys detection in 2d CT images: a statistical approach
MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Survey on liver CT image segmentation methods
Artificial Intelligence Review
Analyses of missing organs in abdominal multi-organ segmentation
MICCAI'11 Proceedings of the Third international conference on Abdominal Imaging: computational and Clinical Applications
3D reconstruction from CT-scan volume dataset application to kidney modeling
Proceedings of the 27th Spring Conference on Computer Graphics
Segmentation of abdominal organs from CT using a multi-level, hierarchical neural network strategy
Computer Methods and Programs in Biomedicine
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Identifying abdominal organs is one of the essential steps in visualizing organ structure to assist in teaching, clinical training, diagnosis, and medical image retrieval. However, due to partial volume effects, gray-level similarities of adjacent organs, contrast media affect, and the relatively high variations of organ position and shape, automatically identifying abdominal organs has always been a high challenging task. To conquer these difficulties, this paper proposes combining a multimodule contextual neural network and spatial fuzzy rules and fuzzy descriptors for automatically identifying abdominal organs from a series of CT image slices. The multimodule contextual neural network segments each image slice through a divide-and-conquer concept, embedded within multiple neural network modules, where the results obtained from each module are forwarded to other modules for integration, in which contextual constraints are enforced. With this approach, the difficulties arising from partial volume effects, gray-level similarities of adjacent organs, and contrast media affect can be reduced to the extreme. To address the issue of high variations in organ position and shape, spatial fuzzy rules and fuzzy descriptors are adopted, along with a contour modification scheme implementing consecutive organ region overlap constraints. This approach has been tested on 40 sets of abdominal CT images, where each set consists of about 40 image slices. We have found that 99% of the organ regions in the test images are correctly identified as its belonging organs, implying the high promise of the proposed method.