Simulating the Grassfire Transform Using an Active Contour Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Finite-Element Methods for Active Contour Models and Balloons for 2-D and 3-D Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Segmentation of Single and Multiple Neoplastic Hepatic Lesions in CT Images
IWINAC '07 Proceedings of the 2nd international work-conference on Nature Inspired Problem-Solving Methods in Knowledge Engineering: Interplay Between Natural and Artificial Computation, Part II
Automatic segmentation of the liver in CT images using a model of approximate contour
ISCIS'06 Proceedings of the 21st international conference on Computer and Information Sciences
Carotid ultrasound segmentation using DP active contours
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
Support vector machine approach to cardiac SPECT diagnosis
IWCIA'11 Proceedings of the 14th international conference on Combinatorial image analysis
SVM approach to classifying lesions in USG images with the use of the gabor decomposition
IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning
AdaBoost-based approach for detecting lithiasis and polyps in USG images of the gallbladder
IVIC'11 Proceedings of the Second international conference on Visual informatics: sustaining research and innovations - Volume Part I
Gallbladder shape extraction from ultrasound images using active contour models
Computers in Biology and Medicine
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Extracting the shape of the gallbladder from an ultrasonography (US) image allows superfluous information which is immaterial in the diagnostic process to be eliminated. In this project an active contour model was used to extract the shape of the gallbladder, both for cases free of lesions, and for those showing specific disease units, namely: lithiasis, polyps and changes in the shape of the organ, such as folds or turns of the gallbladder. The approximate shape of the gallbladder was found by applying the motion equation model. The tests conducted have shown that for the 220 US images of the gallbladder, the area error rate (AER) amounted to 18.15%.