Simulating the Grassfire Transform Using an Active Contour Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Handbook of Computer Vision Algorithms in Image Algebra
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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
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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
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IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning
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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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Segmenting the gallbladder from an ultrasonography (US) image allows background elements which are immaterial in the diagnostic process to be eliminated. In this project, several active contour models were used to extract the shape of the gallbladder, both for cases free of lesions, and for those showing specific disease units, namely: lithiasis, polyps, anatomical changes, such as folds or turns of the gallbladder. First, the histogram normalization transformation was executed allowing the contrast of US images to be improved. The approximate edge of the gallbladder was found by applying one of the active contour models like the motion equation, a center-point model or a balloon model. An operation of adding up areas delimited by the determined contours was also executed to more exactly approximate the shape of the gallbladder in US images. Then, the fragment of the image located outside the gallbladder contour was eliminated from the image. The tests conducted have shown that for the 220 US images of the gallbladder, the area error rate (AER) amounted to 16.4%.