Handbook of Computer Vision Algorithms in Image Algebra
Handbook of Computer Vision Algorithms in Image Algebra
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
Automatic hepatic tumor segmentation using composite hypotheses
ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition
Automatic hepatic tumor segmentation using statistical optimal threshold
ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part I
Automatic boundary tumor segmentation of a liver
ICCSA'05 Proceedings of the 2005 international conference on Computational Science and Its Applications - Volume Part IV
Gallbladder boundary segmentation from ultrasound images using active contour model
IDEAL'10 Proceedings of the 11th international conference on Intelligent data engineering and automated learning
Gallbladder segmentation in 2-D ultrasound images using deformable contour methods
MDAI'10 Proceedings of the 7th international conference on Modeling decisions for artificial intelligence
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This paper describes an automatic method for segmenting single and multiple neoplastic hepatic lesions in computed-tomography (CT) images. The structure of the liver is first segmented using the approximate contour model. Then, the appropriate histogram transformations are performed to enhance neoplastic focal lesions in CT images. To segment neoplastic lesions, images are processed using binary morphological filtration operators with the application of a parameterized mean defining the distribution of gray-levels of pixels in the image. Then, the edges of neoplastic lesions situated inside the liver contour are localized. To assess the suitability of the suggested method, experiments have been carried out for two types of tumors: hemangiomas and hepatomas. The experiments were conducted on 60 cases of various patients. Thirty CT images showed single and multiple focal hepatic neoplastic lesions, and the remaining 30 images contained no disease symptoms. Experimental results confirmed that the method is a useful tool supporting image diagnosis of the normal and abnormal liver. The proposed algorithm is 78.3% accurate.