Handbook of Computer Vision Algorithms in Image Algebra
Handbook of Computer Vision Algorithms in Image Algebra
Pattern Recognition in Medical Imaging
Pattern Recognition in Medical Imaging
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
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In this paper will be described a new method of automatic segmentation of inflammation and neoplastic hepatic disease symptoms, visible in computed-tomography (CT) images. The liver structure will be at first extracted from the image using the ap proximate contour model. Then, the appropriate histogram-based transformations will be proposed to enhance neoplastic focal changes in CT images. For segmentation stage of cancerous symptoms, the analyzed images will be processed using binary morphological filtration with the application of a parameterized mean defining the distribution of pixel gray-levels in the image. Then, the edges of neoplastic lesions situated inside the liver contour are localized. To assess the efficiency of the proposed processing procedures, experiments have been carried out for two types of tumours: haemangiomas and hepatomas. The experiments were conducted on 60 cases of various patients. In this set 30 images showed single and multiple focal hepatic neoplastic lesions, and the remaining 30 images show the healthy organ. Experimental results confirmed that the proposed method is an efficient tool which may be used in the diagnostic support procedures for normal and abnormal liver. The efficiency of proposed algorithm reach the level of over 83% of correct recognition of pathological changes.