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|標題:||Effective segmentation and classification for HCC biopsy images||作者:||Huang, P.W.
|關鍵字:||HCC biopsy image;Morphological grayscale reconstruction;k-nearest;neighbor;Support vector machine;Feature selection;Decision-graph;renal-cell carcinoma;support vector machines;mathematical morphology;pathological images;astrocytomas;algorithms;prostate;tracking;features;snakes||Project:||Pattern Recognition||期刊/報告no：:||Pattern Recognition, Volume 43, Issue 4, Page(s) 1550-1563.||摘要:||
Accurate grading for hepatocellular carcinoma (HCC) biopsy images is important to prognosis and treatment planning. In this paper, we propose an automatic system for grading HCC biopsy images. In preprocessing, we use a dual morphological grayscale reconstruction method to remove noise and accentuate nuclear shapes. A marker-controlled watershed transform is applied to obtain the initial contours of nuclei and a snake model is used to segment the shapes of nuclei smoothly and precisely. Fourteen features are then extracted based on six types of characteristics for HCC classification. Finally, we propose a SVM-based decision-graph classifier to classify HCC biopsy images. Experimental results show that 94.54% of classification accuracy can be achieved by using our SVM-based decision-graph classifier while 90.07% and 92.88% of classification accuracy can be achieved by using k-NN and SVM classifiers, respectively. (C) 2009 Elsevier Ltd. All rights reserved.
|Appears in Collections:||理學院|
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