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Proceedings Paper

Diagnosis of liver cancer based on the analysis of pathological liver color images
Author(s): Mohamed Sammouda; Rachid Sammouda; Noboru Niki; Kiyoshi Mukai
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Paper Abstract

Liver cancer is one of the leading cancerous diseases that can disappoint a physician before reaching the final diagnosis. Thus far, all cancer diagnoses should and usually do have tissue diagnose. A physician gets a little piece of tissue from the abnormal area and a pathologist determines if it is cancer or not. Therefore, the biopsy is the definitive test for liver cancer. In this paper, we present an unsupervised approach using Hopfield Neural Network (HNN) to segment color images of liver tissues prepared by standard staining method. The segmentation problem is formulated as the minimization of an energy function synonymous to that of HNN for optimization. We modify the HNN to reach a status close to the global minimum in a prespecified time of convergence. Furthermore, the nuclei and their corresponding cytoplasm regions are automatically extracted based on the features of color image histogram. The nuclei and cytoplasm regions are then used to formulate the diagnostic rules. In the analysis, we show a tables of the ratio of (nuclei/cytoplasm) image areas inside different subwindow sizes of the image. Each liver color image is represented in the RGB, HSV and HLS color spaces to investigate the effect of color system choice on the results. The automation of the extraction process in the liver pathological image can be easily implemented in the clinic in order to provide more accurate quantitative information that can help for a better liver cancer diagnosis.

Paper Details

Date Published: 6 June 2000
PDF: 8 pages
Proc. SPIE 3979, Medical Imaging 2000: Image Processing, (6 June 2000); doi: 10.1117/12.387601
Show Author Affiliations
Mohamed Sammouda, Univ. of Tokushima (Japan)
Rachid Sammouda, Univ. of Tokushima (Japan)
Noboru Niki, Univ. of Tokushima (Japan)
Kiyoshi Mukai, Tokyo Medical Univ. (Japan)


Published in SPIE Proceedings Vol. 3979:
Medical Imaging 2000: Image Processing
Kenneth M. Hanson, Editor(s)

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