Liver cancer detection system based on the analysis of digitized color images of tissue samples obtained using needle biopsy
Sammouda, Rachid . 2002
In this article, the authors propose a method for automatic diagnosis of liver cancer based on analysis of digitized color images of liver tissue obtained by needle biopsy. The approach is a combination of an unsupervised segmentation algorithm, using a modified artificial Hopfield neural network (HNN), and an analysis algorithm based on image quantization. The segmentation algorithm is superior to HNN in the sense that it converges to a nearby global minimum rather than a local one in a prespecified time. Furthermore, as the segmentation of color images does not only depend on the segmentation algorithm but also on the color space representation, and in order to choose the best segmentation result, segmentation was performed with HNN and using components of the raw image with respect to each of the RGB, HLS, and HSV color spaces. Then, the segmented image was labeled based on chromaticity features and histogram analysis of the RGB color space components of the raw image. The image regions were then classified into normal and cancerous using diagnostic rules formulated based on those used by experienced pathologists in the clinic. The proposed method provides quantitative satisfactory results in diagnosing a liver pathological image set of 17 cases.
Presents contributions to improve a previously published approach for the segmentation of magnetic resonance images of the human brain, based on an unsupervised Hopfield neural network.
The paper presents a method for automatic segmentation of sputum cells color images, to develop an efficient algorithm for lung cancer diagnosis based on a Hopfield neural network.