@inproceedings{b3beb0e01ec9435886262dc2d655c25c,
title = "An automatic liver fibrosis qualitative analysis method based on hyperspectral images",
abstract = "Serious liver fibrosis will develop into liver tumor. Therefore, prevention and early treatment of hepatocellular carcinoma are the focuses of the medical community. To automatically identify and analyze the degree of liver fibrosis, a more intuitive and convenient approach is proposed to segmentation of liver pathological slice images. This paper aims to use hyperspectral image processing technology to analyze the pathological sections of liver tissue cells. The method uses the spectral math for image preprocessing, and utilizes the superior classification ability of neural net (NN) and support vector machines (SVM) to identify the pathological images of liver tissue. On this basis, Majority/Minority Analysis (MMA) is as the post classified tool to weaken small plaques interference. At last the original image and the classification results are synthesized by RGB bands, and good analysis results can be obtained. The experimental results show that the presented method has great practical value in clinical diagnosis.",
keywords = "Hyperspectral Image, Liver Biopsy Images, Liver Fibrosis, Majority/Minority Analysis (MMA), Neural Net (NN), Support Vector Machine (SVM)",
author = "Wang, \{Jian Sheng\} and Mei Zhou and Qingli Li and Li Sun and Song Qiu",
note = "Publisher Copyright: {\textcopyright} 2018 SPIE.; 10th International Conference on Digital Image Processing, ICDIP 2018 ; Conference date: 11-05-2018 Through 14-05-2018",
year = "2018",
doi = "10.1117/12.2502980",
language = "英语",
isbn = "9781510621992",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Jenq-Neng Hwang and Xudong Jiang",
booktitle = "Tenth International Conference on Digital Image Processing, ICDIP 2018",
address = "美国",
}