Detection of fibrosis in liver biopsy images by using Bayesian classifier
© 2015 IEEE. In this paper, an image-processing-based method designed to detect fibrosis in liver biopsy images is proposed. The proposed method first enhances the color difference between liver tissue and fibrosis areas. Then, a low-pass filtering is applied to each color band to reduce noise. In o...
Saved in:
Main Authors: | , , , , , , |
---|---|
Other Authors: | |
Format: | Conference or Workshop Item |
Published: |
2018
|
Subjects: | |
Online Access: | https://repository.li.mahidol.ac.th/handle/123456789/35847 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Mahidol University |
id |
th-mahidol.35847 |
---|---|
record_format |
dspace |
spelling |
th-mahidol.358472018-11-23T17:02:35Z Detection of fibrosis in liver biopsy images by using Bayesian classifier Kanyanat Meejaroen Charoen Chaweechan Wanus Khodsiri Vorapranee Khu-Smith Ukrit Watchareeruetai Pattana Sornmagura Taya Kittiyakara King Mongkut's Institute of Technology Ladkrabang Mahidol University Computer Science © 2015 IEEE. In this paper, an image-processing-based method designed to detect fibrosis in liver biopsy images is proposed. The proposed method first enhances the color difference between liver tissue and fibrosis areas. Then, a low-pass filtering is applied to each color band to reduce noise. In order to calculate the percentage of fibrosis against total liver tissue, the background area, i.e. empty slide area, is detected. Next, Bayesian classifier is used to separate fibrosis from liver tissue based on the color information. Finally, the proportion of the fibrosis area to the tissue area is computed. Experimental results show that the proposed method can estimate and detect fibrosis in the liver biopsy images with the classification accuracy of 91.42%. In addition, the average difference between the percentage of fibrosis obtained from the proposed method and that in ground truth images is 2.29 points. 2018-11-23T10:02:35Z 2018-11-23T10:02:35Z 2015-01-01 Conference Paper Proceedings of the 2015-7th International Conference on Knowledge and Smart Technology, KST 2015. (2015), 184-189 10.1109/KST.2015.7051484 2-s2.0-84925857633 https://repository.li.mahidol.ac.th/handle/123456789/35847 Mahidol University SCOPUS https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84925857633&origin=inward |
institution |
Mahidol University |
building |
Mahidol University Library |
continent |
Asia |
country |
Thailand Thailand |
content_provider |
Mahidol University Library |
collection |
Mahidol University Institutional Repository |
topic |
Computer Science |
spellingShingle |
Computer Science Kanyanat Meejaroen Charoen Chaweechan Wanus Khodsiri Vorapranee Khu-Smith Ukrit Watchareeruetai Pattana Sornmagura Taya Kittiyakara Detection of fibrosis in liver biopsy images by using Bayesian classifier |
description |
© 2015 IEEE. In this paper, an image-processing-based method designed to detect fibrosis in liver biopsy images is proposed. The proposed method first enhances the color difference between liver tissue and fibrosis areas. Then, a low-pass filtering is applied to each color band to reduce noise. In order to calculate the percentage of fibrosis against total liver tissue, the background area, i.e. empty slide area, is detected. Next, Bayesian classifier is used to separate fibrosis from liver tissue based on the color information. Finally, the proportion of the fibrosis area to the tissue area is computed. Experimental results show that the proposed method can estimate and detect fibrosis in the liver biopsy images with the classification accuracy of 91.42%. In addition, the average difference between the percentage of fibrosis obtained from the proposed method and that in ground truth images is 2.29 points. |
author2 |
King Mongkut's Institute of Technology Ladkrabang |
author_facet |
King Mongkut's Institute of Technology Ladkrabang Kanyanat Meejaroen Charoen Chaweechan Wanus Khodsiri Vorapranee Khu-Smith Ukrit Watchareeruetai Pattana Sornmagura Taya Kittiyakara |
format |
Conference or Workshop Item |
author |
Kanyanat Meejaroen Charoen Chaweechan Wanus Khodsiri Vorapranee Khu-Smith Ukrit Watchareeruetai Pattana Sornmagura Taya Kittiyakara |
author_sort |
Kanyanat Meejaroen |
title |
Detection of fibrosis in liver biopsy images by using Bayesian classifier |
title_short |
Detection of fibrosis in liver biopsy images by using Bayesian classifier |
title_full |
Detection of fibrosis in liver biopsy images by using Bayesian classifier |
title_fullStr |
Detection of fibrosis in liver biopsy images by using Bayesian classifier |
title_full_unstemmed |
Detection of fibrosis in liver biopsy images by using Bayesian classifier |
title_sort |
detection of fibrosis in liver biopsy images by using bayesian classifier |
publishDate |
2018 |
url |
https://repository.li.mahidol.ac.th/handle/123456789/35847 |
_version_ |
1763498102704046080 |