Abstract
Accurate analysis of cellular structures has great importance for cancer diagnosis in histopathological images. Manual analysis of sections carried out by pathologists is time-consuming and costly. Analysis of cell structures with computer aid supports pathologists to diagnose cancer easily. In this paper, automated cell nuclei segmentation from histopathological images is investigated by using Fuzzy Local Information C-means Clustering (FLICM) Method. The Cancer Genome Atlas data set annotated by expert pathologists is used to evaluate the method. Compared with the other related studies, the highest f-measure and overlap values are obtained with this method.
| Original language | English |
|---|---|
| Title of host publication | 2019 Scientific Meeting on Electrical-Electronics and Biomedical Engineering and Computer Science, EBBT 2019 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9781728110134 |
| DOIs | |
| Publication status | Published - Apr 2019 |
| Event | 2019 Scientific Meeting on Electrical-Electronics and Biomedical Engineering and Computer Science, EBBT 2019 - Istanbul, Turkey Duration: 24 Apr 2019 → 26 Apr 2019 |
Publication series
| Name | 2019 Scientific Meeting on Electrical-Electronics and Biomedical Engineering and Computer Science, EBBT 2019 |
|---|
Conference
| Conference | 2019 Scientific Meeting on Electrical-Electronics and Biomedical Engineering and Computer Science, EBBT 2019 |
|---|---|
| Country/Territory | Turkey |
| City | Istanbul |
| Period | 24/04/19 → 26/04/19 |
Bibliographical note
Publisher Copyright:© 2019 IEEE.
Keywords
- Clustering
- Computer aided diagnosis
- Fuzzy c-means
- Fuzzy local information c-means
- Histopathological image segmentation
- Nuclei segmentation
