Back to Search
Start Over
Automated Segmentation of Nuclei in Breast Cancer Histopathology Images
- Source :
- PLoS ONE, PLoS ONE, Vol 11, Iss 9, p e0162053 (2016)
- Publication Year :
- 2016
- Publisher :
- Public Library of Science, 2016.
-
Abstract
- The process of Nuclei detection in high-grade breast cancer images is quite challenging in the case of image processing techniques due to certain heterogeneous characteristics of cancer nuclei such as enlarged and irregularly shaped nuclei, highly coarse chromatin marginalized to the nuclei periphery and visible nucleoli. Recent reviews state that existing techniques show appreciable segmentation accuracy on breast histopathology images whose nuclei are dispersed and regular in texture and shape; however, typical cancer nuclei are often clustered and have irregular texture and shape properties. This paper proposes a novel segmentation algorithm for detecting individual nuclei from Hematoxylin and Eosin (H&E) stained breast histopathology images. This detection framework estimates a nuclei saliency map using tensor voting followed by boundary extraction of the nuclei on the saliency map using a Loopy Back Propagation (LBP) algorithm on a Markov Random Field (MRF). The method was tested on both whole-slide images and frames of breast cancer histopathology images. Experimental results demonstrate high segmentation performance with efficient precision, recall and dice-coefficient rates, upon testing high-grade breast cancer images containing several thousand nuclei. In addition to the optimal performance on the highly complex images presented in this paper, this method also gave appreciable results in comparison with two recently published methods-Wienert et al. (2012) and Veta et al. (2013), which were tested using their own datasets. Science Foundation Ireland SFI-ISCA (Science Foundation Ireland - International Strategic Cooperation Award) program
- Subjects :
- Pathology
Computer science
H&E stain
lcsh:Medicine
Gene Expression
Imaging techniques
02 engineering and technology
Pathology and Laboratory Medicine
Image analysis
Pattern Recognition, Automated
Breast cancer
0302 clinical medicine
Breast Tumors
Medicine and Health Sciences
Group-Specific Staining
Image Processing, Computer-Assisted
Segmentation
Breast
lcsh:Science
Staining
Multidisciplinary
Markov random field
Chromosome Biology
Applied Mathematics
Simulation and Modeling
Chromatin
Oncology
030220 oncology & carcinogenesis
Pattern recognition (psychology)
Physical Sciences
Epigenetics
Female
Algorithms
Research Article
medicine.medical_specialty
Imaging Techniques
0206 medical engineering
Automated segmentation
Histopathology
Image processing
Breast Neoplasms
Image Analysis
Research and Analysis Methods
03 medical and health sciences
Breast Cancer
Image Interpretation, Computer-Assisted
medicine
Genetics
Humans
Saliency map
Cell Nucleus
Staining and Labeling
business.industry
lcsh:R
Hematoxylin Staining
Hematoxylin staining
Cancer
Cancers and Neoplasms
Biology and Life Sciences
Pattern recognition
Cell Biology
medicine.disease
020601 biomedical engineering
Nuclear Staining
Anatomical Pathology
Specimen Preparation and Treatment
Nuclear staining
lcsh:Q
Artificial intelligence
business
Mathematics
Subjects
Details
- Language :
- English
- ISSN :
- 19326203
- Volume :
- 11
- Issue :
- 9
- Database :
- OpenAIRE
- Journal :
- PLoS ONE
- Accession number :
- edsair.doi.dedup.....cdbaf5dc8ef7bcccbdba063bb4638b08