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Graph Run-Length Matrices for Histopathological Image Segmentation
- Source :
- IEEE Transactions on Medical Imaging
- Publication Year :
- 2011
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2011.
-
Abstract
- Cataloged from PDF version of article. The histopathological examination of tissue specimens is essential for cancer diagnosis and grading. However, this examination is subject to a considerable amount of observer variability as it mainly relies on visual interpretation of pathologists. To alleviate this problem, it is very important to develop computational quantitative tools, for which image segmentation constitutes the core step. In this paper, we introduce an effective and robust algorithm for the segmentation of histopathological tissue images. This algorithm incorporates the background knowledge of the tissue organization into segmentation. For this purpose, it quantifies spatial relations of cytological tissue components by constructing a graph and uses this graph to define new texture features for image segmentation. This new texture definition makes use of the idea of gray-level run-length matrices. However, it considers the runs of cytological components on a graph to form a matrix, instead of considering the runs of pixel intensities. Working with colon tissue images, our experiments demonstrate that the texture features extracted from "graph run-length matrices" lead to high segmentation accuracies, also providing a reasonable number of segmented regions. Compared with four other segmentation algorithms, the results show that the proposed algorithm is more effective in histopathological image segmentation
- Subjects :
- Perceptual Image Segmentation
Colon
Computer science
Feature extraction
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Scale-space segmentation
Histopathological Image Analysis, Image Segmentation
Sensitivity and Specificity
Pattern Recognition, Automated
Image Texture Analysis
Histopathological image analysis
Minimum spanning tree-based segmentation
Image texture
Artificial Intelligence
Image Interpretation, Computer-Assisted
Humans
Computer vision
Segmentation
Electrical and Electronic Engineering
Cancer
Image segmentation
Microscopy
Radiological and Ultrasound Technology
Pixel
business.industry
Segmentation-based object categorization
Perceptual image segmentation
Reproducibility of Results
Pattern recognition
Image Enhancement
Computer Science Applications
Region growing
Artificial intelligence
Colorectal Neoplasms
business
Graphs
Connected-component labeling
Algorithms
Software
Subjects
Details
- ISSN :
- 1558254X and 02780062
- Volume :
- 30
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Medical Imaging
- Accession number :
- edsair.doi.dedup.....c37dde4641e6e8be500b63553bbd9ee7