Unsupervised Segmentation of Color-Texture Regions in Images and Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning the Topological Properties of Brain Tumors
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Augmented cell-graphs for automated cancer diagnosis
Bioinformatics
Fractal analysis in the detection of colonic cancer images
IEEE Transactions on Information Technology in Biomedicine
Histogram-based segmentation in a perceptually uniform color space
IEEE Transactions on Image Processing
Adaptive perceptual color-texture image segmentation
IEEE Transactions on Image Processing
Unsupervised segmentation for inflammation detection in histopathology images
ICISP'10 Proceedings of the 4th international conference on Image and signal processing
Tissue object patterns for segmentation in histopathological images
Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies
Ensemble classification of colon biopsy images based on information rich hybrid features
Computers in Biology and Medicine
Hi-index | 0.01 |
Staining methods routinely used in pathology lead to similar color distributions in the biologically different regions of histopathological images. This causes problems in image segmentation for the quantitative analysis and detection of cancer. To overcome this problem, unlike previous methods that use pixel distributions, we propose a new homogeneity measure based on the distribution of the objects that we define to represent tissue components. Using this measure, we demonstrate a new object-oriented segmentation algorithm. Working with colon biopsy images, we show that this algorithm segments the cancerous and normal regions with 94.89 percent accuracy on the average and significantly improves the segmentation accuracy compared to its pixel-based counterpart.