Image Analysis in Histology: Conventional and Confocal Microscopy
Image Analysis in Histology: Conventional and Confocal Microscopy
Image Mining: Trends and Developments
Journal of Intelligent Information Systems
Image Indexing Using Color Correlograms
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Color Co-occurence Descriptors for Querying-by-Example
MMM '98 Proceedings of the 1998 Conference on MultiMedia Modeling
Semantic content analysis and annotation of histological images
Computers in Biology and Medicine
Journal of Signal Processing Systems
Mining Lung Shape from X-Ray Images
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
Three-Dimensional Nonlinear Invisible Boundary Detection
IEEE Transactions on Image Processing
Hi-index | 0.00 |
A method is suggested for identification and visualization of histology image structures relevant to the key characteristics of the state of cancer patients. The method is based on a multi-step procedure which includes calculating image descriptors, extracting their principal components, correlating them to known object properties and mapping disclosed regularities all the way back up to the corresponding image structures they found to be linked with. Image descriptors employed are extended 4D color co-occurrence matrices counting the occurrence of all possible pixel triplets located at the vertices of equilateral triangles of different size. The method is demonstrated on a sample of 952 histology images taken from 68 women with clinically confirmed diagnosis of ovarian cancer. As a result, a number of associations between the patients' conditions and morphological image structures were found including both easily explainable and the ones whose biological substrate remains obscured.