Segmentation on Ovarian Cancer Tissue Microarray Using Frequency Weighted Mean Shift and Weighted Fuzzy C Means Algorithms (Published)
Quantifying the presence and extent of staining on account of a vascular biomarker on tissue microarrays is a laborious, time consuming and error prone job. Therefore there is a need for a powerful image segmentation algorithm. This is achieved using Hierarchical Normalized cuts algorithm which is driven by the use of a hierarchically represented data structure that merges two powerful image segmentation algorithms namely, Frequency weighted mean shift for supervised clustering of the images and Normalized cuts for graph partitioning. A system is designed for high throughput in computing and detecting staining of ovarian cancer on a very large pathology in less time. Hierarchical Normalized cut enables rapid analysis of large images. Weighted fuzzy c means for clustering and frequency weighted mean shift for reduced the color resolution that has to be applied in the Normalized cuts then to check the effienecy of the algorithm .Also it requires specification of only a few pixels from the object of interest and is highly insensitive to changes in users’ domain knowledge
Keywords: Fwms, HNCuts, feature weighted, fuzzy clustering