Partitioning Histopathological Images: An Integrated Framework for Supervised Color-Texture Segmentation and Cell Splitting

For quantitative analysis of histopathological images, such as the lymphoma grading systems, quantification of features is usually carried out on single cells before categorizing them by classification algorithms. To this end, we propose an integrated framework consisting of a novel supervised cell-image segmentation algorithm and a new touching-cell splitting method. For the segmentation part, we segment the cell regions from the other areas by classifying the image pixels into either cell or extra-cellular category. Instead of using pixel color intensities, the color-texture extracted at the local neighborhood of each pixel is utilized as the input to our classification algorithm. The color-texture at each pixel is extracted by local Fourier transform (LFT) from a new color space, the most discriminant color space (MDC). The MDC color space is optimized to be a linear combination of the original RGB color space so that the extracted LFT texture features in the MDC colo...

• Type: Journal
• Year: 2011
• Where: IEEE Transactions on Medical Imagine (TMI)
• Authors: Hui Kong, Metin Gurcan, and Kamel Belkacem-Boussaid
• Keywords: Color-texture Feature Extraction, Discriminant Analysis, Follicular Lymphoma, Histopathological Image Segmentation, Local Fourier Transform, Radial-symmetry Point, Supervised Learning, Touchingcell Splitting