Texture classification using nonlinear color quantization: Application to histopathological image analysis
Olcay Sertel, Jun Kong, Gerard Lozanski, Arwa Shana’ah, Ümit V. Çatalyürek, Joel Haskin Saltz, Metin N. Gürcan · IEEE International Conference on Acoustics Speech and Signal Processing · 2008
In this paper, a novel color texture classification approach is introduced and applied to computer-assisted grading of follicular lymphoma from whole-slide tissue samples. The digitized tissue samples of follicular lymphoma were classified into histological grades under a statistical framework. The proposed method classifies the image either into low or high grades based on the amount of cytological components. To further discriminate the lower grades into low and mid grades, we proposed a novel color texture analysis approach. This approach modifies the gray level co-occurrence matrix method by using a non-linear color quantization with self-organizing feature maps (SOFMs). This is particularly useful for the analysis of H&E stained pathological images whose dynamic color range is considerably limited. Experimental results on real follicular lymphoma images demonstrate that the proposed approach outperforms the gray level based texture analysis.