Noble approach for texture classification of H&E stained histopathological image by Gaussian wavelet

In this research paper we are introducing a classification approach for determining the texture feature and the subsequent classification of histopathological digital image i.e. applied computer-aided grading of follicular lymphoma (FL) and Neuroblastoma (NB) from whole-slide tissue samples. Basic idea behind this research is to distinguish among nuclei, cytoplasm, extracellular material and red blood cells from H&E stained input image so that doctors (radiologist) can provide better judgment during the prognosis of histopathological image that sometimes wrongly concluded. In this study we proposed a noble algorithm in which we convolve our H&E stained pathological images with 12 different orientation masks, resulting in an output of 12 different representations (corresponding to 12 different orientations) of our H&E stained input image. The information included in the 12 representations coming from the application of Gaussian filter is summarized in twelve images that correspond to each of the orientations used in the filters. We then combine these 12 images into one textured image represented as a 3-dimensional representation of input image. Experimental results on FL & NB demonstrate that the proposed approach outperforms the gray level based texture analysis.