A Multi-resolution Approach for Combining Visual Information using Nuclei Segmentation and Classification in Histopathological Images

This paper describes a multi-resolution technique to combine diagnostically important visual information at different magnifications in H&E whole slide images (WSI) of gastric cancer. The primary goal is to improve the results of nuclei segmentation method for heterogeneous histopathological datasets with variations in stain intensity and malignancy levels. A minimum-model nuclei segmentation method is first applied to tissue images at multiple resolutions, and a comparative evaluation is performed. A comprehensive set of 31 nuclei features based on color, texture and morphology are derived from the nuclei segments. AdaBoost classification method is used to classify these segments into a set of pre-defined classes. Two classification approaches are evaluated for this purpose. A relevance score is assigned to each class and a combined segmentation result is obtained consisting of objects with higher visual significance from individual magnifications, thereby preserving both coarse and fine details in the image. Quantitative and visual assessment of combination results shows that they contain comprehensive and diagnostically more relevant information than in constituent