Comparative study on feature descriptors for brain image analysis
A key obstacle to developing automated histopathology assessment tools is the difficulty of defining quantifiable image features that could serve as fundamental data elements capable of distinguishing key disease types and subtypes. A variety of feature extraction and selection methods for histology images have been proposed. However, comparisons of different feature descriptor approaches remains challenging because of varying datasets and emphases chosen by different authors. As an example of how a shared reference atlas could accelerate efforts in this area. In this study, we constructed normal and disease sample datasets by standardizing histology images employed from Allen Brain Atlas. After preparing the datasets, we extracted features mentioned in the preceding studies from the datasets to characterize normal and disease tissues. To confirm statistical significance between the normal and disease images, Kolmogorov-Smirnov test was employed. The experimental results indicated that topological features are effective to distinguish the normal images from the disease ones. This paper also shows the details of construction of the datasets, segmentation of nuclei, feature descriptors and the experimental results. We discuss the effectiveness and generalizability of derived features.