Histopathology images exhibit considerable variability, which can make diagnosis prone to uncertainty and errors. Using retrieval systems to locate similar images when a query image is given can assist pathologists in making more reliable decisions when diagnosing, based on accurately diagnosed past cases. Local binary patterns (LBP) have been successfully used as image descriptors for different applications. However, using LBP on histopathology images is still under investigation from different perspectives. The immense texture variability of these images and the lack of labeled training data are among the challenges that must be addressed. In this paper, we propose a new extension of LBP that is explicitly aware of the heterogeneity of local texture patterns through heterogeneity-based weighting. We use both homogeneity and the second moment (variance) of local neighborhoods to extend LBP histograms with heterogeneity information so that they better capture the polymorphism in histopathology images. We applied all concepts at multiple scales and investigated the extensions through both separated and concatenated extended histograms. We applied the proposed method on three publicly available datasets, namely, KimiaPath24, invasive ductal carcinoma (IDC), and BreakHis. The experimental results revealed that our features could retrieve and classify images with the highest accuracy. For KimiaPath24, we achieved 96.4% surpassing both LBP (91.0%) and deep features from VGG network (79.5%). For IDC, we reached the highest F1 measure (0.7665). Only for BreakHis, the proposed method was slightly less accurate than a customized convolutional neural network with rule fusion.INDEX TERMS Content based image retrieval (CBIR), whole slide image (WSI) processing, heterogeneity mask, local binary pattern (LBP).

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Content-based image retrieval (CBIR) of medical images in large datasets to identify similar images when a query image is given can be very useful in improving the diagnostic decision of the clinical experts and as well in educational scenarios. In this paper, we used two stage classification and retrieval approach to retrieve similar images. First, the Gabor filters are applied to Radon-transformed images to extract features and to train a multi-class SVM. Then based on the classification results and using an extracted Gabor barcode, similar images are retrieved. The proposed method was tested on IRMA dataset which contains more than 14,000 images. Experimental results show the efficiency of our approach in retrieving similar images compared to other Gabor-Radon-oriented methods.