Rough-Bayesian approach to select class-pair specific descriptors for HEp-2 cell staining pattern recognition

One of the important problems in computer-aided diagnosis of connective tissue disease is automatic recognition of staining patterns present in HEp-2 cells. In this regard, the paper introduces a novel approach for the recognition of staining patterns by HEp-2 cell indirect immunofluorescence image analysis. The proposed method assumes that a fixed set of local texture descriptors or scales may not be effective for classifying staining patterns into multiple classes. A particular set of descriptors or scales may be significant for classifying a pair of classes, but may not be relevant for other pairs of classes. The proposed approach, therefore, first selects a set of local texture descriptors under appropriate scales for each class pair, and then forms the final feature set for multiple classes from the relevant descriptors of all possible pairs of classes. A novel framework, termed as Rough-Bayesian model, is introduced to evaluate the relevance of a descriptor and/or a scale. It is based on the merits of rough sets and Bayes decision theory. During the selection of relevant descriptor and/or scale, the proposed method takes care of the presence of both noisy pixels in an HEp-2 cell image and noisy HEp-2 cell images in a staining pattern class. The support vector machine is used to predict the staining patterns present in HEp-2 cell images. The performance of the proposed method, along with a comparison with state-of-the-art methods, is demonstrated on several HEp-2 cell image databases. An important finding is that the accuracy for classifying HEp-2 cell images is significantly increased if class-pair specific descriptors under appropriate scales are considered, instead of selecting a uniform set of descriptors and scales for multiple classes. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

This study introduces a novel approach for automatic recognition of staining patterns in HEp-2 cells, utilizing a Rough-Bayesian model to evaluate descriptor relevance and a support vector machine to predict staining patterns. The proposed method outperforms state-of-the-art methods and significantly improves the accuracy of classifying HEp-2 cell images.