Rough-Bayesian approach to select class-pair specific descriptors for HEp-2 cell staining pattern recognition. (September 2021)

• Record Type: Journal Article
• Authors: Kumar, Debamita; Maji, Pradipta
• Is Part Of: Pattern recognition. Volume 117(2021)
• Publication Date: 2021-09
• Subjects: HEp-2 cell images -- Staining pattern recognition -- Texture analysis -- Rough sets -- Bayes decision theory; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2021.107982
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: The proposed method selects a set of relevant descriptors for each pair of classes. Final feature set for multiple classes is formed considering all pairs of classes. decision theory and rough set are used to assess the relevance of descriptors. The efficacy of the proposed method is demonstrated on several HEp-2 cell databases. Significant increase in accuracy is noted employing class-pair specific descriptors. Abstract: 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.