Quantitative evaluation of different thresholding methods using automatic reference image creation via PCA. Issue 7 (9th August 2021)

• Record Type: Journal Article
• Authors: Panigrahi, Susant Kumar; Gupta, Supratim; Vamsee Krishna, S
• Is Part Of: International journal of computers and applications. Volume 43:Issue 7(2021)
• Page Start: 653
• Page End: 662
• Publication Date: 2021-08-09
• Subjects: Edge mismatch error -- F-measure -- modified Hausdorff distance -- object level consistency error -- principal component analysis -- relative area error; Computers -- Periodicals; Computer software -- Periodicals; Computer networks -- Periodicals; Multimedia systems -- Periodicals; Internet -- Periodicals; World Wide Web -- Periodicals; Minicomputers -- Periodicals; Microcomputers -- Periodicals; 004.05
• DOI: 10.1080/1206212X.2019.1615175
• Languages: English
• ISSNs: 1206-212X

Abstract

Abstract : This article proposes a principal component analysis-based automatic approach to generate reference image for evaluating different thresholding techniques. Twenty one thresholding methods have been considered for reference image creation and evaluated using five standard performance indices. Literature suggests a few performance measures, among which F-measure, modified Hausdorff distance, edge mismatch error, relative area error and object level consistency error are popular. However, correlation analysis of these metrics reveal that only F-measure, modified Hausdorff distance and edge mismatch error retain non-redundant information. Thus the best thresholding method can be determined from these three indices for different types of images, automatically. Experimental results demonstrate the potential of different thresholding methods and select the best binary segmentation technique for particular type of image set.