A coupled-contour model for effective segmentation of cotton cross-sections in wide-field microscopic images. Issue 12 (July 2017)

• Record Type: Journal Article
• Authors: Wang, Quande; Xu, Bugao
• Is Part Of: Textile research journal. Volume 87:Issue 12(2017)
• Page Start: 1509
• Page End: 1521
• Publication Date: 2017-07
• Subjects: cotton cross-section -- contour detection -- contour refinement -- algorithm performance; Textile industry -- Periodicals; Textile fabrics -- Periodicals; Textile research -- Periodicals; Textiles et tissus -- Industrie et commerce -- Périodiques; Textiles et tissus -- Périodiques; Textiles et tissus -- Recherche -- Périodiques; Electronic journals; 677.005
• DOI: 10.1177/0040517516654113
• Languages: English
• ISSNs: 0040-5175

Abstract

Image analysis of a fiber cross-section can provide direct measurements for cotton maturity. Effective segmentation of fiber contours in a cross-sectional image is paramount for accurate fiber geometrical measurements. In a wide-field microscopic image, the adhesion, breakage, and ambiguity (low contrast or noise) of fiber contours make the segmentation rather challenging. This paper presents a new approach for contour segmentation that takes advantage of the shape features of the triple concentric contours, called the coupled-contour model (CCM), of a cross-section, and a CCM-based algorithm developed to locate, split, merge, and refine fiber contours based on the established rules concerning contour features. For a wide-field microscopic image (12 megapixels), this CCM-based algorithm could detect >500 fiber cross-sections with a recall rate of 93.53% and a precision rate of 98.13%, and reduced the errors in maturity measurements by 50%.