Automatic measurement of pressure ulcers using Support Vector Machines and GrabCut. (March 2021)

• Record Type: Journal Article
• Authors: Silva, Rodolfo Herman Lara e; Machado, Alexei Manso Correa
• Is Part Of: Computer methods and programs in biomedicine. Volume 200(2021)
• Publication Date: 2021-03
• Subjects: Pressure ulcers -- image segmentation -- support vector machines -- medical image analysis; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2020.105867
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: The automatic assessment of ulcers is an important factor to monitor the healing process Automatic ulcer measurement reduces inconsistencies and the risk of infections The association of machine learning techniques and superpixel segmentation may outperform deep learning methods Abstract: Background and Objective: Pressure ulcers are regions of trauma caused by a continuous pressure applied to soft tissues between a bony prominence and a hard surface. The manual monitoring of their healing evolution can be achieved by area assessment techniques that include the use of rulers and adhesive labels in direct contact with the injury, being highly inaccurate and subjective. In this paper we present a Support Vector Machine classifier in combination with a modified version of the GrabCut method for the automatic measurement of the area affected by pressure ulcers in digital images. Methods: Three methods of region segmentation using the superpixel strategy were evaluated from which color and texture descriptors were extracted. After the superpixel classification, the GrabCut segmentation method was applied in order to delineate the region affected by the ulcer from the rest of the image. Results: Experiments on a set of 105 pressure ulcer images from a public data set resulted in an average accuracy of 96%, sensitivity of 94%, specificity of 97% and precision of 94%. Conclusions: The association of support vector machines with superpixel segmentation outperformed current methods based on deep learning and may be extended to tissue classification.