دورية أكاديمية
Automatic measurement of pressure ulcers using Support Vector Machines and GrabCut.
| العنوان: | Automatic measurement of pressure ulcers using Support Vector Machines and GrabCut. |
|---|---|
| المؤلفون: | Silva RHLE; Graduate Program on Electrical Engineering, Pontifical Catholic University of Minas Gerais, Belo Horizonte, Brazil., Machado AMC; Department of Computer Science, Pontifical Catholic University of Minas Gerais and with the Department of Anatomy and Imaging, School of Medicine, Federal University of Minas Gerais, Belo Horizonte, Brazil. Electronic address: alexeimcmachado@gmail.com. |
| المصدر: | Computer methods and programs in biomedicine [Comput Methods Programs Biomed] 2021 Mar; Vol. 200, pp. 105867. Date of Electronic Publication: 2020 Nov 24. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Elsevier Scientific Publishers Country of Publication: Ireland NLM ID: 8506513 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1872-7565 (Electronic) Linking ISSN: 01692607 NLM ISO Abbreviation: Comput Methods Programs Biomed Subsets: MEDLINE |
| أسماء مطبوعة: | Publication: Limerick : Elsevier Scientific Publishers Original Publication: Amsterdam : Elsevier Science Publishers, c1984- |
| مواضيع طبية MeSH: | Pressure Ulcer*/diagnostic imaging , Support Vector Machine*, Algorithms ; Humans |
| مستخلص: | 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. Competing Interests: Declaration of Competing Interest The authors declare no conflicts of interest. (Copyright © 2020. Published by Elsevier B.V.) |
| فهرسة مساهمة: | Keywords: Pressure ulcers; image segmentation; medical image analysis; support vector machines |
| تواريخ الأحداث: | Date Created: 20201202 Date Completed: 20210514 Latest Revision: 20210514 |
| رمز التحديث: | 20221213 |
| DOI: | 10.1016/j.cmpb.2020.105867 |
| PMID: | 33261945 |
| قاعدة البيانات: | MEDLINE |
Full Text via ClinicalKey 
Full Text Finder | تدمد: | 1872-7565 |
|---|---|
| DOI: | 10.1016/j.cmpb.2020.105867 |