Semi-Automatic Ulcer Segmentation and Wound Area Measurement Supporting Telemedicine

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Cazzolato, Mirela T. ; Ramos, Jonathan S. ; Rodrigues, Lucas S. ; Scabora, Lucas C. ; Chino, Daniel Y. T. ; Jorge, Ana E. S. ; de Azevedo-Marques, Paulo Mazzoncini ; Traina Jr, Caetano ; Traina, Agma J. M. ; DeHerrera, AGS ; Gonzalez, AR ; Santosh, KC ; Temesgen, Z ; Kane, B ; Soda, P

• Document type: Journal article
• Source: 2020 IEEE 33RD INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS(CBMS 2020); v. N/A, p. 6-pg., 2020-01-01.

Abstract

Many patients suffer from chronic skin lesions, commonly known as ulcers. The size evolution of chronic wounds provides meaningful clues regarding the patient's clinical state for healthcare professionals and caretakers. Many studies have been proposed in recent years to support the treatment of skin ulcers. However, there is a lack of practical solutions, as existing studies are not targeted at immediate use in daily medical practice. In this work, we propose URule, an essentially practical framework for segmentation and measurement of skin ulcers. URule-App, a mobile instance of the framework, analyzes images taken by a common camera from a mobile device. The segmentation requires the user to manually outline the outsider region of both the wound and the measurement tool. URule-Seg segments the image and estimates the wound area. The user can further improve the estimated area by manually informing the span of a centimeter in the image. The experimental evaluation reveals that URule can accurately segment ulcer wounds semi-automatically, with an average F-Measure of 0.8 for segmentation, and processing measurement tools better than the manual process in three out of five tested rulers. (AU)

• FAPESP's process: 16/17078-0 - Mining, indexing and visualizing Big Data in clinical decision support systems (MIVisBD)
• Grantee: Agma Juci Machado Traina
• Support Opportunities: Research Projects - Thematic Grants
• FAPESP's process: 20/07200-9 - Analyzing complex data from COVID-19 to support decision making and prognosis
• Grantee: Agma Juci Machado Traina
• Support Opportunities: Regular Research Grants
• FAPESP's process: 17/23780-2 - Content-based retrieval of medical images to aid the clinical decision using radiomics
• Grantee: Jonathan da Silva Ramos
• Support Opportunities: Scholarships in Brazil - Doctorate
• FAPESP's process: 18/24414-2 - A framework for integration of feature extraction techniques and complex databases for MIVisBD
• Grantee: Mirela Teixeira Cazzolato
• Support Opportunities: Scholarships in Brazil - Technical Training Program - Technical Training
• FAPESP's process: 16/17330-1 - Storage and Navigation Operations on Graphs in Relational DBMS
• Grantee: Lucas de Carvalho Scabora
• Support Opportunities: Scholarships in Brazil - Doctorate