Open AccessLower extremity ulcer image segmentation of visual and near‐infrared imagery
Author(s) -
Bochko Vladimir,
Välisuo Petri,
Harju Toni,
Alander Jarmo
Publication year - 2010
Publication title -
skin research and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.521
H-Index - 69
eISSN - 1600-0846
pISSN - 0909-752X
DOI - 10.1111/j.1600-0846.2009.00415.x
Subject(s) - artificial intelligence , computer vision , segmentation , infrared , computer science , medicine , optics , physics
Background/purpose: We propose an automatic ulcer segmentation system with a simple manual correction possibility. In addition to visual color information, we use near‐infrared (NIR) images because NIR can penetrate deeper into tissue than visual light. The system is able to measure the surface area of a lower extremity ulcer segmented at its different stages and constructs corresponding healing curves over time. This knowledge is useful in monitoring lower extremity ulcers and helps clinicians select the most efficient therapy. Methods: Eighteen lower extremity ulcers and one ulcer on the back were examined from 17 patients. The patients were elderly individuals residing in the long‐term care department of the Vaasa city hospital. One of the patients (P14) had been diagnosed with diabetes. The inclusion criteria for patients were an ulcer with a suitable size for the imaging device and the free will to volunteer. We developed a four‐band spectral digital camera to image the reflectance of the skin. We use the spectral image pixels, in visual light and NIR, in analysis of lower extremity ulcers. For segmentation, the support vector classifier was found to be the best one. The segmentation system is designed to analyze three main ulcer tissue classes: black/necrotic, yellow/fibrous and red/granulation tissue. Results: The experiments conducted confirm the feasibility of our approach. In most cases, the computed healing curves correspond to those made manually. The maximum error rate of ulcer area measurement for red/granulation tissue is 33% for 20 cases. This corresponds to the results published in the literature. The black/necrotic tissue may be located deeper under the skin surface; hence, the ulcer boundaries are not well defined, allowing only a rough estimate, yielding a maximum error of 44% for the three cases analyzed. For yellow/fibrous tissue, we had only one image in our database, whose error value is 23%. Conclusion: We propose a new imaging system for segmentation and measurement of different kinds of ulcers. This system is useful in practice for analysis and measurement of ulcer surface areas and observation of their change over time, which helps clinicians in the treatment of ulcers.