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Smartphone-Based Wound Assessment System for Patients with Diabetes Lei Wang, Student Member, IEEE, Peder C

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Smartphone-Based Wound Assessment System for Patients with Diabetes Lei Wang, Student Member, IEEE, Peder C IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 62, NO. 2, FEBRUARY 2015 477 Smartphone-Based Wound Assessment System for Patients With Diabetes Lei Wang, Student Member, IEEE, Peder C. Pedersen*, Senior Member, IEEE, Diane M. Strong, Bengisu Tulu, Member, IEEE, Emmanuel Agu, and Ronald Ignotz Abstract—Diabetic foot ulcers represent a significant health is- statistics, diabetes-related wounds are the primary cause of non- sue. Currently, clinicians and nurses mainly base their wound as- traumatic lower limb amputations with approximately 71 000 sessment on visual examination of wound size and healing status, such amputations in the US in 2004 [5]. Moreover, the cost of while the patients themselves seldom have an opportunity to play an active role. Hence, a more quantitative and cost-effective exami- treating diabetic foot ulcers is estimated at $15 000 per year nation method that enables the patients and their caregivers to take per individual. Overall diabetes healthcare cost was estimated a more active role in daily wound care potentially can accelerate at $245 billion in 2012 and is expected to increase in the coming wound healing, save travel cost and reduce healthcare expenses. years [5]. Considering the prevalence of smartphones with a high-resolution There are several problems with current practices for treat- digital camera, assessing wounds by analyzing images of chronic foot ulcers is an attractive option. In this paper, we propose a novel ing diabetic foot ulcers. First, patients must go to their wound wound image analysis system implemented solely on the Android clinic on a regular basis to have their wounds checked by their smartphone. The wound image is captured by the camera on the clinicians. This need for frequent clinical evaluation is not only smartphone with the assistance of an image capture box. After inconvenient and time consuming for patients and clinicians, that, the smartphone performs wound segmentation by applying but also represents a significant health care cost because patients the accelerated mean-shift algorithm. Specifically, the outline of the foot is determined based on skin color, and the wound boundary may require special transportation, e.g., ambulances. Second, a is found using a simple connected region detection method. Within clinician’s wound assessment process is based on visual exam- the wound boundary, the healing status is next assessed based on ination. He/she describes the wound by its physical dimensions red–yellow–black color evaluation model. Moreover, the healing and the color of its tissues, providing important indications of status is quantitatively assessed, based on trend analysis of time the wound type and the stage of healing [6]. Because the visual records for a given patient. Experimental results on wound im- ages collected in UMASS—Memorial Health Center Wound Clinic assessment does not produce objective measurements and quan- (Worcester, MA) following an Institutional Review Board approved tifiable parameters of the healing status [7], tracking a wound’s protocol show that our system can be efficiently used to analyze the healing process across consecutive visits is a difficult task for wound healing status with promising accuracy. both clinicians and patients. Index Terms—Android-based smartphone, mean shift, patients Technology employing image analysis techniques is a poten- with diabetes, wound analysis. tial solution to both these problems. Several attempts have been made to use image processing techniques for such tasks, includ- ing the measurement of area, or alternatively using a volume I. INTRODUCTION instrument system (MAVIS)[8] or a medical digital photogram- OR individuals with type 2 diabetes, foot ulcers constitute metric system (MEDPHOS) [9]. These approaches suffer from F a significant health issue affecting 5–6 million individuals several drawbacks including high cost, complexity, and lack of in the US [1], [2]. Foot ulcers are painful, susceptible to in- tissue classification [9]. fection and very slow to heal [3], [4]. According to published To better determine the wound boundary and classify wound tissues, researchers have applied image segmentation and super- Manuscript received December 19, 2013; revised May 5, 2014 and August 1, vised machine learning algorithm for wound analysis. A French 2014; accepted September 5, 2014. Date of publication September 17, 2014; date research group proposed a method of using a support vector of current version January 16, 2015. This work was supported by the National Science Foundation under Grant IIS-1065298. Asterisk indicates corresponding machine (SVM)-based wound classification method [10], [11]. author. The same idea has also been employed in [12] for the detection L. Wang is with the Electrical and Computer Engineering Depart- of melanoma at a curable stage. Although the SVM classifier ment, Worcester Polytechnic Institute, Worcester, MA 01609 USA (e-mail: [email protected]). method led to good results on typical wound images [10], it is ∗P. C. Pedersen is with the Electrical and Computer Engineering Department, not feasible to implement the training process and the feature Worcester Polytechnic Institute, Worcester, MA 01609 USA (e-mail: pedersen@ extraction on current smartphones due to its computational de- wpi.edu). D. M. Strong and B. Tulu are with the School of Business, Worcester mands. Furthermore, the supervised learning algorithm requires Polytechnic Institute, Worcester, MA 01609 USA (e-mail: [email protected]; a large number of training image samples and experienced clin- [email protected]). ical input, which is difficult and costly. E. Agu is with the Computer Science Department, Worcester Polytechnic Institute, Worcester, MA 01609 USA (e-mail: [email protected]). Our solution provides image analysis algorithms that run on R. Ignotz is with the Plastic Surgery Department, UMASS Medical School, a smartphone, and thus provide a low cost and easy-to-use de- Worcester, MA 01655 USA (e-mail: [email protected]). vice for self-management of foot ulcers for patients with type 2 Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. diabetes. Our solution engages patients as active participants in Digital Object Identifier 10.1109/TBME.2014.2358632 their own care, meeting the recommendation of the Committee 0018-9294 © 2014 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications standards/publications/rights/index.html for more information. 478 IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 62, NO. 2, FEBRUARY 2015 on Quality of Health Care in America to provide more infor- algorithm used in our system and related region merge methods. mation technology solutions [13]. The widely used commodity Section II-C introduces the wound analysis method based on smartphone containing a high-resolution camera is a viable can- the image segmentation results including foot outline detection, didate for image capture and image processing provided that the wound boundary determination, color segmentation within the processing algorithms are both accurate and well suited for the wound and healing status evaluation. In Section III, the GPU op- available hardware and computational resources. To convert an timization method of the mean-shift segmentation algorithm is ordinary smartphone into a practical device for self-management discussed. Section IV presents the image capture box designed of diabetic wounds, we need to address two tasks: 1) develop for patients with diabetic foot ulcers to easily use the smartphone a simple method for patients to capture an image of their foot to take an image of the bottom of their foot. Experimental results ulcers; and 2) design a highly efficient and accurate algorithm are presented and analyzed in Section V. Finally, Section VI for real-time wound analysis that is able to operate within the provides an overall assessment of the wound image analysis computational constraints of the smartphone. system. A preliminary version of this paper has been reported Our solution for task 1) was specifically designed to aid pa- in [18]. tients with type 2 diabetes in photographing ulcers occurring on the sole of their feet. This is particularly challenging due II. WOUND ANALYSIS METHOD to mobility limitations, common for individuals with advanced A. Wound Image Analysis System Overview diabetes. To this end, we designed and built an image capture box with an optical system containing a dual set of front sur- Our quantitative wound assessment system consists of several face mirrors, integrated LED lighting and a comfortable, slanted functional modules including wound image capture, wound im- surface for the patients to place their foot. The design ensures age storage, wound image preprocessing, wound boundary de- consistent illumination and a fixed optical path length between termination, wound analysis by color segmentation and wound the sole of the foot and the camera, so that pictures captured at trend analysis based on a time sequence of wound images for a different times would be taken from the same camera angles and given patient. All these processing steps are carried out solely by under the same lighting conditions. Task 2) was implemented the computational resources of the smartphone. The functional by utilizing an accurate, yet computationally efficient algorithm, diagram of our quantitative wound assessment system is shown i.e., the mean-shift algorithm, for wound boundary determina- as in Fig. 1 and explained later. Note that the words highlighted tion, followed by color segmentation within the wound area for in italics in the text correspond to specific blocks in figures assessing healing status. with block diagrams. While the image capture is the first step In our previous work [14], the wound boundary determina- in the flowchart, the image capture box is not one of the image tion was done with a particular implementation of the level set processing steps and is therefore presented later in Section IV. algorithm, specifically the distance regularized level set evolu- A Nexus 4 smartphone was chosen due to its excellent tion method [15].
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