Smartphone-Based Wound Assessment System for Patients With Diabetes

Smartphone-Based Wound Assessment System for Patients With Diabetes


Diabetic foot ulcers represent a significant health issue. Currently, clinicians and nurses mainly base their wound assessment on visual examination of wound size and healing status, while the patients themselves seldom have an opportunity to play an active role. Hence, amore quantitative and cost-effective examination method that enables the patients and their caregivers to take a more active role in daily wound care potentially can accelerate wound healing, save travel cost and reduce healthcare expenses. Considering the prevalence of smartphones with a high-resolution digital camera, assessing wounds by analyzing images of chronic foot ulcers is an attractive option. In this paper, we propose a novel wound image analysis system implemented solely on the Android smartphone. The wound image is captured by the camera on the smartphone with the assistance of an image capture box. After that, the smartphone performs wound segmentation by applying the accelerated mean-shift algorithm. Specifically, the outline of the foot is determined based on skin color, and the wound boundaryis found using a simple connected region detection method. Within the wound boundary, the healing status is next assessed based on red–yellow–black color evaluation model. Moreover, the healing status is quantitatively assessed, based on trend analysis of time records for a given patient. Experimental results on wound images collected in UMASS—Memorial Health CenterWound Clinic (Worcester,MA)following an InstitutionalReviewBoard approved protocol show that our system can be efficiently used to analyze the wound healing status with promising accuracy.

Existing system:

There are several problems with current practices for treating diabetic foot ulcers. First, patients must go to their wound clinic on a regular basis to have their wounds checked by their clinicians. This need for frequent clinical evaluation is not only inconvenient and time consuming for patients and clinicians, but also represents a significant health care cost because patients may require special transportation, e.g., ambulances. Second, a clinician’s wound assessment process is based on visual examination. He/she describes the wound by its physical dimensions and the color of its tissues, providing important indications of the wound type and the stage of healing. Because the visual assessment does not produce objective measurements and   quantifiable parameters of the healing status, tracking a wound’s healing process across consecutive visits is a difficult task for both clinicians and patients.


Technology employing image analysis techniques is a potential solution to both these problems. Several attempts have been made to use image processing techniques for such tasks, including the measurement of area, or alternatively using a volume instrument system (MAVIS) or a medical digital photogrammetric system (MEDPHOS). These approaches suffer from several drawbacks including high cost, complexity, and lack of tissue classification.

Proposed system:

To better determine the wound boundary and classify wound tissues, researchers have applied image segmentation and supervised machine learning algorithm for wound analysis. A French research group proposed a method of using a support vector machine (SVM)-based wound classification method. The same idea has also been employed for the detection of melanoma at a curable stage. Although the SVM classifier method led to good results on typical wound images, it is  not feasible to implement the training process and the feature extraction on current smartphones due to its computational demands.