Smartphone-Based Wound Assessment System for Patients With Diabetes

ABSTRACT:

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 boundary is 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 Center Wound Clinic (Worcester, MA)following an Institutional Review Board 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.

·      The wound boundary determination was done with a particular implementation of the level set algorithm; specifically the distance regularized level set evolution The principal disadvantage of the level set algorithm is that the iteration of global level set function is too computationally intensive to be implemented on smart phones, even with the narrow band confined implementation based on GPUs.

·      In addition, the level set evolution completely depends on the initial curve which has to be pre-delineated either manually or by a well-designed algorithm. Finally, false edges may interfere with the evolution when the skin color is not uniform enough and when missing boundaries, as frequently occurring in medical images, results in evolution leakage (the level set evolution does not stop properly on the actual wound boundary). Hence, a better method was required to solve these problems.

DISADVANTAGES OF EXISTING SYSTEM:

·      Patient has to travel with foot ulcers to their clinics to report about the ulcers. This may increase the seriousness of the ulcers instead of curing it.

·      Patient travel exposure may cause a serious problem for them.

PROPOSED SYSTEM:

·      In this paper, we replaced the level set algorithms with the efficient mean-shift segmentation algorithm.

·      While it addresses the previous problems, it also creates additional challenges, such as over-segmentation, which we solved using the region adjacency graph (RAG)-based region merge algorithm.

·      In this paper, we present the entire process of recording and analyzing a wound image, using algorithms that are executable on a smart phone, and provide evidence of the efficiency and accuracy of these algorithms for analyzing diabetic foot ulcers.

ADVANTAGES OF PROPOSED SYSTEM:

·      Patient’s travel exposure is considerably reduced. Also it will reduce the patients stress.

·      Doctor can easily analyze the problem through images and its segmentation. So the proper report can be given to the patient on time

SYSTEM ARCHITECTURE:

MODULES:

1.    Wound Image Analysis System overview.

2.    Mean-Shift-Based Segmentation Algorithm.

3.    Wound Boundary Determination and Analysis Algorithms.

MODULE DESCRIPTION:

Wound Image Analysis System overview:

In this module, we carry out a Wound boundary determination based on the foot outline detection result. If the foot detection result is regarded as a binary image with the foot area marked as “white” and rest part marked as “black,” it is easy to locate the wound  boundary within the foot region boundary by detecting the largest connected  black” component within the “white”  part. If the wound is located at the foot region boundary, then the foot boundary is not closed, and hence the problem becomes more complicated, i.e., we might need to first form a closed boundary. When the wound boundary has been successfully determined and the wound area calculated, we next evaluate the healing state of the wound by performing Color segmentation, with the goal of categorizing each pixel in the wound boundary into certain classes labeled as granulation, slough and necrosis. The classical self-organized clustering method called K-mean with high computational efficiency is used. After the color segmentation, a feature vector including the wound area size and dimensions for different types of wound tissues is formed to describe the wound quantitatively. This feature vector, along with both the original and analyzed images, is saved in the result database. The Wound healing trend analysis is performed on a time sequence of images belonging to a given patient to monitor the wound healing status. The current trend is obtained by comparing the wound feature vectors between the current wound record and the one that is just one standard time interval earlier (typically one or two weeks). Alternatively, a longer term healing trend is obtained by comparing the feature vectors between the current wound and the base record which is the earliest record for this patient.

Mean-Shift-Based Segmentation Algorithm:

In this module we implement mean-shift-based segmentation, the mean-shift algorithm belongs to the density estimation based nonparametric clustering methods, in which the feature space can be considered as the empirical probability density function of the represented parameter. This type of algorithms adequately analyzes the image feature space (color space, spatial space or the combination of the two spaces) to cluster and can provide a reliable solution for many vision tasks.

Wound Boundary Determination and Analysis Algorithms:

In this module we implement wound boundary determination, because the mean-shift algorithm only manages to segment the original image into homogeneous regions with similar color features, an object recognition method is needed to interpret the segmentation result into a meaningful wound boundary determination that can be easily understood by the users of the wound analysis system. As noted, a standard recognition method relies on known model information to develop a hypothesis, based on which a decision is made whether a region should be regarded as a candidate object, i.e., a wound. A verification step is also needed for further confirmation. Because our wound determination algorithm is designed for real time implementation on the smart phones with limited computational resources, we simplify the object recognition process while ensuring that recognition accuracy is acceptable.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS:

·      System :         Pentium IV 2.4 GHz.

·      Hard Disk :         40 GB.

·      Floppy Drive : 44 Mb.

·      Monitor : 15 VGA Colour.

·      Mouse :

·      Ram : 512 Mb.

·      MOBILE : ANDROID

SOFTWARE REQUIREMENTS:

·      Operating system : Windows 7.

·      Coding Language : Java 1.7

·      Tool Kit : Android 2.3 ABOVE

·      IDE : Eclipse