Mr. Lei Wang Profile

Lei Wang

at Worcester Polytechnic Institute

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Publications (2)

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SPIE Journal Paper | 21 April 2019

Boundary determination of foot ulcer images by applying the associative hierarchical random field framework

Lei Wang, Peder Pedersen, Emmanuel Agu, Diane Strong, Bengisu Tulu

JMI, Vol. 6, Issue 02, 024002, (April 2019) https://doi.org/10.1117/12.10.1117/1.JMI.6.2.024002

KEYWORDS: Image segmentation, Data modeling, Detection and tracking algorithms, Light sources and illumination, Wound healing, Machine vision, Image analysis, Computer vision technology, Visual process modeling, Image processing

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As traditional visual-examination-based methods provide neither reliable nor consistent wound assessment, several computer-based approaches for quantitative wound image analysis have been proposed in recent years. However, these methods require either some level of human interaction for proper image processing or that images be captured under controlled conditions. However, to become a practical tool of diabetic patients for wound management, the wound image algorithm needs to be able to correctly locate and detect the wound boundary of images acquired under less-constrained conditions, where the illumination and camera angle can vary within reasonable bounds. We present a wound boundary determination method that is robust to lighting and camera orientation perturbations by applying the associative hierarchical random field (AHRF) framework, which is an improved conditional random field (CRF) model originally applied to natural image multiscale analysis. To validate the robustness of the AHRF framework for wound boundary recognition tasks, we have tested the method on two image datasets: (1) foot and leg ulcer images (for the patients we have tracked for 2 years) that were captured under one of the two conditions, such that 70% of the entire dataset are captured with image capture box to ensure consistent lighting and range and the remaining 30% of the images are captured by a handheld camera under varied conditions of lighting, incident angle, and range and (2) moulage wound images that were captured under similarly varied conditions. Compared to other CRF-based machine learning strategies, our new method provides a determination accuracy with the best global performance rates (specificity: >95 % and sensitivity: >77 % .

Proceedings Article | 13 March 2013 Paper

Wound image analysis system for diabetics

Lei Wang, Peder Pedersen, Diane Strong, Bengisu Tulu, Emmanuel Agu

Proceedings Volume 8669, 866924 (2013) https://doi.org/10.1117/12.2004762

KEYWORDS: Image segmentation, Image processing, Wound healing, Image processing algorithms and systems, Image analysis, Tissues, Detection and tracking algorithms, Cameras, Skin, Telecommunications

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Diabetic foot ulcers represent a significant health issue, and daily wound care is necessary for wound healing to occur. The goal of this research is to create a smart phone based wound image analysis system for people with diabetes to track the healing process of chronic ulcers and wounds. This system has been implemented on an Android smart phone in collaboration with a PC (or embedded PC). The wound image is captured by the smart phone camera and transmitted to the PC via Wi-Fi for image processing. The PC converts the JPEG image to bitmap format, then performs boundary segmentation on the wound in the image. The segmentation is done with a particular implementation of the level set algorithm, the distance regularized level set evolution (DRLSE) method, which eliminates the need for re-initialization of level set function. Next, an assessment of the wound healing is performed with color segmentation within the boundaries of the wound image, by applying the K-Mean color clustering algorithm based on the red-yellow-black (RYB) evaluation model. Finally, the results are re-formatted to JPEG, transmitted back to the smart phone and displayed. To accelerate the wound image segmentation, we have implemented the DRLSE method on the GPU and CPU cooperative hardware platform in data-parallel mode, which has greatly improved the computational efficiency. Processing wound images acquired from UMASS Medical Center has demonstrated that the wound image analysis system provides accurate wounds area determination and color segmentation. For all wound images of size around 640 x 480, with complicated wound boundaries, the wound analysis consumed max 3s, which is 5 times faster than the same algorithm running on the CPU alone.

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