engleski

Detection, Reconstruction and Segmentation of Chronic Wounds Using Kinect v2 Sensor

The advent of inexpensive RGB-D sensors pioneered by the original Kinect sensor, has paved the way for a lot of innovations in computer and robot vision applications. In this article, we propose a system which uses the new Kinect v2 sensor in a medical application for the purpose of detection, 3D reconstruction and segmentation of chronic wounds. Wound detection is based on a per block classification of wound tissue using colour histograms and nearest neighbour approach. The 3D reconstruction is similar to KinectFusion where ICP is used for determining rigid body transformation. Colour enhanced TSDF is applied for scene fusion, while the Marching cubes algorithm is used for creating the surface mesh. The wound contour is extracted by a segmentation procedure which is driven by geometrical and visual properties of the surface. Apart from the segmentation procedure, the entire system is implemented in CUDA which enables real-time operation. The end result of the developed system is a precise 3D coloured model of the segmented wound, and its measurable properties including perimeter, area and volume, which can be used for determining a correct therapy and treatment of chronic wounds. All experiments were conducted on a medical wound care model.

chronic wound ; detection ; reconstruction ; segmentation ; measurement ; kinect v2

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