Label Card Segmentation and Calibration for Computerized wound Measurement System

Monitoring the changes in wound size over time is one of the most important parameters for assessment of the applied treatment plan in wound care. The current manual methods for wound measurement have several drawbacks including high error rates, time consuming and also causing patient discomfort because of pain or infection. There are some computerized tools available in clinical settings known as digital planimetry which require the clinician to identify wound boundaries and calibrate them manually. However these methods are a barrier to achieving clinical quality benchmarks as the accuracy decreases drastically with improper camera lens orientation and some other factors. This paper proposes an automated calibration method for wound measurement based on the segmentation of reference label card and geometrical image operations. Due to the uncontrolled setting of the camera for our images, segmentation of the label card can be very challenging, thus preventing accurate calibration and wound size measurement. We proposed a label card segmentation method based on probability map approach and superpixel region growing. Theoretically, the white label card regions would have a distinguished probability map in the white channel compared to other regions, generating a reliable initial segmentation. Morphological operations and edge detection are then performed to obtain a more accurate final segmentation. Based on the segmented label card, the size of the pixel can be calibrated, and the size of the wound can be automatically calculated. Experimental results confirm the feasibility of the proposed method by demonstrating segmentation accuracy in terms of the Jaccard index of 94.77%, Dice coefficient of 97.20%, and contour matching score of 94.21%. The wound measurement accuracy also shows very promising results.