Automatic segmentation of plantar thermograms using adaptive C means technique

DFU (Diabetic Foot Ulcer) is one of the major concern of diabetes and it is rapidly increasing, in worst case scenario this may lead to amputation. However, in several cases the DFU can be avoided by the early detection and proper diagnosis. Many of the studies carried out on thermography highlights that, is the most useful technique to measure the changes in the temperature of plantar surface and act as an alert to indicate the risk associated with DFU. The distribution of temperature does not have a fixed pattern across the patients, hence it makes the difficulty in measuring the appropriate changes. This gap will provide a scope to improve the analysis technique so as to measure the plantar surface temperature effectively and identify any abnormal changes. The process used to detect the DFU are pre-processing, segmentation and feature extraction. The segmentation is one of the challenging task. In this paper, the segmentation algorithm namely ACM (Adaptive C means) for the image segmentation is discussed. ACM is based on the spatial information and this method includes the two stage implementation. In the first stage, nonlocal spatial information is added and in the second stage, spatial shape information is used in order to refine the constraint of local spatial. Outcome of the proposed method shows that ACM is very much effective and it outperforms the other existing methods.