Open AccessAutomatic segmentation of plantar thermograms using adaptive C means technique
Author(s) -
S Madhava Prabhu,
Seema Verma
Publication year - 2021
Publication title -
international journal of power electronics and drive systems/international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
eISSN - 2722-2578
pISSN - 2722-256X
DOI - 10.11591/ijece.v11i2.pp1250-1258
Subject(s) - computer science , segmentation , artificial intelligence , measure (data warehouse) , pattern recognition (psychology) , thermography , image segmentation , computer vision , data mining , infrared , physics , optics
Diabetic foot ulcer (DFU) is one of the major concern of diabetes and it is rapidly increasing, in worst case scenario this may lead to amputation. The DFU can be avoided by the early detection and proper diagnosis. Many of the studies carried out highlights that, thermography is the most useful technique to measure the changes in the temperature of plantar surface and alerts 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. In this paper, the segmentation algorithm namely adaptive C means (ACM) 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.