Segmentation of Thermal Images using Adaptive C means (ACM) technique for diabetic feet

Madhava Prabhu


DFU (Diabetic Foot Ulcer) is one of the major concern and it is rapidly increasing, in worst case scenario this may lead for amputation. However, in several cases the DFU can be avoided by early detection and diagnosis. Several detection technique has been presented in order to detect the DFU. However, the detection fails to provide the absolute detection. The process used to detect the DFU is pre-processing, segmentation and feature extraction, segmentation is one of the challenging task. Hence, in this paper, we have presented the segmentation algorithm namely ACM (Adaptive C means) clustering for the image segmentation. ACM is based on the spatial information and this method includes the two stage.  In first stage nonlocal spatial information is added, in second stage spatial shape information is used in order to refine the constraint of local spatial and this in terms helps in refining the local spatial constraint. Outcome of the proposed method shows that ACM is very much effective and outperforms other existing method.


Thermography; Segmentation; Diabetes; Adaptive; Image Processing


S. M. Prabhu and S. Verma, “Comparative Analysis of Segmentation techniques for Progressive Evaluation and Risk Identification of Diabetic Foot Ulcers,” 2019 4th MEC International Conference on Big Data and Smart City (ICBDSC), 2019.

K. G. Osgouie and A. Azizi, "Optimizing fuzzy logic controller for diabetes type I by genetic algorithm," 2010 The 2nd International Conference on Computer and Automation Engineering (ICCAE), Singapore, 2010, pp. 4-8.

Ning Wang and Guixia Kang, "A monitoring system for type 2 diabetes mellitus," 2012 IEEE 14th International Conference on e-Health Networking, Applications and Services (Healthcom), Beijing, 2012, pp. 62-67.

S. M. Prabhu and S. Verma, “A Systematic Literature Review for Early Detection of Type II Diabetes,” 2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS), 2019.

S. Rahaman, "Diabetes diagnosis decision support system based on symptoms, signs and risk factor using special computational algorithm by rule base," 2012 15th International Conference on Computer and Information Technology (ICCIT), Chittagong, 2012, pp. 65-71.

M. Adam, E. Y. Ng, J. H. Tan, M. L. Heng, J. W. Tong, and U. R. Acharya, “Computer aided diagnosis of diabetic foot using infrared thermography: A review,” Computers in Biology and Medicine, vol. 91, pp. 326–336, 2017.

T. Bernard, C. D'Elia, R. Kabadi and N. Wong, "An early detection system for foot ulceration in diabetic patients," 2009 IEEE 35th Annual Northeast Bioengineering Conference, Boston, MA, 2009, pp. 1-2.

M. Shikano et al., "Infrared thermography and diabetic foot," 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Istanbul, Turkey, 2001, pp. 2873 vol.3.

M.J. McNeely, E.J. Boyko, J.H. Ahroni, V.L. Stensel, G.E. Reiber, D.G. Smith, et al., The independent contributions of diabetic neuropathy and yasculopatny in foot ulceration: how great are the risks? Diabetes care 18 (2) (1995) 216–219.

H. C. Pereira, P. do Mar and C. Correia, "Metabolic.Care: Development and characterization of a new thermographic platform for diabetic foot detection," 2015 IEEE 4th Portuguese Meeting on Bioengineering (ENBENG), Porto, 2015, pp. 1-1.

L. Vilcahuaman et al., "Detection of diabetic foot hyperthermia by infrared imaging," 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Chicago, IL, 2014, pp. 4831-4834.

Do Hyun Chung and G. Sapiro, "Segmenting skin lesions with partial differential equations based image processing algorithms," Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101), Vancouver, BC, Canada, 2000, pp. 404-407 vol.3.

E. S. Papazoglou, L. Zubkov, X. Mao, M. Neidrauer, N. Rannou, and M. S. Weingarten, “Image analysis of chronic wounds for determining the surface area,” Wound repair and regeneration, vol. 18, no. 4, pp. 349–358, 2010.

F. Veredas, H. Mesa, and L. Morente, “Binary tissue classification on wound images with neural networks and bayesian classifiers,” IEEE transactions on medical imaging, vol. 29, no. 2, pp. 410–427, 2010.

Castro, C. B´oveda, and B. Arcay, “Analysis of fuzzy clustering algorithms for the segmentation of burn wounds photographs,” in International Conference Image Analysis and Recognition. Springer, 2006, pp. 491–501.

I. T. Kokten, Y. Artan, and H. R. Ozakpinar, “Diabetic foot ulcer detection using interactive image segmentation techniques,” 2016 20th National Biomedical Engineering Meeting (BIYOMUT), 2016.

C. Eswaran, M. D. Saleh and J. Abdullah, "Projection based algorithm for detecting exudates in color fundus images," 2014 19th International Conference on Digital Signal Processing, Hong Kong, 2014, pp. 459-463.

P. Anupama and S. Nandyal, "Blood vessel segmentation using hessian matrix for diabetic retinopathy detection," 2017 Second International Conference on Electrical, Computer and Communication Technologies.

G. L. Nandagopan and A. B. Haripriya, "Implementation and comparison of two image segmentation techniques on thermal foot images and detection of ulceration using asymmetry," 2016 International Conference on Communication and Signal Processing (ICCSP), Melmaruvathur, 2016, pp. 0356-0360.

S. Patel, R. Patel and D. Desai, "Diabetic foot ulcer wound tissue detection and classification," 2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), Coimbatore, 2017, pp. 1-5.

M. Goyal, N. D. Reeves, A. K. Davison, S. Rajbhandari, J. Spragg and M. H. Yap, "DFUNet: Convolutional Neural Networks for Diabetic Foot Ulcer Classification," in IEEE Transactions on Emerging Topics in Computational Intelligence.

J. M. Román, J. V. Noguera, H. Legal-Ayala, D. Pinto-Roa, S. Gomez-Guerrero, and M. G. Torres, “Entropy and Contrast Enhancement of Infrared Thermal Images Using the Multiscale Top-Hat Transform,” Entropy, vol. 21, no. 3, p. 244, Apr. 2019.

Z. Qu and Z.-Y. Wang, “Research on preprocessing of palmprint image based on adaptive threshold and Euclidian distance,” 2010 Sixth International Conference on Natural Computation, 2010.

M. J. Garbade, “Understanding K-means Clustering in Machine Learning,” Medium, 12-Aug-2019. [Online]. Available:

Mahnaz Etehadtavakol, E.Y.K. Ng, Naima Kaabouch, “Automatic segmentation of thermal images of diabetic-at-risk feet using the snakes algorithm”, Infrared Physics & Technology,Volume 86,2017,Pages 66-76.

Total views : 0 times

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

ISSN 2088-8708, e-ISSN 2722-2578