Statistical Approximation of Plantar Temperature Distribution on Diabetic Subjects Based on Beta Mixture Model

A change in plantar temperature distribution can be an indicator of tissue damage, inflammation, or peripheral vascular abnormalities associated with diabetic foot. Despite the efforts to detect these abnormalities through infrared thermography, there are still several problems to be addressed, especially to detect abnormalities on each foot separately. In this paper, a characterization of the plantar temperature distribution based on a probabilistic approach is proposed. The objective is to detect temperature variations on each foot eluding contralateral comparison. A beta mixture model with four components approximates the plantar temperature distributions of diabetic and non-diabetic subjects. Each component represents an area of the plantar region: toes; metatarsal heads; arch; and heel. The approximation was applied to 60 temperature distributions of non-diabetic subjects and 220 of diabetic subjects. The results suggest that it is possible to characterize distribution in terms of the mean of its beta components.

[1]  U. Rajendra Acharya,et al.  Infrared thermography on ocular surface temperature: A review , 2009 .

[2]  C. Mathers,et al.  Projections of Global Mortality and Burden of Disease from 2002 to 2030 , 2006, PLoS medicine.

[3]  D. Hernandez-Contreras,et al.  Automatic classification of thermal patterns in diabetic foot based on morphological pattern spectrum , 2015 .

[4]  D. Armstrong,et al.  A Supplement to : Foot & Ankle Surgery , 2006 .

[5]  B. Martin PARAMETER ESTIMATION , 2012, Statistical Methods for Biomedical Research.

[6]  Benjamin A Lipsky,et al.  Prevention and management of foot problems in diabetes: a Summary Guidance for Daily Practice 2015, based on the IWGDF Guidance Documents , 2016, Diabetes/metabolism research and reviews.

[7]  D. Bowsher,et al.  Contact Thermography of Painful Diabetic Neuropathic Foot , 1991, Diabetes Care.

[8]  Hayde Peregrina-Barreto,et al.  Measuring changes in the plantar temperature distribution in diabetic patients , 2017, 2017 IEEE International Instrumentation and Measurement Technology Conference (I2MTC).

[9]  Naima Kaabouch,et al.  Alternative Technique to Asymmetry Analysis-Based Overlapping for Foot Ulcer Examination: Scalable Scanning , 2016, ArXiv.

[10]  Ferdi van der Heijden,et al.  Automatic detection of diabetic foot complications with infrared thermography by asymmetric analysis , 2015, Journal of biomedical optics.

[11]  J. Oliveira,et al.  Use of infrared thermography for the diagnosis and grading of sprained ankle injuries , 2016 .

[12]  C. Clark,et al.  Prevention and treatment of the complications of diabetes mellitus. , 1995, The New England journal of medicine.

[13]  Hayde Peregrina-Barreto,et al.  Quantitative Estimation of Temperature Variations in Plantar Angiosomes: A Study Case for Diabetic Foot , 2014, Comput. Math. Methods Medicine.

[14]  G. McLachlan,et al.  Extensions of the EM Algorithm , 2007 .

[15]  A. Veves,et al.  The risk of foot ulceration in diabetic patients with high foot pressure: a prospective study , 1992, Diabetologia.

[16]  Mohamed Bouguessa,et al.  An Unsupervised Approach for Identifying Spammers in Social Networks , 2011, 2011 IEEE 23rd International Conference on Tools with Artificial Intelligence.

[17]  D. Hernandez-Contreras,et al.  Narrative review: Diabetic foot and infrared thermography , 2016 .

[18]  Arne Leijon,et al.  Beta mixture models and the application to image classification , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[19]  U. Rajendra Acharya,et al.  Computer aided diagnosis of diabetic foot using infrared thermography: A review , 2017, Comput. Biol. Medicine.

[20]  Francisco-J Renero-C,et al.  The thermoregulation of healthy individuals, overweight–obese, and diabetic from the plantar skin thermogram: a clue to predict the diabetic foot , 2017, Diabetic foot & ankle.

[21]  O. Hänninen,et al.  Plantar infrared thermography measurements and low back pain intensity. , 2006, Journal of manipulative and physiological therapeutics.

[22]  Gojiro Nakagami,et al.  Variations of plantar thermographic patterns in normal controls and non-ulcer diabetic patients: novel classification using angiosome concept. , 2011, Journal of plastic, reconstructive & aesthetic surgery : JPRAS.

[23]  Rachid Harba,et al.  Automatic Analysis of Plantar Foot Thermal Images in at-Risk Type II Diabetes by Using an Infrared Camera , 2015 .

[24]  Hiroshi Noguchi,et al.  Morphological Pattern Classification System for Plantar Thermography of Patients with Diabetes , 2013, Journal of diabetes science and technology.

[25]  E. Y.-K. Ng,et al.  A review of thermography as promising non-invasive detection modality for breast tumor , 2009 .

[26]  Christopher M. Bishop,et al.  Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .