An Application for Skin Macules Characterization Based on a 3-Stage Image-Processing Algorithm for Patients with Diabetes

• Published in: Journal of healthcare engineering 2018-01, Vol.2018 (2018), p.1-13
• Main Authors: Leija, L., Vera, A., Vega Martínez, Gabriel, Ramos Becerril, Francisco José, Toledo-Peral, Cinthya Lourdes, Gutiérrez-Martínez, Josefina
• Format: Article
• Language: English
• Subjects: Algorithms; Care and treatment; Color; Computer Graphics; Diabetes Complications - diagnostic imaging; Diabetes Complications - pathology; Diabetes Mellitus, Type 2 - complications; Diabetes Mellitus, Type 2 - diagnostic imaging; Diabetic foot; Diabetic Foot - complications; Diabetics; Humans; Image processing; Image Processing, Computer-Assisted - methods; Leg - diagnostic imaging; Leg - pathology; Neural Networks, Computer; Photography; Pigmentation Disorders - diagnostic imaging; Pigmentation Disorders - pathology; Principal Component Analysis; Purpura - pathology; Skin; Skin - diagnostic imaging; Skin - pathology; Skin Abnormalities - diagnostic imaging; Skin Pigmentation; Software; User-Computer Interface
• ISSN: 2040-2295; 2040-2309
• DOI: 10.1155/2018/9397105

Diabetic skin manifestations, previous to ulcers and wounds, are not highly accounted as part of diagnosis even when they represent the first symptom of vascular damage and are present in up to 70% of patients with diabetes mellitus type II. Here, an application for skin macules characterization based on a three-stage segmentation and characterization algorithm used to classify vascular, petechiae, trophic changes, and trauma macules from digital photographs of the lower limbs is presented. First, in order to find the skin region, a logical multiplication is performed on two skin masks obtained from color space transformations; dynamic thresholds are stabilised to self-adjust to a variety of skin tones. Then, in order to locate the lesion region, illumination enhancement is performed using a chromatic model color space, followed by a principal component analysis gray-scale transformation. Finally, characteristics of each type of macule are considered and classified; morphologic properties (area, axes, perimeter, and solidity), intensity properties, and a set of shade indices (red, green, blue, and brown) are proposed as a measure to obviate skin color differences among subjects. The values calculated show differences between macules with a statistical significance, which agree with the physician’s diagnosis. Later, macule properties are fed to an artificial neural network classifier, which proved a 97.5% accuracy, to differentiate between them. Characterization is useful in order to track macule changes and development along time, provides meaningful information to provide early treatments, and offers support in the prevention of amputations due to diabetic feet. A graphical user interface was designed to show the properties of the macules; this application could be the background of a future Diagnosis Assistance Tool for educational (i.e., untrained physicians) and preventive assistance technology purposes.