Open AccessA Hybrid Soft-computing Method for Image Analysis of Digital Plantar Scanners
Author(s) -
Javad Razjouyan,
Omid Khayat,
Mehdi Siahi,
AliAlizadeh Mansouri
Publication year - 2013
Publication title -
journal of medical signals and sensors
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.337
H-Index - 21
ISSN - 2228-7477
DOI - 10.4103/2228-7477.114304
Subject(s) - artificial intelligence , pixel , computer science , histogram , computer vision , scanner , computation , metric (unit) , digital image , grayscale , pattern recognition (psychology) , mathematics , image processing , image (mathematics) , algorithm , operations management , economics
Digital foot scanners have been developed in recent years to yield anthropometrists digital image of insole with pressure distribution and anthropometric information. In this paper, a hybrid algorithm containing gray level spatial correlation (GLSC) histogram and Shanbag entropy is presented for analysis of scanned foot images. An evolutionary algorithm is also employed to find the optimum parameters of GLSC and transform function of the membership values. Resulting binary images as the thresholded images are undergone anthropometric measurements taking in to account the scale factor of pixel size to metric scale. The proposed method is finally applied to plantar images obtained through scanning feet of randomly selected subjects by a foot scanner system as our experimental setup described in the paper. Running computation time and the effects of GLSC parameters are investigated in the simulation results.
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