DocumentCode
1013566
Title
The use of arch index to characterize arch height: a digital image processing approach
Author
Chyn Chu, Woei ; Hwa Lee, Shin ; Chu, William ; Wang, Tzyy-Jiuan ; Lee, Maw-Chang
Author_Institution
Inst. of Biomed. Eng., Nat. Yang-Ming Univ., Taipei, Taiwan
Volume
42
Issue
11
fYear
1995
Firstpage
1088
Lastpage
1093
Abstract
Attempts to evaluate foot arch types from footprint parameters have yielded conflicting results in the past. This could be caused by the uncertainty inherent in the definition of some footprint parameters and the inaccuracy during the footprint acquisition and the parameter calculation phases of the traditional methods. In order to avoid these problems, digital image processing methods were used to acquire and to calculate the Arch Index (AI), a parameter which is robust in its definition. A significant correlation (r=-0.70, p<0.0001) was found between AI and arch height. Therefore this study confirms that foot arch type does correlate with the footprint parameter, AI. This was further revealed by a new parameter, the modified arch index (MAI), which incorporates foot pressure information in the evaluation. MAI not only correlated well with arch height (r=-0.71, p<0.0001) but appeared to characterize abnormal foot types better than AI.
Keywords
biomedical measurement; height measurement; medical image processing; abnormal foot types characterization; arch index; foot arch types evaluation; foot pressure information; footprint acquisition inaccuracy; footprint parameters; medical diagnostic imaging; video images analysis; Artificial intelligence; Biomedical measurements; Councils; Data mining; Digital images; Foot; Image processing; Machine assisted indexing; Pathology; Uncertainty; Adult; Anthropometry; Bias (Epidemiology); Female; Foot; Humans; Image Processing, Computer-Assisted; Linear Models; Male; Pressure; Reference Values; Reproducibility of Results; Videotape Recording;
fLanguage
English
Journal_Title
Biomedical Engineering, IEEE Transactions on
Publisher
ieee
ISSN
0018-9294
Type
jour
DOI
10.1109/10.469375
Filename
469375
Link To Document