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Proceedings Paper

Comparative study of local binary pattern and its shifted variant for osteoporosis identification
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Paper Abstract

Osteoporosis is an age-based disease causing skeletal disorder. It is described by the little bone mass and weakening of the bone structure thereby resulting in the higher fracture risks. Early identification can help prevent the disease and successfully predict the fracture risks. Automated diagnosis of osteoporosis using X-ray image is a very challenging task because the radiographs from the healthy subjects and osteoporotic cases show a high grade of resemblance. This study presents an evaluation of osteoporosis identification using texture descriptor Local Binary Pattern (LBP) and Shift Local Binary Pattern (SLBP). In contrast with the conventional LBP, with the shifted LBP specific number of binary local codes are generated for each pixel place. The distinguishing ability of the texture descriptors is evaluated using ten-fold cross validation and leave-one out scheme using different machine learning techniques. The results prove the SLBP outperforms the traditional LBP for bone texture characterization.

Paper Details

Date Published: 27 April 2018
PDF: 7 pages
Proc. SPIE 10649, Pattern Recognition and Tracking XXIX, 1064908 (27 April 2018); doi: 10.1117/12.2304721
Show Author Affiliations
Hina Ajmal, National Univ. of Sciences and Technology (Pakistan)
Saad Rehman, National Univ. of Sciences and Technology (Pakistan)
Farhan Hussain, National Univ. of Sciences and Technology (Pakistan)
Muhammad Abbas, National Univ. of Sciences and Technology (Pakistan)
Aimal Khan, National Univ. of Sciences and Technology (Pakistan)
Rupert Young, Univ. of Sussex (United Kingdom)
Mohammad S. Alam, Texas A&M Univ.-Kingsville (United States)

Published in SPIE Proceedings Vol. 10649:
Pattern Recognition and Tracking XXIX
Mohammad S. Alam, Editor(s)

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