Fuzzy Texture Descriptors for Early Diagnosis of Osteoarthritis

被引:2
|
作者
Chetty, Girija [1 ]
Scarvell, Jennie [2 ]
Mitra, Sushmita [3 ]
机构
[1] Univ Canberra, Fac ESTeM, IT&E, Canberra, ACT 2601, Australia
[2] Univ Canberra, Fac Hlth, Physioltherapy Discipline, Canberra, ACT, Australia
[3] Indian Stat Inst, Machine Intelligence Unit, Kolkata, India
关键词
Texture Descriptors; Image Analysis; Fuzzy Logic; Osteoarthritis; T2; RELAXATION-TIME; ARTICULAR-CARTILAGE; KNEE; SEVERITY;
D O I
10.1109/FUZZ-IEEE.2013.6622439
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Knee osteoarthritis (OA) is a debilitating health condition affecting elderly population. Early diagnosis of the disease noninvasively will be very useful, and requires novel image analysis techniques to be developed for processing radiological and MRI scans of knee joints. In this paper, we propose novel micro texture based feature descriptors for modeling subtle variations in MRI T2 maps, so as to facilitate early detection of osteoarthritis. The experimental evaluation of the proposed micro texture descriptors on a publicly available OAI database show, that the proposed features allow a significant improvement in discriminating the textures for MRI T2 maps, corresponding to normal subjects as compared to the subjects with risk factors for developing osteoarthritis.
引用
收藏
页数:6
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