27 September 2016 Thermography based diagnosis of ruptured anterior cruciate ligament (ACL) in canines
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Abstract
Anterior cruciate ligament (ACL) rupture in canines is a common orthopedic injury in veterinary medicine. Veterinarians use both imaging and non-imaging methods to diagnose the disease. Common imaging methods such as radiography, computed tomography (CT scan) and magnetic resonance imaging (MRI) have some disadvantages: expensive setup, high dose of radiation, and time-consuming. In this paper, we present an alternative diagnostic method based on feature extraction and pattern classification (FEPC) to diagnose abnormal patterns in ACL thermograms. The proposed method was experimented with a total of 30 thermograms for each camera view (anterior, lateral and posterior) including 14 disease and 16 non-disease cases provided from Long Island Veterinary Specialists. The normal and abnormal patterns in thermograms are analyzed in two steps: feature extraction and pattern classification. Texture features based on gray level co-occurrence matrices (GLCM), histogram features and spectral features are extracted from the color normalized thermograms and the computed feature vectors are applied to Nearest Neighbor (NN) classifier, K-Nearest Neighbor (KNN) classifier and Support Vector Machine (SVM) classifier with leave-one-out validation method. The algorithm gives the best classification success rate of 86.67% with a sensitivity of 85.71% and a specificity of 87.5% in ACL rupture detection using NN classifier for the lateral view and Norm-RGB-Lum color normalization method. Our results show that the proposed method has the potential to detect ACL rupture in canines.
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Norsang Lama, Norsang Lama, Scott E. Umbaugh, Scott E. Umbaugh, Deependra Mishra, Deependra Mishra, Rohini Dahal, Rohini Dahal, Dominic J. Marino, Dominic J. Marino, Joseph Sackman, Joseph Sackman, } "Thermography based diagnosis of ruptured anterior cruciate ligament (ACL) in canines", Proc. SPIE 9971, Applications of Digital Image Processing XXXIX, 99712N (27 September 2016); doi: 10.1117/12.2237462; https://doi.org/10.1117/12.2237462
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