Thermographic image analysis as a pre-screening tool for the detection  of canine bone cancer

Samrat Subedi; Scott E. Umbaugh; Jiyuan Fu; Dominic J. Marino; Catherine A. Loughin; Joseph Sackman

doi:10.1117/12.2061233

23 September 2014 Thermographic image analysis as a pre-screening tool for the detection of canine bone cancer

Samrat Subedi, Scott E. Umbaugh, Jiyuan Fu, Dominic J. Marino, Catherine A. Loughin, Joseph Sackman

Proceedings Volume 9217, Applications of Digital Image Processing XXXVII; 92171D (2014) https://doi.org/10.1117/12.2061233
Event: SPIE Optical Engineering + Applications, 2014, San Diego, California, United States

Abstract

Canine bone cancer is a common type of cancer that grows fast and may be fatal. It usually appears in the limbs which is called "appendicular bone cancer." Diagnostic imaging methods such as X-rays, computed tomography (CT scan), and magnetic resonance imaging (MRI) are more common methods in bone cancer detection than invasive physical examination such as biopsy. These imaging methods have some disadvantages; including high expense, high dose of radiation, and keeping the patient (canine) motionless during the imaging procedures. This project study identifies the possibility of using thermographic images as a pre-screening tool for diagnosis of bone cancer in dogs. Experiments were performed with thermographic images from 40 dogs exhibiting the disease bone cancer. Experiments were performed with color normalization using temperature data provided by the Long Island Veterinary Specialists. The images were first divided into four groups according to body parts (Elbow/Knee, Full Limb, Shoulder/Hip and Wrist). Each of the groups was then further divided into three sub-groups according to views (Anterior, Lateral and Posterior). Thermographic pattern of normal and abnormal dogs were analyzed using feature extraction and pattern classification tools. Texture features, spectral feature and histogram features were extracted from the thermograms and were used for pattern classification. The best classification success rate in canine bone cancer detection is 90% with sensitivity of 100% and specificity of 80% produced by anterior view of full-limb region with nearest neighbor classification method and normRGB-lum color normalization method. Our results show that it is possible to use thermographic imaging as a pre-screening tool for detection of canine bone cancer.

Citation Download Citation

Samrat Subedi, Scott E. Umbaugh, Jiyuan Fu, Dominic J. Marino, Catherine A. Loughin, and Joseph Sackman "Thermographic image analysis as a pre-screening tool for the detection of canine bone cancer", Proc. SPIE 9217, Applications of Digital Image Processing XXXVII, 92171D (23 September 2014); https://doi.org/10.1117/12.2061233

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