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Proceedings Paper

Thermographic image analysis as a pre-screening tool for the detection of canine bone cancer
Author(s): Samrat Subedi; Scott E. Umbaugh; Jiyuan Fu; Dominic J. Marino; Catherine A. Loughin; Joseph Sackman
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Paper 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.

Paper Details

Date Published: 23 September 2014
PDF: 8 pages
Proc. SPIE 9217, Applications of Digital Image Processing XXXVII, 92171D (23 September 2014); doi: 10.1117/12.2061233
Show Author Affiliations
Samrat Subedi, Southern Illinois Univ. Edwardsville (United States)
Scott E. Umbaugh, Southern Illinois Univ. Edwardsville (United States)
Jiyuan Fu, Southern Illinois Univ. Edwardsville (United States)
Dominic J. Marino, Long Island Veterinary Specialists (United States)
Catherine A. Loughin, Long Island Veterinary Specialists (United States)
Joseph Sackman, Long Island Veterinary Specialists (United States)


Published in SPIE Proceedings Vol. 9217:
Applications of Digital Image Processing XXXVII
Andrew G. Tescher, Editor(s)

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