Thermography based prescreening software tool for veterinary clinics

Rohini Dahal; Scott E. Umbaugh; Deependra Mishra; Norsang Lama; Mehrdad Alvandipour; David Umbaugh; Dominic J. Marino; Joseph Sackman

doi:10.1117/12.2260758

5 May 2017 Thermography based prescreening software tool for veterinary clinics

Rohini Dahal, Scott E. Umbaugh, Deependra Mishra, Norsang Lama, Mehrdad Alvandipour, David Umbaugh, Dominic J. Marino, Joseph Sackman

Author Affiliations +

Proceedings Volume 10214, Thermosense: Thermal Infrared Applications XXXIX; 102141A (2017) https://doi.org/10.1117/12.2260758
Event: SPIE Commercial + Scientific Sensing and Imaging, 2017, Anaheim, CA, United States

Abstract

Under development is a clinical software tool which can be used in the veterinary clinics as a prescreening tool for these pathologies: anterior cruciate ligament (ACL) disease, bone cancer and feline hyperthyroidism. Currently, veterinary clinical practice uses several imaging techniques including radiology, computed tomography (CT), and magnetic resonance imaging (MRI). But, harmful radiation involved during imaging, expensive equipment setup, excessive time consumption and the need for a cooperative patient during imaging, are major drawbacks of these techniques. In veterinary procedures, it is very difficult for animals to remain still for the time periods necessary for standard imaging without resorting to sedation – which creates another set of complexities. Therefore, clinical application software integrated with a thermal imaging system and the algorithms with high sensitivity and specificity for these pathologies, can address the major drawbacks of the existing imaging techniques. A graphical user interface (GUI) has been created to allow ease of use for the clinical technician. The technician inputs an image, enters patient information, and selects the camera view associated with the image and the pathology to be diagnosed. The software will classify the image using an optimized classification algorithm that has been developed through thousands of experiments. Optimal image features are extracted and the feature vector is then used in conjunction with the stored image database for classification. Classification success rates as high as 88% for bone cancer, 75% for ACL and 90% for feline hyperthyroidism have been achieved. The software is currently undergoing preliminary clinical testing.

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Rohini Dahal, Scott E. Umbaugh, Deependra Mishra, Norsang Lama, Mehrdad Alvandipour, David Umbaugh, Dominic J. Marino, and Joseph Sackman "Thermography based prescreening software tool for veterinary clinics", Proc. SPIE 10214, Thermosense: Thermal Infrared Applications XXXIX, 102141A (5 May 2017); https://doi.org/10.1117/12.2260758

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KEYWORDS

Cancer

Algorithm development

Image classification

Bone

Pathology

Databases

Thermography

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