Thermography and pattern classification techniques are used to classify three different pathologies in veterinary images. Thermographic images of both normal and diseased animals were provided by the Long Island Veterinary Specialists (LIVS). The three pathologies are ACL rupture disease, bone cancer, and feline hyperthyroid. The diagnosis of these diseases usually involves radiology and laboratory tests while the method that we propose uses thermographic images and image analysis techniques and is intended for use as a prescreening tool. Images in each category of pathologies are first filtered by Gabor filters and then various features are extracted and used for classification into normal and abnormal classes. Gabor filters are linear filters that can be characterized by the two parameters wavelength λ and orientation θ. With two different wavelength and five different orientations, a total of ten different filters were studied. Different combinations of camera views, filters, feature vectors, normalization methods, and classification methods, produce different tests that were examined and the sensitivity, specificity and success rate for each test were produced. Using the Gabor features alone, sensitivity, specificity, and overall success rates of 85% for each of the pathologies was achieved.
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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