In this study, we propose an automated otoscopy image analysis system called Autoscope. To the best of our knowledge, Autoscope is the first system designed to detect a wide range of eardrum abnormalities by using high-resolution otoscope images and report the condition of the eardrum as “normal” or “abnormal.” In order to achieve this goal, first, we developed a preprocessing step to reduce camera-specific problems, detect the region of interest in the image, and prepare the image for further analysis. Subsequently, we designed a new set of clinically motivated eardrum features (CMEF). Furthermore, we evaluated the potential of the visual MPEG-7 descriptors for the task of tympanic membrane image classification. Then, we fused the information extracted from the CMEF and state-of-the-art computer vision features (CVF), which included MPEG-7 descriptors and two additional features together, using a state of the art classifier. In our experiments, 247 tympanic membrane images with 14 different types of abnormality were used, and Autoscope was able to classify the given tympanic membrane images as normal or abnormal with 84.6% accuracy.
Caglar Senaras, Aaron C. Moberly, Theodoros Teknos, Garth Essig, Charles Elmaraghy, Nazhat Taj-Schaal, Lianbo Yu, and Metin Gurcan, "Autoscope: automated otoscopy image analysis to diagnose ear pathology and use of clinically motivated eardrum features," Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 101341X (Presented at SPIE Medical Imaging: February 16, 2017; Published: 3 March 2017); https://doi.org/10.1117/12.2250592.
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