Share Email Print

Proceedings Paper

Autoscope: automated otoscopy image analysis to diagnose ear pathology and use of clinically motivated eardrum features
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

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.

Paper Details

Date Published: 3 March 2017
PDF: 8 pages
Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 101341X (3 March 2017); doi: 10.1117/12.2250592
Show Author Affiliations
Caglar Senaras, The Ohio State Univ. (United States)
Aaron C. Moberly, The Ohio State Univ. (United States)
Theodoros Teknos, The Ohio State Univ. (United States)
Garth Essig, The Ohio State Univ. (United States)
Charles Elmaraghy, The Ohio State Univ. (United States)
Nazhat Taj-Schaal, The Ohio State Univ. College of Medicine (United States)
Lianbo Yu, The Ohio State Univ. (United States)
Metin Gurcan, The Ohio State Univ. (United States)

Published in SPIE Proceedings Vol. 10134:
Medical Imaging 2017: Computer-Aided Diagnosis
Samuel G. Armato III; Nicholas A. Petrick, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?