Dr. Aaron C. Moberly Profile

Dr. Aaron C. Moberly

at Vanderbilt Univ. Medical Ctr.

SPIE Involvement:

Author

Publications (5)

Proceedings Article | 3 April 2024 Poster + Paper

Tympanic membrane segmentation of video frames to create composite images using SAM

Seda Camalan, Muhammad Khalid Khan Niazi, Charles Elmaraghy, Aaron Moberly, Metin Gurcan

Proceedings Volume 12927, 1292736 (2024) https://doi.org/10.1117/12.3006926

KEYWORDS: Image segmentation, Video, Ear, Education and training, Diagnostics, Visual process modeling, Transformers, Semantics, Diseases and disorders, Data modeling

Read Abstract +

Proceedings Article | 15 February 2021 Presentation + Paper

Automated video summarization and label assignment for otoscopy videos using deep learning and natural language processing

Hamidullah Binol, M. Khalid Khan Niazi, Charles Elmaraghy, Aaron Moberly, Metin Gurcan

Proceedings Volume 11601, 116010S (2021) https://doi.org/10.1117/12.2582009

KEYWORDS: Video, Video processing, Diagnostics, Visualization, Ear, Visual process modeling, Instrument modeling, Image segmentation, Composites, Visual analytics

Read Abstract +

Proceedings Article | 16 March 2020 Presentation + Paper

Decision fusion on image analysis and tympanometry to detect eardrum abnormalities

Hamidullah Binol, Aaron Moberly, M. Khalid Khan Niazi, Garth Essig, Jay Shah, Charles Elmaraghy, Theodoros Teknos, Nazhat Taj-Schaal, Lianbo Yu, Metin Gurcan

Proceedings Volume 11314, 113141M (2020) https://doi.org/10.1117/12.2549394

KEYWORDS: Ear, Image analysis, Video, Image classification, Visualization, Medicine, Sensors, Diagnostics, Decision support systems, Pattern recognition, Machine learning, Machine vision, Computer aided diagnosis and therapy

Read Abstract +

Ear diseases are frequently occurring conditions affecting the majority of the pediatric population, potentially resulting in hearing loss and communication disabilities. The current standard of care in diagnosing ear diseases includes a visual examination of the tympanic membrane (TM) by a medical expert with a range of available otoscopes. However, visual examination is subjective and depends on various factors, including the experience of the expert. This work proposes a decision fusion mechanism to combine predictions obtained from digital otoscopy images and biophysical measurements (obtained through tympanometry) for the detection of eardrum abnormalities. Our database consisted of 73 tympanometry records along with digital otoscopy videos. For the tympanometry aspect, we trained a random forest classifier (RF) using raw tympanometry attributes. Additionally, we mimicked a clinician’s decision on tympanometry findings using the normal range of the tympanogram values provided by a clinical guide. Moreover, we re-trained Inception-ResNet-v2 to classify TM images selected from each otoscopic video. After obtaining predictions from each of three different sources, we performed a majority voting-based decision fusion technique to reach the final decision. Experimental results show that the proposed decision fusion method improved the classification accuracy, positive predictive value, and negative predictive value in comparison to the single classifiers. The results revealed that the accuracies are 64.4% for the clinical evaluations of tympanometry, 76.7% for the computerized analysis of tympanometry data, and 74.0% for the TM image analysis while our decision fusion methodology increases the classification accuracy to 84.9%. To the best of our knowledge, this is the first study to fuse the data from digital otoscopy and tympanometry. Preliminary results suggest that fusing information from different sources of sensors may provide complementary information for accurate and computerized diagnosis of TM-related abnormalities.

Proceedings Article | 27 February 2018 Paper

Detection of eardrum abnormalities using ensemble deep learning approaches

Caglar Senaras, Aaron C. Moberly, Theodoros Teknos, Garth Essig, Charles Elmaraghy, Nazhat Taj-Schaal, Lianbo Yua, Metin Gurcan

Proceedings Volume 10575, 105751A (2018) https://doi.org/10.1117/12.2293297

KEYWORDS: Neural networks, Bragg cells, Network architectures, Convolutional neural networks, Ear, Medicine, Diagnostics, Machine learning, Health informatics, Machine vision

Read Abstract +

Proceedings Article | 3 March 2017 Presentation + Paper

Autoscope: automated otoscopy image analysis to diagnose ear pathology and use of clinically motivated eardrum features

Caglar Senaras, Aaron Moberly, Theodoros Teknos, Garth Essig, Charles Elmaraghy, Nazhat Taj-Schaal, Lianbo Yu, Metin Gurcan

Proceedings Volume 10134, 101341X (2017) https://doi.org/10.1117/12.2250592

KEYWORDS: Image analysis, Diagnostics, Ear, Image classification, Feature extraction, Machine vision, Computer vision technology, Bragg cells, Video

Read Abstract +

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Keywords/Phrases

Search In:

Publication Years