Citation
Automatic Detection of Knee Joints in Radiographic Images

Material Information

Title:
Automatic Detection of Knee Joints in Radiographic Images
Creator:
Patel, Deep
Publication Date:
Language:
English

Notes

Abstract:
Knee osteoarthritis (OA) is a highly prevalent, disabling joint disease affecting people worldwide. It is currently diagnosed by evaluating symptoms as well as plain radiographic images. However, existing diagnostic methods suffer from subjective assessment, hindering early diagnosis of the disease to enable effective treatment. A plausible approach is the use of computer-aided image analysis to provide an objective evaluation of knee OA severity. A critical step in this approach involves detection of knee joint region to enable higher-level analysis, a task with significant room for improvement. In this preliminary study, the use of transfer learning to automatically detect knee joint in X-ray images is explored and YOLOv2, a state-of-the-art object detection system, is trained to perform the task. The model is trained and tested using data from Osteoarthritis Initiative (OAI). Once trained, the proposed model accurately detected and localized knee joint region with mean intersection-over-union of 0.907 and an average analysis time of 0.0153 seconds per image. This model can be used in OA research as many of the existing studies attempting to quantify knee OA severity rely on knee joint area detection. Furthermore, the pipeline can potentially be extended to automatically evaluate knee joint features during the early stages of the disease to predict knee OA progression. ( en )
General Note:
Awarded Bachelor of Health Science, summa cum laude, on May 8, 2018. Major: Health Science. Emphasis/Concentration: Public Health
General Note:
College or School: College of Engineering
General Note:
Advisor: Ruogu Fang. Advisor Department or School: Biomedical Engineering

Record Information

Source Institution:
University of Florida
Holding Location:
University of Florida
Rights Management:
Copyright Deep Patel. Permission granted to the University of Florida to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.

UFDC Membership

Aggregations:
UF Undergraduate Honors Theses

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