General Information
| Student: | Tram Nghi Pham |
|---|---|
| Office: | CoRE 446 |
| School: | University of California, Berkeley |
| E-mail: | tram.pham@rutgers.edu |
| Project: | Spatiotemporal Big Data Analytics for Osteoarthritis Detection |
Project Description
The Osteoarthritis Initiative (OAI) is a multicenter, longitudinal, prospective, observational study of knee osteoarthritis launched by the National Institutes of Health in 2002. The OAI has accumulated a massive amount of clinical and imaging data and biological specimens from thousands of volunteers with risk factors for early knee osteoarthritis for a total of 8 years of follow up. However, these data have not been fully exploited to make optimal decision for the improvement of the prevention and intervention strategies of knee osteoarthritis. This study focuses on spatiotemporal data analytics for osteoarthritis detection. The goal of this project is to identify clinical biomarkers for early detection of osteoarthrosis and improve the prevention and intervention strategies of knee osteoarthritis used in current clinical practice. Existing methods on spatiotemporal data analytics, dimensionality reduction, feature extraction and selection, and anomaly detection will be reviewed and studied.
Weekly Log
- Week 1:
- This week, I spent most of my time reading research papers regarding my project. We were able to narrow down some general methods which help me become familiar with the subject. The main method, which is proposed by my mentor, will be studied carefully. I ended this week by finishing up the presentation for next Monday and doing some image pre-processing implementations using Matlab.
- Week 2:
- I started working on method 1 - image processing to detect the boundary of cartilage. I haven't finished the code. The intensity of each pixel oscillates so it is hard to determine the threshold. I also read some papers regarding tensor decomposition, principal component analysis and its variants, etc. to get myself familiar with method 2.
- Week 3:
- This week, I was able to detect the inner and outer boundary of cartilage after spending a descent amount of time debugging the code that wasn't wrong. However, there are serveral images that need special treatment since I cannot even tell which part of the image is the cartilage. I read some articles about cartilage, OA knee in order to understand MRI images better. Next week, I will spend time to improve my implementations.
- Week 3:
- I finished detecting the break point of cartilage for severe and minor OA knee using linear regression and least square approximation. I tried to make my code efficient so that It works for all cases. We also decided to model the cartilage thickness in 3D using Matlab and calcualte its volume. I haven't done any 3D model simulations in Matlab, so it will be really cool to learn about 3D modeling.
Presentations
Additional Information
- My Mentors
- References