- Spatiotemporal Big Data Analytics for Osteoarthritis Knee
- Project Year:
2017
- REU Student (s):
Tram Pham | University of California, Berkeley
- Student 1 Institution:
University of California, Berkeley
- Project Mentor:
Weihong 'Grace' Guo
- Project Mentor Area:
Industrial and Systems Engineering
- Project Abstract:
Osteoarthritis (OA) is the most prevalent disease amongst knee joints which mostly affects the cartilage in elderly and overweight people. Articular cartilage is simply
defined as a soft connective tissue at the end of bones which
prevents the bones from erosion and allows smooth glide
bones in the joint. OA is characterized by the gradual
degeneration of cartilage in knee joint. In Osteoarthritis,
cartilage seizes up, erodes away, and eventually disappears
in some regions causing the bones to rub again one another
with severe pain during motions. Measurement of cartilage
loss and 3D visualization can help to quantify the severity
of osteoarthritis. Nowadays, Magnetic Resonance imaging
(MRI) is extensively used to image the knee joint due to
its high resolution and contrast displacement between the
bones and cartilage. Though a profound work has been done
to detect boundary, there is lack of discussion on cartilage
measurement of severe OA knee and the advantages of image
pre-processing have not been fully exploited. Encouraged by
initial research, this paper proposes a semiautomatic method
to improve clinical evaluation of osteoarthritis disease. It
emphasizes on improving image pre-processing techniques
and the treatments to severe osteoarthritis cases. We explore
the image processing techniques applied on MRI in the
progression of osteoarthritis diagnosis. Namely, we will
identify the clinical biomarkers for early detection of OA
by (1) pre-processing MR images, (2) detecting boundary of
cartilage by modified radial search method, (3) 2D and 3D
cartilage visualizing and (4) calculating volume and thickness
of cartilage for OA quantification.
