Mr. Przemyslaw W. Kowalski Profile

Przemyslaw W. Kowalski

at Univ Twente

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PROCEEDINGS ARTICLE | February 23, 2012

MRI based knee cartilage assessment

Proc. SPIE. 8315, Medical Imaging 2012: Computer-Aided Diagnosis

KEYWORDS: Medicine, Cartilage, Data modeling, Visualization, Magnetic resonance imaging, Image segmentation, Interfaces, Bone, Medical imaging, Brain-machine interfaces

Read Abstract

Osteoarthritis is one of the leading causes of pain and disability worldwide and a major health problem in developed countries due to the gradually aging population. Though the symptoms are easily recognized and described by a patient, it is difficult to assess the level of damage or loss of articular cartilage quantitatively. We present a novel method for fully automated knee cartilage thickness measurement and subsequent assessment of the knee joint. First, the point correspondence between a pre-segmented training bone model is obtained with use of Shape Context based non-rigid surface registration. Then, a single Active Shape Model (ASM) is used to segment both Femur and Tibia bone. The surfaces obtained are processed to extract the Bone-Cartilage Interface (BCI) points, where the proper segmentation of cartilage begins. For this purpose, the cartilage ASM is trained with cartilage edge positions expressed in 1D coordinates at the normals in the BCI points. The whole cartilage model is then constructed from the segmentations obtained in the previous step. An absolute thickness of the segmented cartilage is measured and compared to the mean of all training datasets, giving as a result the relative thickness value. The resulting cartilage structure is visualized and related to the segmented bone. In this way the condition of the cartilage is assessed over the surface. The quality of bone and cartilage segmentation is validated and the Dice's coefficients 0.92 and 0.86 for Femur and Tibia bones and 0.45 and 0.34 for respective cartilages are obtained. The clinical diagnostic relevance of the obtained thickness mapping is being evaluated retrospectively. We hope to validate it prospectively for prediction of clinical outcome the methods require improvements in accuracy and robustness.

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