Articular cartilage is a thin and curvilinear structure whose image may get corrupted with noise during acquisition process. Image segmentation of this structure depends on expertise of an operator. In this paper, we propose an automated cartilage detection technique using 3D histogram and wavelet multiscale singularity analysis. 3D undecimated wavelet transform is implemented on MRI volume to obtain wavelet coefficients, which are used to determine an adaptive threshold for a given local resolution and a global threshold value. Local threshold helps to segment foreground cartilage edge details and is obtained using maximum likelihood estimate of the 3D histogram for a selected confidence level of intensity histogram. A global threshold is used to optimize coefficients using wavelet multiresolution singularity. The final wavelet coefficients are used to obtain a 3D model of cartilage tissue. The proposed method has been tested and validated using MRI and phantom datasets of articular cartilage. Quantitative analysis has been performed using mean square error (MSE), signal-to-noise ratio (SNR) and volumetric estimation of the datasets for different confidence and noise levels. The proposed method displays reduction in MSE for both denoised and noisy MRI volumes at different standard deviations of noise with overall improvement in SNR.
['Isshaa Aarya', 'Danchi Jiang']
Automated and optimal detection of 3D articular cartilage using undecimated wavelets in MRI