A Hybrid Technique for Thickness-Map Visualization of the Hip Cartilages in MRI

Abstract

SUMMARYQuantification of the hip cartilages is clinically important. In this study, we propose an automatic technique for segmentation and visualization of the acetabular and femoral head cartilages based on clinically obtained multi-slice T1-weighted MR data and a hybrid approach. We follow a knowledge based approach by employing several features such as the anatomical shapes of the hip femoral and acetabular cartilages and corresponding image intensities. We estimate the center of the femoral head by a Hough transform and then automatically select the volume of interest. We then automatically segment the hip bones by a self-adaptive vector quantization technique. Next, we localize the articular central line by a modified canny edge detector based on the first and second derivative filters along the radial lines originated from the femoral head center and anatomical constraint. We then roughly segment the acetabular and femoral head cartilages using derivative images obtained in the previous step and a top-hat filter. Final masks of the acetabular and femoral head cartilages are automatically performed by employing the rough results, the estimated articular center line and the anatomical knowledge. Next, we generate a thickness map for each cartilage in the radial direction based on a Euclidian distance. Three dimensional pelvic bones, acetabular and femoral cartilages and corresponding thicknesses are overlaid and visualized. The techniques have been implemented in C++ and MATLAB environment. We have evaluated and clarified the usefulness of the proposed techniques in the presence of 40 clinical hips multi-slice MR images.

Shape-based acetabular cartilage segmentation: application to CT and MRI datasets

“…Up to now, many efforts have been made for cartilage segmentation such as the methods proposed in [6][7][8][9][10][11][12][13][14][15][16][17]. Some of these methods require a training set to get the prior knowledge about the shape, position, or texture of the cartilage [6][7][8][9][10][11][12][13], while others are based on the knowledge-free methods [14][15][16][17].…”

“…Some of these methods require a training set to get the prior knowledge about the shape, position, or texture of the cartilage [6][7][8][9][10][11][12][13], while others are based on the knowledge-free methods [14][15][16][17]. In [6], a multi-template-based label fusion method was used to automatically segment 15 hip cartilage tissues taken by the magnetic resonance imaging (MRI) technique.…”

“…Then, acetabular cartilage was localized using the radial differentiation and image bottom hat (IBH) techniques in a two-dimensional (2D) way. In [15], an automatic method was suggested to segment the acetabular cartilage slice by slice in the MR images. This method contains hip joint bone segmentation using the vector quantization (VQ) technique and the principal component analysis (PCA) algorithm, articular centerline localization based on edge information, acetabular cartilage determination using some intensity information, and the experimentally obtained anatomical knowledge from the datasets used.…”

“…In [17], a semiautomatic method based on the Bezier splines and the Canny edge detector was utilized to segment the knee cartilages from MR database. Knowledge-free methods have low accuracy level in the presence of noise and loss of information [14][15][16][17], while training-based methods are more accurate [6][7][8][9][10][11][12][13]. However, training-based methods have at least one of these limitations: (1) high user interaction needs [8], (2) effectiveness only for healthy cartilage datasets [8,9], (3) dependency on modality [6,7,[9][10][11][12][13], and (4) lack of attention to spatial relationship between the neighboring structures in order to increase the accuracy level and generalization ability [6,8,10,11].…”

“…Comparing to the methods proposed in [6][7][8][9][10][11][12][13][14][15][16][17], our new method has these improvements: (1) requires a minimum user interaction only in pelvic bone segmentation procedures, (2) applicable for the damaged cartilages because of using the shape and intensity information simultaneously, (3) independent of different imaging techniques because of its prediction-based manner, (4) considers the anatomical information of pelvic bone in acetabular cartilage localization, and (5) has good performance in the presence of noise and loss of information because of utilizing the prior knowledge about cartilage's position, shape, and intensity. We compare our method with the methods proposed in [7,[13][14][15].…”