Ahsan Humayun’s research while affiliated with Dalian University of Technology and other places

Segmenting anterior and posterior cruciate ligaments (ACL/PCL) presents challenges in medical imaging due to diverse characteristics, including size, shape, and intensity. Our study uses superpixel-based spectral clustering for knee cruciate ligament segmentation in 2D DICOM slices, renowned for generating high-quality clusters. The proposed method addresses the challenges by (i) identifying the ligamentous region (ROI) through superpixel-based computation, (ii) extracting features (intensity-based, shape-based, geometric complexity, and Scale-Invariant Feature Transform) from the ROI, and (iii) segmenting knee ligament tissues using spectral clustering on the extracted features. Superpixel-based spectral clustering addresses the challenge of constructing a dense similarity matrix and significantly reduces the computational burden. Furthermore, 3D visualization of ligament structures is performed using the Visualization Toolkit (VTK). We evaluated our proposed approach on a dataset of knee MRI slices, assessing the results via the dice score, average surface distance (ASD), and root mean squared error (RMSE) metrics. Our method achieved an average dice score of 0.912 for ACL segmentation and 0.896 for PCL segmentation, outperforming other clustering methods. These scores showed an enhancement of 10.7% and 14.9% in segmentation accuracy for the ACL and PCL, respectively. Furthermore, reduced error margins were demonstrated with the mean ASD values of 1.60 and 1.78 and the mean RMSE values of 1.76 and 1.86 for ACL and PCL, respectively. These results show the effectiveness of the proposed method for cruciate ligament segmentation and its potential for increasing the segmentation accuracy and speed, offering significant advantages over manual segmentation by reducing time and expertise.

Background Accurate delineation of knee bone boundaries is crucial for computer-aided diagnosis (CAD) and effective treatment planning in knee diseases. Current methods often struggle with precise segmentation due to the knee joint’s complexity, which includes intricate bone structures and overlapping soft tissues. These challenges are further complicated by variations in patient anatomy and image quality, highlighting the need for improved techniques. This paper presents a novel semi-automatic segmentation method for extracting knee bones from sequential computed tomography (CT) images. Methods Our approach integrates the fuzzy C-means (FCM) algorithm with an adaptive region-based active contour model (ACM). Initially, the FCM algorithm assigns membership degrees to each voxel, distinguishing bone regions from surrounding soft tissues based on their likelihood of belonging to specific bone regions. Subsequently, the adaptive region-based ACM utilizes these membership degrees to guide the contour evolution and refine segmentation boundaries. To ensure clinical applicability, we further enhance our method using the marching cubes algorithm to reconstruct a three-dimensional (3D) model. We evaluated the method on six randomly selected knee joints. Results We evaluated the method using quantitative metrics such as the Dice coefficient, sensitivity, specificity, and geometrical assessment. Our method achieved high Dice scores for the femur (98.95%), tibia (98.10%), and patella (97.14%), demonstrating superior accuracy. Remarkably low root mean square distance (RSD) values were obtained for the tibia and femur (0.5±0.14 mm) and patella (0.6±0.13 mm), indicating precise segmentation. Conclusions The proposed method offers significant advancements in CAD systems for knee pathologies. Our approach demonstrates superior performance in achieving precise and accurate segmentation of knee bones, providing valuable insights for anatomical analysis, surgical planning, and patient-specific prostheses.