Automatic atlas-based three-label cartilage segmentation from MR knee images

Osteoarthritis (OA) is the most common form of joint disease and often characterized by cartilage changes. Accurate quantitative methods are needed to rapidly screen large image databases to assess changes in cartilage morphology. We therefore propose a new automatic atlas-based cartilage segmentation method for future automatic OA studies

Semantic Context Forests for Learning-Based Knee Cartilage Segmentation in 3D MR Images

The automatic segmentation of human knee cartilage from 3D MR images is a useful yet challenging task due to the thin sheet structure of the cartilage with diffuse boundaries and inhomogeneous intensities. In this paper, we present an iterative multi-class learning method to segment the femoral, tibial and patellar cartilage simultaneously, which effectively exploits the spatial contextual constraints between bone and cartilage, and also between different cartilages. First, based on the fact that the cartilage grows in only certain area of the corresponding bone surface, we extract the distance features of not only to the surface of the bone, but more informatively, to the densely registered anatomical landmarks on the bone surface. Second, we introduce a set of iterative discriminative classifiers that at each iteration, probability comparison features are constructed from the class confidence maps derived by previously learned classifiers. These features automatically embed the semantic context information between different cartilages of interest. Validated on a total of 176 volumes from the Osteoarthritis Initiative (OAI) dataset, the proposed approach demonstrates high robustness and accuracy of segmentation in comparison with existing state-of-the-art MR cartilage segmentation methods.Comment: MICCAI 2013: Workshop on Medical Computer Visio

Development of Anterior Cruciate Ligament (ACL) diagnosis system using image processing

No AbstractKeywords: anterior cruciate ligament (ACL);diagnosis system, image processing, magnetic resonance image (MRI

Fuzzy Clustering Image Segmentation Based on Particle Swarm Optimization

Image segmentation refers to the technology to segment the image into different regions with different characteristics and to extract useful objectives, and it is a key step from image processing to image analysis. Based on the comprehensive study of image segmentation technology, this paper analyzes the advantages and disadvantages of the existing fuzzy clustering algorithms; integrates the particle swarm optimization (PSO) with the characteristics of global optimization and rapid convergence and fuzzy clustering (FC) algorithm with fuzzy clustering effects starting from the perspective of particle swarm and fuzzy membership restrictions and gets a PSO-FC image segmentation algorithm so as to effectively avoid being trapped into the local optimum and improve the stability and reliability of clustering algorithm. The experimental results show that this new PSO-FC algorithm has excellent image segmentation effects

Brain Tumor Segmentation from MRI Data Using Ensemble Learning and Multi-Atlas

Atlases are frequently employed to assist medical image segmentation with prior information. This paper introduces a multi-atlas architecture that is trained to locally characterize the appearance (average intensity and standard deviation) of normal tissues in various observed and computed data channels of brain MRI records. The multiple atlas is then deployed to enhance the accuracy of an ensemble learning based brain tumor segmentation procedure that uses binary decision trees. The proposed method is validated using the low-grade tumor volumes of the BraTS 2016 train data set. The use of atlases improve the segmentation quality, causing a rise of up to 1.5% in average Dices scores

Articular cartilage surface roughness as an imaging‐based morphological indicator of osteoarthritis: A preliminary investigation of osteoarthritis initiative subjects

Current imaging‐based morphometric indicators of osteoarthritis (OA) using whole‐compartment mean cartilage thickness (MCT) and volume changes can be insensitive to mild degenerative changes of articular cartilage (AC) due to areas of adjacent thickening and thinning. The purpose of this preliminary study was to evaluate cartilage thickness‐based surface roughness as a morphometric indicator of OA. 3D magnetic resonance imaging (MRI) datasets were collected from osteoarthritis initiative (OAI) subjects with Kellgren–Lawrence (KL) OA grades of 0, 2, and 4 (n = 10/group). Femoral and tibial AC volumes were converted to two‐dimensional thickness maps, and MCT, arithmetic surface roughness (Sa), and anatomically normalized Sa (normSa) were calculated. Thickness maps enabled visualization of degenerative changes with increasing KL grade, including adjacent thinning and thickening on the femoral condyles. No significant differences were observed in MCT between KL grades. Sa was significantly higher in KL4 compared to KL0 and KL2 in the whole femur (KL0: 0.55 ± 0.10 mm, KL2: 0.53 ± 0.09 mm, KL4: 0.79 ± 0.18 mm), medial femoral condyle (KL0: 0.42 ± 0.07 mm, KL2: 0.48 ± 0.07 mm, KL4: 0.76 ± 0.22 mm), and medial tibial plateau (KL0: 0.42 ± 0.07 mm, KL2: 0.43 ± 0.09 mm, KL4: 0.68 ± 0.27 mm). normSa was significantly higher in KL4 compared to KL0 and KL2 in the whole femur (KL0: 0.22 ± 0.02, KL2: 0.22 ± 0.02, KL4: 0.30 ± 0.03), medial condyle (KL0: 0.17 ± 0.02, KL2: 0.20 ± 0.03, KL4: 0.29 ± 0.06), whole tibia (KL0: 0.34 ± 0.04, KL2: 0.33 ± 0.05, KL4: 0.48 ± 0.11) and medial plateau (KL0: 0.23 ± 0.03, KL2: 0.24 ± 0.04, KL4: 0.40 ± 0.10), and significantly higher in KL2 compared to KL0 in the medial femoral condyle. Surface roughness metrics were sensitive to degenerative morphologic changes, and may be useful in OA characterization and early diagnosis. © 2017 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 35:2755–2764, 2017.A custom algorithm was used to create two‐dimensional articular cartilage thickness maps of patients from the Osteoarthritis Initiative. Thickness maps demonstrate significantly increased surface roughness as a function of increasing Kellgren–Lawrence (KL) osteoarthritis (OA) grade, particularly in the medial femoral condyle, though mean cartilage thickness was not found to differ significantly between KL grades. Surface roughness‐based metrics have potential utility as morphological indicators of OA.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/141486/1/jor23588_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/141486/2/jor23588.pd