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In this paper, we propose a multi-atlas-based method to automatically segment the femoral and tibial cartilage from T1 weighted magnetic resonance (MR) knee images. The segmentation result is a joint decision of the spatial priors from a multi-atlas registration and the local likelihoods within a Bayesian framework. The cartilage likelihoods are obtained from a probabilistic k nearest neighbor classification...
The accurate segmentation of the bone and articular cartilages from magnetic resonance (MR) images of the hip is important for clinical studies and drug trials into conditions like osteoarthritis. In current studies, segmentations are obtained using time-consuming manual or semi-automatic algorithms which have high inter- and intra-observer variabilities. This paper presents an important step towards...
Statistical shape and intensity modelling have been subject to an increasing interest within the past decade. However, construction of such models requires large number of segmented examples. Accurate and automatic segmentation techniques that do not require any explicit prior model are therefore of high interest. We propose a fully-automatic method for segmenting the femur in 3D Computed Tomography...
In the human body knees are most complex and delicate joints. Knee joints are frequently injured and damaged due to articulations. The knees are among the joints most commonly affected by osteoarthritis (OA). Articular Cartilage is a hard but slippery coating on the end of each bone which forms the joint. Cartilage breaks down and wears away in OA. In this work, image processing techniques are applied...
In this paper we propose a novel semi-automated atlas-based approach for organ and bone approximation for micro-Magnetic Resonance Imaging (μMRI) data of mice. Based on a set of 18 manually indicated landmarks at specific joint & bone locations, individual atlas bones (pelvis, limb bones and sternum) are mapped to the target in a first step and a sparse set of corresponding landmarks on a skin...
This paper describes an automatic algorithm to extract the knee frame of reference from 3D MR isotropic scans. The method ultimately seeks to determine two lines that are tangent to the bottom of the condyles in an axial and a coronal plane. It consists of three major parts, initial detection of the knee joint using Hidden Markov Models, femur segmentation using Random Walker segmentation, and finally...
In this paper, we propose a CAD (Computer aided diagnosis) system to analyze the RA (rheumatoid arthritis) and osteoporosis by using image processing techniques from the CR images. To analyze the RA, we develop a segmentation method for phalanges in CR Image by use of DCT (Discrete Cosine Transform) for detection of temporal change. The temporal change is detected using the difference image between...
In this paper, we propose a fully automatic method to segment bone compartments in magnetic resonance (MR) images of knee joints gathered from a public database for research on knee osteoarthritis (OA), the osteoarthritis initiative (OAI). Considering the fixed scanning parameters which include position and flexion of the knee joint, the proposed method efficiently utilizes both shape and intensity...
In this paper we propose a framework for fully automatic, robust and accurate segmentation of the human pelvis and proximal femur in CT data. We propose a composite statistical shape model of femur and pelvis with a flexible hip joint, for which we extend the common definition of statistical shape models as well as the common strategy for their adaptation. We do not analyze the joint flexibility statistically,...
In this paper we develop a fully automated method for the segmentation of the femur in axial MR images and its use in the analysis of imaging biomarkers for osteoarthritis (OA). The proposed method is based on anatomical constraints implemented using morphological operations to extract the femur medulla and a level set evolution to extract the femur cortex. The average agreement of the automated segmentation...
The temporomandibular joint is one of the most complicated joints in the human body. Diagnosing its disorders is difficult because the pain is mistakenly taken for toothache, pain in the jaw bones, etc. The paper deals with a post-processing tomographic examination of temporomandibular joint. An interesting post-processing method was used to increase the contrast related to relaxation time T2. Both...
We present the minimal entropy prior for the iterative reconstruction of muCT projection data using the maximum a posteriori (MAP) framework. The minimal entropy prior restricts the number of intensity values in the reconstruction without requiring a priori information about the number of image tissues or tissue intensity estimates. We utilise the presented prior in the MAP framework to simultaneously...
Meniscal myxoid degeneration (MMD) represents a type of degenerative lesion, characterized by histological alterations of the meniscus. In the context of magnetic resonance (MR) imaging evaluation of MMD, the incidence of the condition is indicated by the presence of high intensity signal within the meniscus, while normal menisci are depicted as of homogeneously low intensity. In the present study,...
The segmentation of bones in computed tomography (CT) images is an important step for the simulation of forearm bone motion, since it allows to include patient specific anatomy in a kinematic model. While the identification of the bone diaphysis is straightforward, the segmentation of bone joints with weak, thin, and diffusive boundaries is still a challenge. We propose a graph cut segmentation approach...
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