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Medical image analysis brings about a revolution to the medicine of the 21st century, introducing a collection of powerful new tools designed to better assist the clinical diagnosis and to model, simulate, and guide more efficiently the patient’s therapy. A new discipline has emerged in computer science, closely related to others like computer vision, computer graphics, artificial...
Analysis of medical images, especially the extraction of anatomical structures, is a critical component of many medical applications: surgical planning and navigation, and population studies of anatomical shapes for tracking disease progression are two primary examples. We summarize recent trends in segmentation and analysis of shapes, highlighting how different sources of information have been factored...
In this article, we present some advances on medical imaging and computing at the National Laboratory of Pattern Recognition (NLPR) in the Chinese Academy of Sciences. The first part is computational neuroanatomy. Several novel methods on segmentations of brain tissue and anatomical substructures, brain image registration, and shape analysis are presented. The second part consists of brain connectivity,...
Tissue Engineering is an interdisciplinary field that applies the principles of biology and engineering to develop tissue substitutes to restore, maintain, or improve the function of diseased or damaged human tissues. One approach for engineering tissue involves seeding biodegradable scaffolds with hormones, then culturing and implanting the scaffolds by means of a printing technology to induce and...
A decade ago, cardiovascular image analysis very much meant the application of image analysis methods to a cardiac image sequence obtained from a friendly cardiologist. 10 years on, have things changed? In this short paper, I overview research conducted in Oxford in the area of cardiovascular image analysis over this period, aimed at developing clinically useful methods for diagnosing heart disease...
Automated image analysis tools have the potential to play an important role in assisting in the diagnosis and treatment of retinal diseases. Problems that must be addressed in developing these tools include extraction of vascular and non-vascular features, segmentation of pathologies, unimodal and multimodal image registration, mosaic construction, and real-time systems. Research at Rensselaer Polytechnic...
This paper presents the creation of 3D statistical shape models of the knee bones and their use to embed information into a segmentation system for MRIs of the knee. We propose utilising the strong spatial relationship between the cartilages and the bones in the knee by embedding this information into the created models. This information can then be used to automate the initialisation of segmentation...
Image segmentation is an important research topic in medical image analysis area. In this paper, we firstly propose a generalized level set formulation of the Mumford-Shah functional by a sound mathematical definition of line integral. The variational flow is implemented in level set framework and thus implicit and intrinsic. By embedding a weighted length term to the original Mumford-Shah functional,...
We present a novel approach to efficiently compute thickness, correspondence, and gridding of tissues between two simply connected boundaries. The solution of Laplace’s equation within the tissue region provides a harmonic function whose gradient flow determines the correspondence trajectories going from one boundary to the other. The proposed method uses and expands upon two recently introduced techniques...
In this paper we present a new framework for image segmentation using probabilistic multinets. We apply this framework to integration of region-based and contour-based segmentation constraints. A graphical model is constructed to represent the relationship of the observed image pixels, the region labels and the underlying object contour. We then formulate the problem of image segmentation as the one...
In this paper we present a fully automatic and accurate segmentation framework for 2D tagged cardiac MR images. This scheme consists of three learning methods: a) an active shape model is implemented to model the heart shape variations, b) an Adaboost learning method is applied to learn confidence-rated boundary criterions from the local appearance features at each landmark point on the shape model,...
Microaneurysms (MAs) detection is a critical step in diabetic retinopathy screening, since MAs are the earliest visible warning of potential future problems. A variety of thresholding based algorithms have been proposed for MAs detection in mass screening. Most of them process fundus images globally without a mechanism to take into account the local properties and changes. Their performance is often...
Registration, that is, the alignment of multiple images, has been one of the most challenging problems in the field of computer vision. It also serves as an important role in biomedical image analysis and its applications. Although various methods have been proposed for solving different kinds of registration problems in computer vision, the results are still far from ideal when it comes to real world...
Digital colposcopy is an emerging new technology, which can be used as adjunct to the conventional Pap test for staging of cervical cancer and it can improve the diagnostic accuracy of the test. Computer aided diagnosis (CAD) in digital colposcopy has as a goal to segment and outline abnormal areas on the cervix, one of which is an important anatomical landmark on the ectocervix – the transformation...
We present a novel framework to exert topology control over a level set evolution. Level set methods offer several advantages over parametric active contours, in particular automated topological changes. In some applications, where some a priori knowledge of the target topology is available, topological changes may not be desirable. This is typically the case in biomedical image segmentation, where...
The paper addresses the problem of virtual craniofacial reconstruction from a set of Computer Tomography (CT) images, with the multiple objectives of achieving accurate local matching of the opposable fracture surfaces and preservation of the global shape symmetry and the biomechanical stability of the reconstructed mandible. The first phase of the reconstruction, with the mean squared error as the...
Image segmentation is an important research topic in image processing and computer vision community. In this paper, we present a novel segmentation method based on the combination of fuzzy connectedness and adaptive fuzzy C means (AFCM). AFCM handles intensity inhomogeneities problem in magnetic resonance images (MRI) and provides effective seeds for fuzzy connectedness simultaneously. With the seeds...
A method for automated detection of calcifications in the abdominal aorta from standard X-ray images is presented. Pixel classification based on local image structure is combined with a spatially varying prior that is derived from a statistical model of the combined shape variation in aorta and spine. Leave-one-out experiments were performed on 87 standard lateral lumbar spine X-rays, resulting...
This paper presents a method for tessellating tissue boundaries and their interiors, given as input a tissue map consisting of relevant classes of the head, in order to produce anatomical models for finite element-based simulation of endoscopic pituitary surgery. Our surface meshing method is based on the simplex model, which is initialized by duality from the topologically accurate results of the...
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