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Brain image segmentation is one of the most important applications in medicine and also is one of the most challenging topics in the field of medical image processing. In general, most automatic segmentation methods consist of an energy function, a shape model, and an optimization strategy. Each plays an important role in the design of an accurate segmentation algorithm. Here we introduce a modified...
Filamentary structures extraction in medical and biological images is a challenging problem. Muscular/Neural fibers, neurites and blood arteries are some examples. Their delineation is particularly problematic due to the lack of solid visual support that is also compromised by the presence of clutter and low signal to noise ratios. In this article, we propose a modular approach to curvilinear structures...
In this paper, a novel approach to MRI Brain Image segmentation based on the Hybrid Parallel Ant Colony Optimization (HPACO) with Fuzzy C-Means (FCM) Algorithm have been used to find out the optimum label that minimizes the Maximizing a Posterior (MAP) estimate to segment the image. There are M colonies, M-1 colonies treated as slaves and one colony for master. Each colonies visit all the pixels with...
This paper proposes a hybrid framework in the study of ultrasound image segmentation that combines bottom-up (BU) with top-down (TD) strategy. The BU part with high precision constructs a weight matrix combining region- and edge- cues in terms of a novel idea of designing support regions, whereas the TD part incorporates location-driven prior knowledge from the doctor and spatial coherence information...
The process of single section 2D/3D registration one by one based on the whole bone registration is presented. After linear interpolation, the original 3D CT data had the same resolution in all three direction and the CT volume data is segmented by manual. After calibration x-ray film scene, the digitally reconstructed radiographs(DRRs) are got using "Ray Tracing" algorithm and interpolation...
We introduce a 3D segmentation framework which uses principal shapes. The probabilistic energy function of the method is defined based on intensity, tissue type, and location information of the structures using a multiple atlas method. For intensity information, nonparametric probability density function is used which considers intensity relation of different structures. To find a local minimum of...
Statistical shape modeling is an established technique and is used for a variety of tasks in medical image processing, such as image segmentation and analysis. A challenging task in the construction of a shape model is establishing a good correspondence across the set of training shapes. Especially for shapes of cylindrical topology, very little work has been done. This paper describes an automatic...
AM-FM analysis methods have been used in several biomedical imaging applications. In this paper, we are interested in the development of AM-FM analysis methods for small components, regions of interests (ROIs), and segmented objects. For detecting small components, we propose the use of a new multi-scale AM-FM edge and peak analysis system. The new system uses the product of gradient estimates from...
Many medical image segmentation techniques have been proposed by lots of authors but they are mainly dedicated to particular solutions. There is no generic method for solving the image segmentation problem. The difficulty comes from that two types of noise are presented in medical images: physical noise due to the acquisition system, for example, Optical, X-rays and MRI, and physiological noise due...
Glaucoma is the second leading cause of blindness. Glaucoma can be diagnosed through measurement of neuro-retinal optic cup-to-disc ratio (CDR). Automatic calculation of optic cup boundary is challenging due to the interweavement of blood vessels with the surrounding tissues around the cup. A Convex Hull based Neuro-Retinal Optic Cup Ellipse Optimization algorithm improves the accuracy of the boundary...
In order to distinguish normal tissues and abnormal pathological changes in the clinic diagnose and pathology, it is required to segment the medical images. The snake model is an important method of getting the contour of the object in the image segmentation. However, it has many defects in some fields such as concavity processing, local optimization, convergence speed and segmentation precision....
Graph cut algorithms (i.e., min s-t cuts) [3][10][15] are useful in many computer vision applications. In this paper we develop a formulation that allows the addition of side constraints to the min s-t cuts algorithm in order to improve its performance. We apply this formulation to foreground/background segmentation and provide empirical evidence to support its usefulness. From our experiments on...
This paper presents a new technique to generate triangular mesh surface parameterization and characterize 3-D surfaces by invariant spherical harmonic shape descriptors of objects with spherical topology. First, the surface is initially parameterized by defining a continuous one-to-one mapping from the surface of the object to the surface of a unit sphere. Then, the initial parameterization is optimized...
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