The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Maximum Likelihood Estimation can provide an accurate estimate of activity distribution for Positron Emission Tomography (PET) and Single Photon Emission Computed Tomography (SPECT), however its unconstrained application suffers from dimensional instability due to approximation of activity distribution to a grid of point processes. Correlation between the activity distribution and the underlying tissue...
In the paper, Otsu's algorithm is combined with mutual information (MI) technique. The initial threshold can be chosen using Otsu algorithm, and in the iteration process, an optimal threshold will be determined by maximizing the MI between the original volume and the thresholded volume. We evaluate the effectiveness of the proposed approach by applying it to the medical images (MR, microphotographic)...
We have devised a new technique to segment an diseased MRI image wherein the diseased part is segregated using a masking based thresholding technique together with entropy maximization. The particle swarm optimization technique (PSO) is used to get the region of interest (ROI) of the MRI image. The mask used is a variable mask. The rectangular mask is grown using an algorithm provided in the subsequent...
The geometry of conduits derived from in vivo image data is subject to acquisition and reconstruction errors. This results in a degree of uncertainty in defining the bounding geometry for a patient-specific anatomical conduit. The impact of the conduit geometry uncertainty should be considered with respect to haemodynamic clinically relevant measures that may alter the perception and evaluation of...
The clinical interpretation of breast MRI remains largely subjective, and the reported findings qualitative. Although the sensitivity of the method for detecting breast cancer is high, its specificity is poor. Computerised interpretation offers the possibility of improving specificity through objective quantitative measurement. This paper reviews the plethora of such features that have been proposed...
Based on the clonal selection theory of artificial immune system, a novel optimal entropy threshold medical image segmentation method is proposed, in which, the affinity function is the optimal entropy threshold, and the medical image segmentation is considered as a optimization problem, clonal operator effectively enlarges searching range, supplies the diversity of solutions and can find the optimal...
This paper presents improved mountain clustering technique based MRI (magnetic resonance imaging) brain image segmentation for spotting tumors. The proposed technique is compared with some existing techniques such as K-Means and FCM, clustering. The performance of all these clustering techniques is compared in terms of cluster entropy as a measure of information and also is visually compared for image...
Accurate segmentation of magnetic resonance images (MRI) corrupted by intensity in homogeneity is a challenging problem and has received an enormous amount of attention lately. On the basis of the local image model, we propose a different segmentation method for MR brain images without estimation and correction for intensity heterogeneity. Firstly, we obtain clustering context which size is optimized...
Accurate segmentation of magnetic resonance images (MRI) corrupted by intensity inhomogeneity is a challenging problem and has received an enormous amount of attention lately. On the basis of the local image model, we propose a different segmentation method for MR brain images without estimation and correction for intensity heterogeneity. Firstly, we obtain clustering context based on the distributing...
This paper introduces a new method-the cloud model theory for the fMRI image processing which aims at the uncertainty of the fMRI image data and the flaws of the current medical image segmentation algorithm. The method takes full account of the uncertainty of fMRI data, through the production cloud, the cloud synthesis as well as the category determination, can be effective to carry on the classification...
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,...
To overcome the drawbacks of fuzzy multi-level entropic thresholding algorithm, a modified fuzzy multi-level thresholding algorithm for segmentation of MRI is presented in this paper. The algorithm is different from the entropic thresholding algorithm including two aspects. First, maximum variance criteria is used in the proposed algorithm to overcome the limitation of entropic algorithm, which usually...
We propose a constrained, three-dimensional, nonparametric, entropy-based, coupled, multi-shape approach to segment subcortical brain structures from magnetic resonance images (MRI). The proposed method uses PCA to develop shape models that capture structural variability. It integrates geometrical relationship between different structures into the algorithm by coupling them (limiting their independent...
In this paper we propose the use of a neurobiology-based saliency measure to improve the performance of a quantitative- qualitative measure of mutual information for rigid registration of 4D renal perfusion MR images. Our registration method assigns greater importance to more salient voxels by applying a soft thresholding function to normalized saliency values. The resulting saliency map is a better...
In this paper, we use an anisotropic diffusion in a level set framework for low-level segmentation of necrotic femoral heads. Our segmentation is based on three speed terms. The first one includes an adaptive estimation of the contrast level. We use the entropy for evaluating our diffusion on synthetic 3D data. We notice that using the data fidelity term in the last iterations excessively penalizes...
We propose a 3D nonparametric, entropy-based, coupled, multishape approach for the segmentation of subcortical brain structures in magnetic resonance images (MRI). Our method uses PCA to capture structures variability. Because of complex relationships of pose and shape of the coupled structures, we only use their shape and size relation. To this end, we apply separate registrations of the structures...
A very important artifact corrupting magnetic resonance images is the RF inhomogeneity. This kind of artifact generates variations of illumination which trouble both direct examination by the doctor and segmentation algorithms. Even if homomorphic filtering approaches have been presented in literature, none of them has developed a measure to determine the cut-off frequency. In this work we present...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.