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Recent developments in high resolution MRI scanning of the human spine are providing increasing opportunities for the development of accurate automated approaches for pathoanatomical assessment of intervertebral discs and vertebrae. We are developing a fully automated 3D segmentation approach for MRI scans of the human spine based on statistical shape analysis and template matching of grey level intensity...
The aim of this research is to produce an accurate segmentation of the brain grey matter tissue of a 3D MR (Magnetic Resonance) image from a high field (7T) MR scanner. 7T scanners produce images with a high SNR (Signal to Noise Ratio), but also with high inhomogeneity that makes brain segmentation a very challenging problem. The level set method is a popular method for image segmentation. However,...
This paper presents an algorithm for a 3D segmentation of the aorta artery in magnetic resonance images (MRI). The purpose is to project the 3D segmented aorta in the patient's abdomen with an augmented reality (AR) system to help the surgeon in laparoscopic interventions. In order to obtain accurate results in the segmentation process a marker-controlled watershed algorithm is used. Since this method...
Prostate segmentation is an essential step in developing any non-invasive Computer-Assisted Diagnostic (CAD) system for the early diagnosis of prostate cancer using Magnetic Resonance Images (MRI). In this paper, a novel framework for 3D segmentation of the prostate region from Diffusion-Weighted Magnetic Resonance Imaging (DW-MRI) is proposed. The framework is based on a Maximum A Posteriori (MAP)...
In this paper a new automatic skull stripping method for T1-weighted MR image of human brain is presented. Skull stripping is a process that allows to separate the brain from the rest of tissues. The proposed method is based on a 2D brain extraction making use of fuzzy c-means segmentation and morphological operators applied on transversal slices. The approach is extended to the 3D case, taking into...
This paper presents a study that investigated the potential of texture analysis using Fluid Sensitive Fat Suppressed MRI images for the use in detection of bone marrow edema. A total of 168 slices of knee MRI from 10 subjects were involved. Six histogram-based textures (mean intensity, standard deviation, smoothness, third moment, uniformity and entropy) were calculated in both 2D and 3D, and were...
Viability assessment of heart muscle after a myocardial infarction is an important step for diagnosis and therapy planning. It is important to quantify the area of edema because it can differentiate between viable and death myocardial tissues. In this paper an automatic method to quantify cardiac edema is presented. The method is based on a combination of morphological operations and statistical thresholding...
In patients with intractable epilepsy, focal cortical dysplasia (FCD) is the most frequent malformation of cortical development. Identification of subtle FCD lesions using brain MRI scans is very often based on the cortical thickness measurement, where brain cortex segmentation is required as a preprocessing step. However, the accuracy of the selected segmentation method can highly affect the final...
In this paper we report a method to automatically segment the internal part of globus pallidus (GPi) on the pre-operative low-resolution magnetic resonance images (MRIs) of patients affected by Parkinson's disease. Herein we used an ultra-high resolution human brain dataset as electronic atlas of reference on which we segmented the GPi. First, we registered the ultra-high resolution dataset on the...
3D Quantitative measurement of left ventricle (LV) motion on patients with acute myocardial infarction has been recognized as essential for effective LV function diagnosis. This paper presents a method to quantify 3D LV motion obtained from conventional CINE MRI using image analysis based on mathematical modeling. Level set method is employed for segmentation, and a 3D LV geometry was reconstructed...
In medical image processing segmentation of anatomical regions of the brain is the fundamental problem. Here, a brain tumor segmentation method has been developed and validated using MRI Data. In Preprocessing and Enhancement stage, medical image is converted into standard formatted image. Segmentation subdivides an image into its constituent regions or objects. This method can segment a tumor provided...
Breast cancer is the most common non-skin cancer and the second leading cause of cancer-related death in women here in the United States. Mammography is the current standard clinical imaging modality for breast cancer screening and diagnosis, and mammographic breast density (i.e. the percentage of the entire breast volume that is taken up by dense glandular tissue) has been shown to be a biomarker...
The study of in utero fetal MR images is essential for the diagnosis of abnormal brain development and the study of the maturation of the brain structures. Because of the particular properties of these images, only a few automated segmentation methods have been developed so far compared to the numerous ones existing for the adult brain anatomy. In this paper, we propose a two-step cortex segmentation...
This paper presents a fully unsupervised segmentation method for the segmentation of 3D DESS MRI images of the human knee. Five MRI knees manually segmented by human experts are used as reference atlases to automatically segment subsequent MRI images. The five segmentations are averaged to create the knee segmentation. The methodology was tested on the pilot Osteoarthritis Initiative (OAI) image set...
Prostate segmentation is an essential step in developing any non-invasive Computer-Assisted Diagnostic (CAD) system for the early diagnosis of prostate cancer using Magnetic Resonance Images (MRI). In this paper, a novel framework for 3D segmentation of the prostate region from Diffusion-Weighted Magnetic Resonance Imaging (DW-MRI) is proposed. The framework is based on a Maximum the Posteriori (MAP)...
This paper presents a novel framework for assessing tumor changes based on histogram analysis of temporal Magnetic Resonance Image (MRI) data. The proposed method detects the distribution of tumor and quantitatively models its growth or shrinkage offering the potential to assist clinicians in objectively assessing subtle changes during therapy. The presented work and the initial validation refer to...
Prostate segmentation is an essential step in developing any non-invasive Computer-Assisted Diagnostic (CAD) system for the early diagnosis of prostate cancer using Magnetic Resonance Images (MRI). In this paper, we propose, a novel framework for 3D segmentation of the prostate region from Dynamic Contrast Enhancement Magnetic Resonance Images (DCE-MRI). The framework is based on a maximum aposteriori...
In the recent years human brain segmentation in three-dimensional magnetic resonance imaging (MRI) has gained a lot of importance in the field of biomedical image processing since it is the main stage for the automatic brain disease diagnosis. In this paper, we propose an image segmentation scheme to segment 3D brain tumor from MRI images through the clustering process. The clustering is achieved...
This paper proposes a novel combinational approach for statistical de-noising and segmentation of 3D magnetic resonance images (MRIs) of the brain. The proposed method is based on Markov Random Field (MRF), conjunction with simulated annealing (SA) and improved genetic algorithm (IGA). MRF methods have been widely studied for segmentation. Despite the Markovianity which depicts the local characteristic,...
We present a novel method for 3D brain tumor volume segmentation based on a parallel cellular automata framework. Our method incorporates prior label knowledge gathered from user seed information to influence the cellular automata decision rules. Our proposed method is able to segment brain tumor volumes quickly and accurately using any number of label classifications. Exploiting the inherent parallelism...
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