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We present an enhancement method based on nonlinear diffusion filter and statistical intensity approaches for smoothing and extracting 3-D vascular system from Magnetic Resonance Angiography (MRA) data. Our method distinguishes and enhances the vessels from the other embedded tissues. The Expectation Maximization (EM) technique is employed with non-linear diffusion in order to find the optimal contrast...
In this paper, we propose a new method for the extraction of blood vessels in retinal images. This approach starts with a Hessian-based multiscale filtering method to enhance blood vessels in gray retinal images. Subsequently, a new radial symmetry transformation, which is based on line kernels, is proposed to improve the detection of vessel structures and restrain the response of nonvessel structures...
Segmenting cerebral blood vessels is of great importance in diagnostic and clinical applications, especially in quantitative diagnostics and surgery on aneurysms and arteriovenous malformations (AVM). Segmentation of CT angiography images requires algorithms robust to high intensity noise, while being able to segment low-contrast vessels. Because of this, most of the existing methods require user...
We present a framework for identifying disease states by classifying cells in the pathological regions of tissues into different categories. We use conditional random fields (CRF) to incorporate characteristics of cells and their spatial distributions. The efficacy of CRF to model cell-cell feature interactions is demonstrated by using a lung tissue dataset and a synthesized cancer tissue dataset...
In this study we discuss different types of texture features such as Fractal Dimension (FD) and Multifractional Brownian Motion (mBm) for estimating random structures and varying appearance of brain tissues and tumors in magnetic resonance images (MRI). We use different selection techniques including KullBack — Leibler Divergence (KLD) for ranking different texture and intensity features. We then...
In ultrasonic images, identification of speckled regions helps to estimate probe movement as well as improve performance of algorithms for adaptive speckle suppression and the elevational separation of B-scans by speckle decorrelation. By tracking FDS patch displacements over time we can calculate strain and detect tumor location. Previous studies for speckle detection were based on classification...
The aim of this research is proposing a 3-D similarity enhancement technique useful for improving the segmentation of cardiac structures in Multi-Slice Computerized Tomography (MSCT) volumes. The similarity enhancement is obtained by subtracting the intensity of the current voxel and the gray levels of their adjacent voxels in two volumes resulting after preprocessing. Such volumes are: a. — a volume...
In Magnetic Resonance Imaging (MRI), intensity inhomogeneity has been an issue affecting the quality of post processing. In this paper, we present a simultaneous segmentation and inhomogeneity correction (IC) method based on active contour algorithm. It uses a generative model which is a modified Mumford-Shah functional proposed by Chan and Vese. The piecewise constant image model in the functional...
This paper presents a method of automatically measuring peritoneum thickness in ultrasound images. In our previous work, a method of manually selecting the region of interest (ROI) area has been developed. To achieve an automatic ROI area selection, two phases: Gaussian high-pass filtering and bilateral filtering, are used in the proposed method. In the bilateral filtering phase, the ultrasound image...
We present VAMPIRE, a software application for efficient, semi-automatic quantification of retinal vessel properties with large collections of fundus camera images. VAMPIRE is also an international collaborative project of four image processing groups and five clinical centres. The system provides automatic detection of retinal landmarks (optic disc, vasculature), and quantifies key parameters used...
For medical diagnosis, blood is an indispensable indicator for a wide variety of diseases, i.e. hemic, parasitic and sexually transmitted diseases. A robust detection and exact segmentation of white blood cells (leukocytes) in stained blood smears of the peripheral blood provides the base for a fully automated, image based preparation of the so called differential blood cell count in the context of...
Cell segmentation is a crucial step in many bio-medical image analysis applications and it can be considered as an important part of a tracking system. Segmentation in phase-contrast images is a challenging task since in this imaging technique, the background intensity is approximately similar to the cell pixel intensity. In this paper we propose an interactive automatic pixel level segmentation algorithm,...
The gradient vector flow (GVF) algorithm has been used extensively as an efficient method for medical image segmentation. This algorithm suffers from poor robustness against noise as well as lack of convergence in small scale details and concavities. As a cure to this problem, in this paper the idea of multi scale is applied to the traditional GVF algorithm for segmentation of brain tumors in MRI...
Hemorrhage is the main cause of deaths that occurs within first 24 hours after a traumatic pelvic injury. Therefore, it is very important to determine hemorrhage quickly. Hemorrhages are detected using a CT scan. However, it is very time consuming for physicians to look for hemorrhage in all CT slices. Therefore, an automated system is needed. This paper proposes an automated hemorrhage detection...
Current standard quantitative 3D spectral-domain optical coherence tomography (SD-OCT) analyses of various ocular diseases is limited in detecting structural damage at early pathologic stages. This is mostly because only a small fraction of the 3D data is used in the current method of quantifying the structure of interest. This paper presents a novel SD-OCT data analysis technique, taking full advantage...
In this paper, we propose an automated liver segmentation method to overcome the challenging issues of high degree of variations in liver shape / size and similar density distribution shared by the liver and its surrounding structures. To improve the performance of conventional statistical shape model for liver segmentation, in our method, the signed distance function is utilized so that the landmarks...
We present a novel detection and classification method to process SPECT-CT images representing breast and prostate lymph nodes. Lymph nodes are those nodes that are near the primer tumor and may become cancerous in time, hence their early detection is a key factor for the successful treatment of the patient. Prior methods focus on the visual aid to manually detect the lymph nodes which still makes...
The extraction of airway and vessel trees plays an important role in the diagnosis and treatment planning of lung diseases. However, this is a challenging task due to the small size of the anatomical structures, noise, or artifacts in the image. The similar intensity values between the lung parenchyma and airway lumen, the airway wall and the blood vessels make extraction particularly difficult. Our...
In this work, we present a scheme for the registration of digitally reconstructed whole mount histology (WMH) to pre-operative in vivo multiprotocol prostate MR imagery (T2w and DCE) using spatially weighted mutual information (SWMI). Spatial alignment of ex vivo histological sections to pre-operative in vivo MRI for prostate cancer (CaP) patients undergoing radical prostatectomy is a necessary first...
Age related Macular Degeneration (AMD) is a disease of the retina associated with aging. AMD progression in patients is characterized by drusen, pigmentation changes, and geographic atrophy, which can be seen using fundus imagery. The level of AMD is characterized by standard scaling methods, which can be somewhat subjective in practice. In this work we propose a statistical image processing approach...
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