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Various studies have been conducted to expand the utilization of combined positron emission tomography and computed tomography (PET/CT) covering cases of infection and inflammation. PET images provide the functional activity of a lesion while CT images demonstrate the anatomical location. Hence, existence of infected lesions can be recognized in PET image but since the structural position can not...
Accurate segmentation of bone tissue from Pelvic CT images is an important step in the process of developing an automated computer aided decision making system that would provide physicians with recommendations for the diagnosis and treatment of traumatic pelvic injuries. The proposed algorithm is an automated, unsupervised, and hierarchical method for the segmentation of bone tissue. The method incorporates,...
In this paper, a segmentation method using Gaussian mixture model (GMM) combined with template match is proposed for analysis of brain CT images. The specific aim of this method is to extract ventricles from brain CT images. These can then be used for automated detection of the midline shift in brain. In the method, different types of brain tissue, of which the ventricles form the region of interest,...
The digital image processing techniques, including gray level transformation, image sharpening, image segmentation and edge detection, is applied to the study on the meso-structure image of shale. The results show that the digital image processing technique can extract useful information from the CT images and construct a model of meso-structure of shale. The model shows the spatial distribution of...
Immunoglobulin light chain amyloidosis (AL) is characterized by the deposition of light chain components as insoluble fibrils in tissues and organs leading to dysfunction and death and is the most common form of peripheral amyloid disease in humans. Presently, there are no means available in the USA to image specifically these deposits and thus ascertain the presence or extent of disease. To this...
Two-dimensional computed tomography (CT) images are not enough for further analysis. Only the points with the same Z coordinates can be measured on the two-dimensional images, and the points on the different layers can not be measured. In order to promote the development of the industrial CT, the three-dimensional reconstruction and analysis system is researched in this paper. The three-dimensional...
In this paper, an interactive lung parenchyma segmentation algorithm is put forward with improved Live-Wire model, Snake model and contour interpolation, which takes full advantage of lung contours' slow change in adjacent CT image layers and operators' professional knowledge. Firstly, we manually select key slices of lung parenchyma in serial CT images, then draw the lung's contours in key slices...
In this paper problem of airway tree segmentation from CT data sets was regarded. The idea of segmentation using 3D seeded region growing algorithm was introduced. Common problems arising during extracting airway tree from CT data using region growing method were explained. Especially problem of leakage from airway into the lung parenchyma was outlined. Common methods of CT data sets preprocessing...
Segmentation of lesions plays an important role in computer-aided diagnostic (CAD) schemes, because the accuracy of segmentation affects the accuracy of the feature extraction and analysis based on segmented lesions, and therefore, the final accuracy of classification. Accurate segmentation is difficult especially for complicated patterns such as lesions overlapping or touching normal structures,...
A novel algorithm is proposed in this paper in terms of the existing extraction algorithms for intracranial structures on cerebral computed tomography (CT) lacking automation. The skull image from CT is characterized by great width and high gray-level, considering that, the proposed algorithm uses linear filtering to extract the outline of skull. and then mathematic morphology and horizontal scanning...
Liver segmentation from CT image is a difficult task due to abdomen apparatus complexity. Snakes, or active contours, are extensively used in medical image segmentation. The automatic generation of initial snake curves and improving snake performance in case of blur edges are still open challenges for liver segmentations. In this paper, texture classification and morphology filter are employed to...
In this paper, an unsupervised approach based on non-linear filtering and region growing techniques to obtain the endocardial surface is proposed. The filtering stage is performed using mathematical morphology operators in order to improve the left ventricle cavity information in multi slice computerized tomography images. A seed point located inside the cardiac cavity is used as input for the region...
Trajectory segmentation is the process of partitioning a given trajectory into a small number of homogeneous segments w.r.t. some criteria. Conventional segmentation techniques only focus on the spatial features of the movement and could lead to spatially homogeneous segments but with presumably dissimilar temporal structures. Furthermore, trajectories could be over-segmented in the presence of outliers...
In this paper a new method of speckle reduction of SAR images in curvelet domain is proposed. In the method, curvelet transform is integrated with wavelet filtering. The new method consists of five parts: preprocessing, curvelet transform (CT), curvelet coefficients processing and two inverse transforms. In the preprocessing step, homomorphic transform is applied to convert multiplicative noise in...
Non-rigid registration between CT and ultrasound images is a difficult task due to the low resolution and contrast of ultrasound images. We present a method incorporating the shape information of contours extracted from the image pairs for the registration. Firstly, the shapes are represented by the automatically detected landmarks along the contour of segmented object on CT and ultrasound images...
This paper presents a novel approach to automatically detect the fracture of skull in CT images. The approach consists of 5 steps: 1) skull segmentation, 2) skull extraction, 3) edge detection, 4) noise removal and, 5) image classification. Experiments show that the recognition rate is 99% for 100 images that are randomly chosen from a medical image database contributed by Hospital Putrajaya, Malaysia...
In this paper, we propose a methodology consists of several unsupervised clustering techniques to acquire a satisfactory segmentation of computed tomography (CT) brain images. The ultimate goal of segmentation is to obtain three segmented images, which are the abnormalities, cerebrospinal fluid (CSF) and brain matter respectively. The proposed approach contains of two phase-segmentation methods. In...
Three-dimensional finite element meshes of human femur are generated using an automatic grid-based method for biomechanical analysis purpose. The meshing algorithm firstly preproceses the CT slices to extract geometrical information of the femur structure, and then organizes a tagged background grid based on the segmented imaging dataset. The surface triangular mesh is constructed by isosurface extraction,...
Recently, we proposed an original approach, in a statistical framework, for fully automatic detection of pathological kidneys in 2D CT images. In this paper, we propose some important improvements of our previous work and an attempt to classify the identified regions into pathological vs non pathological. To this end, we propose two indexing methods to construct the signatures coding the relevant...
Automatic kidney segmentation from abdominal computed tomography (CT) images is a key step in computer-aided diagnosis for kidney CT. However, due to gray levels similarities of adjacent organ??s positions and shapes, automatically identifying abdominal organs has always been a high challenging task. In this paper, we proposed an automatic segmentation method integrated some prior knowledge into traditional...
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