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The function of medical image mining in computer-aided diagnoses is discussed. Aiming at the hospital of valuable CT images, combining medical peculiar domain knowledge, a kind of association rules based on rough sets and combination of mining method is proposed, this method can help doctors early diagnose malignant diseases and has a great significance.
In this paper, we present a novel framework to segment and quantify stenosed coronary arteries in 3D contrast enhanced computed tomography angiography (CTA). According to our knowledge, no commercially available software package permits fully automated detection and assessment of atherosclerotic stenosis. Therefore, in clinical practice, the radiologist has to make a detailed evaluation, segment by...
Extracting hepatic vasculature from three dimensional imagery is important for diagnosis of liver disease and planning of liver surgery. In this paper we propose a method for generation of 3D skeletal graph of liver vessels using thinning algorithm and graph theory. First of all, basic methodology in the proposed method is introduced. Secondly, the skeletonization method together with a pre-processing...
Motion correction of the abdominal-thoracic region is one of the main research challenges in tomographic nuclear medicine imaging. We address this issue with a flexible data-driven method of motion correction. This uses marker-less stereo tracking of the anterior abdominal-chest surface and a 'virtual dissection'-based registration approach, combined within a novel paricle filtering (PF) framework...
By taking the advantages of the Voronoi-based segmentation method in detecting the thin edges and the region growing segmentation method in segmenting regions more completely, we proposed a novel segmentation algorithm by combining the Voronoi diagrams and region growing to deal with the medical images in this paper. Firstly, the region growing segmentation algorithm is used to produce a rough partition,...
The inner thoracic region consists of several important anatomical structures and an accurate delineation of this region is an essential step for various biomedical image analysis applications. In this paper, we present a fully automatic graph-based method for the delineation of the inner thoracic region in non-contrast cardiac CT data. In particular, we reformulate the problem of delineating the...
In spite of the advancement and proliferation of cardiovascular imaging data, the rate of deaths due to unpredicted heart attack remains high. Thus, it becomes imperative to develop novel computational tools to mine quantitative parameters from imaging data for early detection and diagnosis of asymptomatic cardiovascular disease. In this paper, we present our progress towards developing a computational...
Recent developments in medical imaging technology have enabled us to acquire high-resolution datasets within a few minutes. It is important for a physician to recognize three-dimensional structure of vessels prior to any vascular treatments. However, extracting this structure is not a simple image processing task. In this paper, we propose an algorithm to extract hepatic artery from CT datasets through...
This paper proposes an automated method to identify abnormalities by exploiting symmetrical property features in computed tomography (CT) brain images. This method consists of two main steps; symmetrical axis detection and rule based abnormalities detection. Based on the principle axis theorem, any tilted intracranial is firstly corrected before symmetrical axis is generated. Then, segmented CT brains...
A challenge in content-based retrieval of image exams is to provide a timely answer that complies to the specialist's expectation. In many situations, when a specialist gets a new image to analyze, having information and knowledge from similar cases can be very helpful. However, the semantic gap between low-level image features and their high level semantics may impair the system acceptability. In...
Sliding effects often occur along tissue/organ boundaries. For instance, it is widely observed that the lung and diaphragm slide against the rib cage and the atria during breathing. Conventional homogeneous smooth registration methods fail to address this issue. Some recent studies preserve motion discontinuities by either using joint registration/segmentation or utilizing robust regularization energy...
In this paper, it is provided to reconstruct three-dimensional(3D) models of human body by using CT slices and digital images and precisely finding locations of pathological formations such as tumours. 3D image CT reconstruction is an attractive field generally in digital image processing techniques, especially in biomedical imaging. It is necessary to incise a 3D object to obtain detail structure...
Accurate liver segmentation on computed tomography (CT) images is a challenging task because of inter and intra- patient variations in liver shapes, similar intensity with its nearby organs. We proposed a liver segmentation method based on region growing approach. First of all, basic theory of region growing approach is introduced. Secondly, a pre-processing method using anisotropic filter and Gaussian...
We have recently proposed a new level-set formulation, where the level-set is modelled as a continuous parametric function expressed in a B-spline basis. We propose in this paper to adapt this formalism to the class of narrow-band level-set methods, where the implicit function evolves only around its zero-level. For this purpose, we propose to model the interface by two lists of boundary points and...
A method was proposed in order to process and classify CT slices representing funnel chest deformities. A manually chosen CT slice was processed to detect the inner curvature of the chest for characterization. Normalized data from the detected inner curvature was gained and saved next to a manually-given deformity type for further classifications. Based on the multiple correlations of the values gained...
In diagnosing pulmonary diseases aided by computer, accurate segmentation of the airway tree from the CT images is the basis for subsequent processing and analyzing. It is still a challenging task due to the image noise, partial volume effect and texture similarity of the airway and parenchyma. In order to solve these problems, various algorithms have been proposed, among which the region growing...
Many nodule measurement methods rely on accurate segmentation of the nodule and may fail with complex nodule morphologies; often slight variations in segmentation result in large volume differences. A method, growth analysis from density (GAD), is presented that measures nodule growth without explicit segmentation through the application of a Gaussian weighting function to a region around the nodule,...
In this study, we used multi-detector computed tomographic (MDCT) images of human left ventricles at end-diastole and end-systole to perform quantitative analysis and comparison of heart motion in patients with anterior wall myocardial infarction and ischemic cardiomyopathy (ICM) versus those with global non-ischemic cardiomyopathy (NICM). MDCT ventricular images of 25 patients (13 with ICM) with...
At present, there are two main types for medical CT image surface reconstruction: one is way of the slice contour, the other is voxel reconstruction, the former method is simple, easy for calculation and much classical computer graphics techniques can be used, the latter is related to complex computing. In this paper, image contours are extracted by active contour model, simplified by DP algorithm...
This paper presents a new lung segmentation algorithm which is based on anatomical knowledge and Snake model. This algorithm totally overcomes the disadvantage of traditional lung segmentation algorithms, which are mainly based on edge extraction, mathematical morphology, region growing, threshold, etc.; and can't get satisfied results when segmenting pathological clinical CT images with traditional...
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