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Nowadays, the lung lobe segmentation is the most basic step in Lung CAD (Computer-aided diagnosis) and is playing an increasingly important role in the early diagnosis of lung diseases and the analysis of pulmonary functions. The key to achieving lung lobe segmentation is to detect and locate lung fissures. With the wide applications of HRCT (High-Resolution Computed Tomography), CT data with higher...
A vital task in the planning of peripheral bronchoscopy is the segmentation of the airway tree from a 3-D multidetector computed tomography chest scan. Unfortunately, existing methods typically do not sufficiently extract the necessary peripheral airways needed to plan a procedure. We present a robust method that draws upon both local and global information. The method begins with a conservative segmentation...
The first step for computer-aided diagnosis for liver of CT scans is the identification of liver region. To deal with multislice CT scans, automatic liver segmentation is required. In this paper, we propose a liver segmentation algorithm using hybrid techniques by combining morphological-based, region-based and histogram-based techniques to segment volumetric CT data. A morphological-based technique...
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...
This paper studies Self-Organizing Map (SOM) based adaptive thresholding technique for semi-automatic image segmentation. CT images of patients with nasopharyngeal carcinoma are considered in the study. The thresholds are determined from histogram of a topological map created from SOM method. With this proposed technique, initial tumor pixel must be manually selected. Pixels which are in the same...
In this paper, we propose an improved model, called three-dimensional (3-D) level set method, for active contours to detect maxillary sinuses in cone beam computed tomography(CBCT) images. This method is based on the techniques of two-dimensional(2-D) level set method and active contour without edges (or Chan-Vese) method. This model can detect the maxillary sinus correctly not only in normal cases,...
An automated system is developed for lung and mediastinum segmentation in lung CT (Computed Tomography) images for the purpose of using these segmentations not only in CT images but also in PET (Positron Emission Tomography) images to exploit the useful integration of the CT and PET images performed by the highly valuable oncological equipment PET/CT. Segmentation is the most crucial step in a CAD...
This paper presents a semiautomatic method to detect the coronary arteries in X-ray CT angiography (CTA) images with simple user interaction. The algorithm is started with performing modified Otsu threshold method and clustering of superpixel to reconstruct the image. The interactive graph cut segmentation and an improved top-hat filter is used to extract the coronary artery in series of two dimension...
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...
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,...
CT angiography (CTA) is a useful tool for diagnosis on vessel diseases. To get high quality images, a dose of high contrast material (bolus) is injected into a patient's body. In recent years, bolus-chasing CTA has been introduced to capture better images. The CT table in bolus-chasing CTA can be moved according to the real speed of bolus. Segmenting the bolus boundary in each cross-section image...
Metallic implants may cause severe artifacts in CT exams. Suppression of metal artifacts remains to be a very challenging problem, in which metal region segmentation is one of the most important steps. We proposed a novel, semiautomatic segmentation algorithm based on the dual-front active contour model and the boundary mapping strategy to detect the metal regions on reformatted projection data, and...
Cancer is one of the most serious health problems in the world. Lung Computer-Aided Diagnosis (CAD) is a potential method to accomplish a range of quantitative tasks such as early cancer and disease detection, analysis of disease progression, analysis of pulmonary function and perfusion, and automatic identification and tracking of implanted devices. For identifying the lung diseases, computed tomography...
Simulating physiological interventions for planning purposes requires an accurate virtual liver model as computation input. To best meet the demands the data acquisition has to be oriented on image processing purposes. We provide a CT imaging protocol which makes it possible to extract much more vessels with the same segmentation algorithms than when using them on data sets from the clinical routine...
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...
The measurements from registered images obtained from cone beam computed tomography (CBCT) and a photogrammetric sensor are used to track three-dimensional shape variations of orthodontic patients before and after their treatments. The methodology consists of five main steps: (1) the patient's bone and skin shapes are measured in 3D using the fusion of images from a CBCT and a photogrammetric sensor...
Computed tomography (CT) is used for the attenuation correction of positron emission tomography (PET) to enhance the efficiency of data acquisition process and to improve the quality of the reconstructed PET data in the brain. Due to the use of two different modalities, chances of misalignment between PET and CT images are quite significant. The main cause of this misregistration is the motion of...
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...
Liver segmentation on computed tomography (CT) images is a challenging task due to the anatomic complexity and the imaging system noises. In this paper a complex algorithm based on active contour is proposed to automatically extract the liver region in abdominal CT images. Combined with threshold segmentation, morphology image processing and active contour models, we can automatically extract the...
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