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Lung vessel segmentation of computed tomography (CT) images is important in clinical practise and challenging due to difficulties associated with minor size and blurred edges of lung vessels. A vessel segmentation method is proposed for lung images based on a random forest classifier and sparse auto-encoder features. First, the multi-scale representations of lung images are obtained using the Gaussian...
Detection of implanted iodine-125 seeds in postoperative CT is a necessary step for evaluating the output of seed implantation brachytherapy of lung tumor. In this paper, we propose a semi-automated method to detect implanted seeds in postoperative lung CT. Three main steps are included in our approach. Firstly, the ROI (Region Of Interest) containing all seeds is extracted from the original image...
In the recent years, reconstructing 3D liver and its vessels from abdominal CT volume images becomes an inevitable and necessary research field. In this paper, a method of 3D reconstruction of liver with its vessels has been implemented, which involves volume preprocessing, de-noising, segmentation, contouring, and combination of different modalities. An advanced liver segmentation algorithms have...
For lung tissue adhesion of the situation in the lung CT images, based on the classical watershed algorithm, this introduce line-encoded ideology, use contour tracing method to determine adhesion region segmentation points, according to segment code to get the split line, thus separating the adhesion region. Experimental results show that this method will be effective in the lung adhesion region segmentation,...
We present a new frame of lung parenchyma segmentation. Optimal threshold value method and the boundary tracking method are used to get rid of the background interference and segment the lung region. Then new algorithm is used for lung region boundary repairing based on the mathematical morphology method. The experimental results show the new algorithm can segment lung regions from the chest CT images...
In this paper 2D Otsu algorithm based on particle swarm optimization (PSO) is proposed to segment CT lung images. This method can extract pulmonary parenchyma from multisliced CT images, which is primary step to detect the pulmonary disease such as lung cancer, tumor, and mass cells. In the automated pulmonary disease diagnosis, image segmentation plays an important role and image analysis result...
Modern multislice computed tomography (CT) scanners produce isotropic CT images with a thickness of 0.6 mm. These CT images offer detailed information of lung cavities, which could be used for better surgical planning of treating lung cancer. The major challenge for developing a surgical planning system is the automatic segmentation of lung lobes by identifying the lobar fissures. This paper presents...
Accurate prostate localization is the key to the success of radiotherapy. It remains a difficult problem for CT images due to the low image contrast, the prostate motion, and the uncertain presence of rectum gas. In this paper, a learning based framework is proposed to improve the accuracy of prostate detection in CT. It adaptively determines distinctive feature types at distinctive image regions,...
In prostate cancer radiotherapy, accurate segmentation of prostate and organs at risk in planning CT and follow-up CBCT images is an essential part of the therapy planning and optimization. Automatic segmentation is challenging because of the poor constrast in soft tissues. Although atlas-based approaches may provide a priori structural information by propagating manual expert delineations to a new...
In this paper, we propose a robust level sets method to segment vertebral bodies (VBs) in clinical computed tomography (CT) images. Since the VB and surrounding organs have very close gray level information and there are no strong edges in some CT images, the initialization of level sets method becomes very crucial step. If the object and background regions are not initialized correctly, the results...
This work presents a computer-aided detection (CAD) system to aid radiologists in finding sclerotic bone metastases in the spine on CT images. The spine is first segmented using thresholding, region growing and a vertebra template. A watershed algorithm and a merging routine segment potential lesion candidates in each two-dimensional (2-D) axial CT image. Next, overlapping 2-D detections on sequential...
To diagnose the osteoporosis accurately, the bone mineral density (BMD) measurements of the vertebral bodies (VBs) are required. In this paper, we propose a new segmentation and registration method in order to assist the BMD measurements and fracture analysis (FA) accurately. In this experiment, image appearance and shape information of VBs are used. Our shape model is required to be registered to...
This study presents a computer-aided detection (CADe) system of hepatocellular carcinoma (HCC) using sequential forward floating selection (SFFS) method with linear discriminant analysis (LDA). We extracted morphologic and texture features from the segmented HCC candidate regions from the arterial phase (AP) images of the contrast-enhanced hepatic CT images. To select the most discriminatory features...
Pleural effusions are accumulations of fluid in the pleural space, usually associated with atelectasis of the adjacent lung. We have previously presented an automated method to measure the volume of pleural effusions on chest CT images. This paper presents an improved version of the same method, which adds 3D surface modeling and additional propagation of the segmentation in the inferior direction...
This paper addresses the clinically challenging problem of hairline mandibular fracture detection from a sequence of Computed Tomography (CT) images. A hairline fracture of critical clinical importance, can be easily missed due to the absence of sharp surface and contour discontinuities and the presence of intensity inhomogeneity in the CT image, if not scrutinized carefully. In this work, the 2D...
Multi-organ localization is required for many automated abdominal organ analysis tasks. We recently developed an automated organ localization method, which used an MAP framework, and applied it to non-contrast CT images. This method failed to localize smaller organs such as kidneys in some image data because it did not respect the spatial relationship among multiple organs. To address the problem,...
Three-dimensional X-Ray Micromotomography (3D μCT) has become an important tool to investigate bone morphology. Several investigators have searched a standard method for determining the optimal threshold value (optimal TH) to segment microtomographic images and quantify the bone morphology. The Conventional methods are based on subjective methods, and it is possible to obtain under or overestimated...
Pancreas segmentation in 3-D computed tomography (CT) data is of high clinical relevance, but extremely difficult since the pancreas is often not visibly distinguishable from the small bowel. So far no automated approach using only single phase contrast enhancement exist. In this work, a novel fully automated algorithm to extract the pancreas from such CT images is proposed. Discriminative learning...
Manual delineations by experts are often used as reference standards for validating segmentation algorithms, although it is well known that they always show some degree of variability. Our goal is to estimate the effects of using a limited number of expert segmentations. Given ten manual delineations of 13 liver tumors, we analyzed the volume error made by randomly selecting subsets of the ten segmentations...
While many computer-aided detection (CADe) systems for CT colonography can detect polypoid lesions at a high sensitivity level, very few are targeted toward detecting flat lesions. Research has shown that flat lesions are more likely to contain carcinoma than polypoid lesions; therefore, it is imperative that they be adequately detected in screenings and examinations. However, current CADe systems...
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