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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 work, we investigate the performances of segmentation algorithms applied to micro-CT images of small animal to derive anatomical templates for guided Fluorescence Molecular Tomography. We report on the performances of Active Contours methods to retrieve the boundary surface of the specimen and the internal distribution of a fluorescent probe. The spatial information gained from this segmentation...
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...
Human heart anatomy is the study of morphological structures and relationships between morphological structures. Hence such a study when visualized in the form of a two dimensional atlas can hold strong interactive sessions between doctors and medical students. Atlas is a visualization technique developed for understanding the morphological structures of a human body. Interacting directly with morphological...
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 the context of vessel tree structures segmentation with implicit deformable models, we propose to exploit convolution surfaces to introduce a novel variational formulation, robust to bifurcations, tangential vessels and aneurysms. Vessels are represented by an implicit function resulting from the convolution of the centerlines of the vessels, modeled as a second implicit function, with localized...
Micro-tomography produces high resolution images of biological structures such as vascular networks. In this paper, we present a new approach for segmenting vascular network into pathological and normal regions from considering their micro-vessel 3D structure only. We define and use a conditional random field for segmenting the output of a watershed algorithm. The tumoral and normal classes are thus...
A novel interactive segmentation method based on distance metric learning is proposed for segmentation of tumors in CT and MRI images. Firstly, the moments of the gray-level histogram are extracted as the image features for segmentation. Then, Neighborhood Components Analysis is employed to learn a task-specific distance metric in the feature space using the interactive inputs. The probability of...
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...
Breast cancer is the most common non-skin cancer and the second leading cause of cancer-related death in women here in the United States. Mammography is the current standard clinical imaging modality for breast cancer screening and diagnosis, and mammographic breast density (i.e. the percentage of the entire breast volume that is taken up by dense glandular tissue) has been shown to be a biomarker...
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...
Bone fragility involved in diseases such as osteoporosis implicates many mechanisms at the cellular level. It was recently shown that the lacunar-canalicular network interconnecting osteocytes has a major role in mechanosensitivity. So far, this system has only been studied from 2D microscopic images. In a previous work, we demonstrated the feasibility of synchrotron radiation micro-CT with a voxel...
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...
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