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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 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...
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...
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...
Radiation therapy is one of the most effective options used in the treatment of about half of all people with cancer. A critical goal in radiation therapy is to deliver optimal radiation doses to the observed tumor while sparing the surrounding healthy tissues. Radiation oncologists typically manually delineate normal and diseased structures on three-dimensional computed tomography~(3D-CT) scans....
Automatic hepatic tumor segmentation is a crucial step for diagnosis and surgery planning. This paper presents a new fully automatic technique to segment the tumors in liver structure with no interaction from user. Contrast enhancement is applied to the slices of segmented liver, then adding each image to itself to have a white image with some pepper noise and tumors as dark gray spots. After applying...
The liver is a common site for the occurrence of tumors. Automatic hepatic lesion segmentation is a crucial step for diagnosis and surgery planning. This paper presents a new fully automatic technique to segment the tumors in liver structure with no interaction from user. Contrast enhancement is applied to the slices of segmented liver, then adding each image to itself to have a white image with some...
Liver cancer causes the majority of primary malignant liver tumors among adults. Computed Tomography (CT) scans are generally used to make the treatment plan or to prepare for ablation surgery. Processing CT image includes the automatic diagnosis of liver pathologies, such as detecting lesions and following vessels ramification, and 3D volume rendering. This paper presents a new fully automatic method...
A new active contouring algorithm for lung tumor segmentation in 3D-CT image data, based on a mixed internal-external force and on a cluster function, is presented. The purpose of the project is to define the gross tumor volume (GTV) for irradiation in cancer therapy. For a more dedicated segmentation and to simplify the algorithms five different models of tumors were defined.
Attenuation correction (AC) is an important prerequisite for quantitative brain PET in MR-BrainPET systems. The new knowledge-based method segments attenuation-differing head regions solely based on the routinely acquired T1-weighted MR data set of the patient's head. The original approach (O) was extended (E1-E3) with regard to the MR image quality at different bandwidth/voxel (130 HZ/voxel, 610...
Volume measurement of liver tumor is an important task for surgical planning and cancer following-up. The computation of this volume requires an efficient liver tumor segmentation method. This work deals with liver tumor segmentation from computed tomography (CT) images. We are interested by HMRF-EM classification method. This method considers the spatial information given by voxel neighbors. A Bootstrap...
Accurate three-dimensional tumor localization in radiotherapy is critical to the treatment outcome, particularly when high dose gradients are present. Digital tomosynthesis (DTS) is a method to reconstruct three-dimensional (3D) images from two-dimensional (2D) x-ray projections acquired over limited scan angles. In this study, we have used a Feldkamp-type approach to reconstruct DTS images. The accuracy...
MR images acquired from open magnetic resonance system have been applied to guide percutaneous puncture for ablation of liver tumors recently. However, the MR images do not always show the tumors clearly because of the lower magnetic field (0.5T) and various different surgical and pathological conditions. In our study, we use preoperative CT images to assist locate tumors by registration. In our method,...
Advances in radiotherapy technology and the development of intensity modulated radiation therapy (IMRT) planning and delivery systems have led to the emergence of image-guided and adaptive radiotherapy (IGART) to meet the new requirements of precise localization and definition of tumor targets while sparing surrounding critical structures during radiation delivery. While these technological advances...
Medical imaging, used for both diagnosis and therapy planning, is evolving towards multi-modality acquisition protocols. Manual segmentation of 3D images is a tedious task and prone to inter- and inter-experts variability. Moreover, the automatic segmentation exploiting the characteristics of multi-modal images is still a difficult problem. In this paper, we propose the use of a variational segmentation...
In clinical routine of liver surgery there are a multitude of risks such as vessel injuries, blood loss, incomplete tumor resection, etc. In order to avoid these risks the surgeons perform a planning of a surgical intervention. A good graphical representation of the liver and its inner structures is of great importance for a good planning. In this work we introduce a new planning system for liver...
In this paper, we propose a new method to detect liver tumors in CT images automatically. The proposed method is composed of two steps. In the first step, tumor candidates are extracted by EM/MPM algorithm; which is used to cluster liver tissue. To cluster a dataset, EM/MPM algorithm exploits both intensity of voxels and labels of the neighboring voxels. It increases the accuracy of detection, with...
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...
Attenuation correction of PET data acquired in new hybrid MR-PET scanners which do not offer the possibility of a measured attenuation correction can be done in different ways. A previous report of our group described a method which used attenuation templates. The present study utilizes a new knowledge-based segmentation approach applied on T1-weighted MR images. It examines the position and the tissue...
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