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Pulmonary nodules are the potential manifestation of lung cancer. Margin sharpness of a nodule is an important imaging characteristic for estimation of its malignancy. Quantitative evaluation of margin sharpness could be helpful in the development of content-based image retrieval system of pulmonary nodules and computer-aided diagnosis system for lung cancer. In this article, margin sharpness of a...
This study proposes an umbrella deployment of swarm intelligence algorithm, such as stochastic diffusion search for medical imaging applications. After summarising the results of some previous works which shows how the algorithm assists in the identification of metastasis in bone scans and microcalcifications on mammographs, for the first time, the use of the algorithm in assessing the CT images of...
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...
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...
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...
An alternative method of diagnosing malignant lung nodules by their shape rather than conventional growth rate is proposed. The 3D surfaces of the detected lung nodules are delineated by spherical harmonic analysis that represents a 3D surface of the lung nodule supported by the unit sphere with a linear combination of special basis functions, called spherical harmonics (SHs). The proposed 3D shape...
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...
Active geometric functions were recently introduced as a tool to perform real time 3-D segmentation. In the present paper we propose a B-spline formulation of the problem, which further improves the computational efficiency of the algorithm. We also introduce local region based energies, overcoming the limitations of the original method. The feasibility of real-time 3D segmentation in challenging...
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.
A pulmonary nodule is the most common sign of lung cancer. The proposed system efficiently predicts lung tumor from Computed Tomography (CT) images through image processing techniques coupled with neural network classification as either benign or malignant. The lung CT image is denoised using non-linear total variation algorithm to remove random noise prevalent in CT images. Optimal thresholding is...
Allowing the early detection of metabolic changes during treatment, Positron Emission Tomography (PET) is a promising tool for therapy response assessment. A therapeutic response is usually defined as variations of semi-quantitative parameters such as standardized uptake value (SUV) measured in PET scans performed during the treatment. However, this approach does not take into account volume variation...
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...
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