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Automatic liver segmentation from abdominal Computed Tomography (CT) is an important step for hepatic disease diagnosis. It is a challenging task owing to the similarity between liver and its adjacent organs and the low contrast of liver texture (e.g. tumors and blood veins). In this paper, we propose a cascaded structure to automatically segment liver in CT scans. First, we train a fully convolutional...
In this paper, a novel kidney segmentation method for Computed Tomography patient data with kidney cancer is proposed. The segmentation process is based on Hybrid Level Set method with elliptical shape constraints. Using segmentation results, a fully automated technique of kidney region classification is introduced. Identification of the kidney, tumor and vascular tree is based on RUSBoost and the...
Among five main type of cancer lung cancer is one of causing health hazards in both men and women all over the world. Advanced techniques of Computed Tomography and medical images play an important role in clinically detection of lung cancer tumors in all TNM stages. Efficient Computer Aided Detection (CADe) systems help the radiologist in early detection and diagnosis of lung cancer. The objective...
Automatic tumor detection and segmentation is essential for the computer-aided diagnosis of live tumors in CT images. However, it is a challenging task in low-contrast images as the low-level images are too weak to detect. In this paper, we propose a new method for the automatic detection of liver tumors. We first adaptively enhance the intensity contrast of CT images by probability density function...
This paper presents a novel stochastic level set method for the longitudinal tracking of lung tumors in computed tomography (CT). The proposed model addresses the limitations of registration based and segmentation based methods for longitudinal tumor tracking. It combines the advantages of each approach using a new probabilistic framework, namely Chance-Constrained Programming (CCP). Lung tumors can...
Advanced liver surgery requires a precise pre-operative planning, where liver segmentation and remnant liver volume are key elements to avoid post-operative liver failure. In that context, level-set algorithms have achieved better results than others, especially with altered liver parenchyma or in cases with previous surgery. In order to improve functional liver parenchyma volume measurements, in...
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...
In follow-up CT examinations of cancer patients, therapy success is evaluated by estimating the change in tumor size from diameter or volume comparison between corresponding lesions. We present an algorithm that automatizes the detection of matching lesions, given a baseline segmentation mask. It is generally applicable and does not need an organ mask or CAD findings, only a coarse registration of...
Medical image registration methods based on the maximization of mutual information have shown promising results. But it needs much computing time. This paper presents a new method to rigid registration based on the combination of scale images and mutual information. Firstly, the concept of area morphology is introduced. And then, the scale images are constructed with the area morphological operators...
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