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The measurement of residual thyroid tissue after thyroidectomy is crucial for the precise quantification of thyroid cancer treatment. Accurate residual thyroid tissue segmentation from CT images is challenging due to the indistinct tissue boundary. We propose a vote-in & vote-out region propagation model for residual thyroid tissue segmentation which incorporates global and local constraints and...
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...
Segmentation of organs at risk in CT volumes is a prerequisite for radiotherapy treatment planning. In this paper we focus on esophagus segmentation, a challenging problem since the walls of the esophagus have a very low contrast in CT images. Making use of Fully Convolutional Networks (FCN), we present several extensions that improve the performance, including a new architecture that allows to use...
The proposed system aims to detect the lung nodules from a series of CT scan images. Otsu's thresholding and morphological operations are applied for nodules segmentation. After segmentation, the objects that do not hold the possibility to be nodules are removed. Geometric, histogram as well as texture features are then extracted for benign and malignant nodules classification. Multilayer Perceptron...
Detection and segmentation of small renal mass (SRM) in renal CT images are important pre-processing for computer-aided diagnosis of renal cancer. However, the task is known to be challenging due to its variety of size, shape, and location. In this paper, we propose an automated method for detecting and segmenting SRM in contrast-enhanced CT images using texture and context feature classification...
We propose a new semi-automatic framework for tooth segmentation in Cone-Beam Computed Tomography (CBCT) combining shape priors based on a statistical shape model and graph cut optimization. Poor image quality and similarity between tooth and cortical bone intensities are overcome by strong constraints on the shape and on the targeted area. The segmentation quality was assessed on 64 tooth images...
Aortic dissection is a condition in which a tear in the inner wall of the aorta allows blood to flow between two layers of the aortic wall. Aortic dissection is associated with severe chest pain and can be deadly. Contrast-enhanced CT is the main modality for detection of aortic dissection. Aortic dissection is one of the target abnormalities during evaluation of a triple rule-out CT in emergency...
Mandible bone segmentation from computed tomography (CT) scans is challenging due to mandible's structural irregularities, complex shape patterns, and lack of contrast in joints. Furthermore, connections of teeth to mandible and mandible to remaining parts of the skull make it extremely difficult to identify mandible boundary automatically. This study addresses these challenges by proposing a novel...
Positron Emission Tomography (PET) using 18F-FDG is recognized as the modality of choice for lymphoma, due to its high sensitivity and specificity. Its wider use for the detection of lesions, quantification of their metabolic activity and evaluation of response to treatment demands the development of accurate and reproducible quantitative image interpretation tools. An accurate tumour delineation...
CT image based lung nodule detection is the most widely used and accepted method for detecting lung cancer. Most CT image based methods are based on supervised/unsupervised learning, which has a high number of false positives and needs a large amount pre-segmented training samples. This problem can be solved, if a set of optimally small number of training samples can be created, where each sample...
The liver shapes are complex, pathological changes severely affect the liver shapes. In order to realize the segmentation of the boundary of liver in CT images, the liver shapes dictionary is built, the input CT images are sparse represented by the angular points in gold standard liver boundary dictionary, and the best matches is selected to be the final segmentation result. Experimental results show...
The paper introduces a novel model-guided method for liver segmentation in CT and PET-CT images. Using a model liver volume as a template and a liver shape annotated in one of the patient slices, it automatically segments the whole liver volume in the patient dataset. The method is based on non-deformable registration of the model volume to the patient data and combination of components pre-segmented...
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...
Region of Non-Interest (RONI) based digital watermarking has attracted much attention recently for medical image (MI) applications. The legal and ethical concern of medical professionals grow with watermark embedding as it erratically modifies the MIs. To minimize this concern, RONI segmentation is an essential process of MI watermarking that confines the embedding region. However, finding a suitable...
A framework for 3D kidney segmentation from abdominal computed tomography (CT) images is proposed. Accurate kidney segmentation from CT images is a challenging task due to the large inhomogeneity of the kidney (e.g., cortex and medulla), inter-patient anatomical differences, etc. To account for these challenges, a novel framework utilizing random forest (RF) classification that has the ability to...
This paper proposes a novel framework for the identification of the radiation-induced lung injury (RILI) after radiation therapy (RT) using 4D computed tomography (CT) scans. The proposed methodology consists of four components: (i) elastic image registration; (ii) segmentation of the lung fields; (iii) extraction of functional and texture features; and (iv) classification of the lung tissues. The...
Due to the weak boundary, narrow or even disappeared joint space and varying topology in challenging CT, accurate segmentation of the femur from hip joint is still a difficult task. To address this problem, the proposed method combines anatomical information of relative location of bone tissues and neighboring slices to predict a statistical model for Joint space identification. A novel idea in this...
For detecting pleural effusion and pneumothorax, which affect the pleural membranes of the lungs, Computer Aided Diagnosis system (CAD) is proposed. The chest CT slices are initially preprocessed to remove the Gaussian noise by using a sigma filter segmentation technique used here extracts the lungs and the regions affected by pleural effusion using conventional thresholding techniques like Otsu's...
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...
Knowledge of vertebra location, shape and orientation is crucial in many medical applications such as orthopedics or interventional procedures. The wide range of shapes, joint alterations and pathological cases encountered in an aging population makes automatic segmentation sometimes challenging. This paper presents a new automated vertebra segmentation method for 3D CT data which tackles these problems...
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