The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
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...
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...
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...
Significant progress has been made in recent years for computer-aided diagnosis of abnormal pulmonary textures from computed tomography (CT) images. Similar initiatives in chest radiographs (CXR), the common modality for pulmonary diagnosis, are much less developed. CXR are fast, cost effective and low-radiation solution to diagnosis over CT. However, the subtlety of textures in CXR makes them hard...
Lung segmentation is the premise of the computer aided by lung disease. Lesions (such as mass and inflammation) lead to loss of large part of normal tissue in the lung region. They are close to the surrounding tissues (such as chest wall and blood vessel) and have similar CT values with surrounding tissues. So the segmentation method based on local feature cannot get the correct results and ASM model...
An important initial step in many medical image analysis applications is the accurate detection of anatomical landmarks. Most successful methods for this task rely on data-driven machine learning algorithms. However, modern machine learning techniques, e.g. convolutional neural networks, need a large corpus of training data, which is often an unrealistic setting for medical datasets. In this work,...
In this paper, we present an indexing structure of data-driven cuboid patterns to speed up the process of liver detection and segmentation from computed tomography (CT) scans using the cube-based generalized Hough transform (CGHT). Most existing approaches to automatic liver segmentation from CT scans use a statistical shape model (SSM) integrated with a searching algorithm to recover the deformation...
We present an open-source 4D (3D+t) statistical shape model of the heart developed as numerical phantom for cone-beam CT simulation. The training set consists of surface meshes from 20 ten-phase-CT angiography data sets extracted using automatic registration-based segmentation. Incorporating 90% of the training set variation, the model exhibits a generalization ability of 5.00 ± 0.93 mm and specificity...
We propose a machine learning-based method to automatically detect flow diverters in cerebral C-arm CT images. An appearance detector is learned to generate hypotheses of a flow diverter's location in a volumetric image. A probabilistic framework incorporating a local appearance and shape model is developed to trace the flow diverter. Promising results have been obtained on clinical data. The proposed...
Machine learning based methods have been widely used for detecting and segmenting various anatomical structures in different medical imaging modalities. The robustness of such approaches is largely determined by the number of training samples. In practice it is often difficult to acquire sufficient training samples for a certain imaging modality. Since multiple imaging modalities are often used for...
This paper presents an accurate object segmentation method using novel active shape and appearance models that evolve according to the output of a support vector machine as well as traditional appearance features at shape landmarks. The method consists of two main processes including the building of the shape and appearance models and support vector machine (SVM) classifier, and the segmentation of...
A novel method for the automatic segmentation of the lung in X-ray computed tomography (CT) images is presented. In this paper, a maximum a posteriori (MAP) estimation framework, combining neighbor prior information and image gray level information, is used to extract the boundary of lung. The relationship of the left lung and the right lung is represented as a joint density function. We use the principal...
In this paper, we propose a clinically desired segmentation method for vertebral bodies (VBs) in computed tomography (CT) images. Three pieces of information (intensity, spatial interaction, and shape) are modeled to optimize a new probabilistic energy functional; and hence to obtain the optimum segmentation. The information of the intensity and spatial interaction are modeled using the Gaussian and...
In this paper, a novel statistical shape modeling method is developed for the vertebral body (VB) segmentation framework. Two-dimensional principle component analysis (2D-PCA) technique is exploited to extract the shape prior. The obtained shape prior is then embedded into the image domain to develop a new shape-based segmentation approach. Our framework consists of four main steps: i) shape model...
Cone-beam CT images are useful in operative dentistry but suffer from a comparatively bad image quality with regard to the signal-to-noise ratio. Therefore, we use a statistical shape model (SSM) for robust segmentation of the mandible. In contrast to previous approaches, our method (i) is fully automatic in terms of both, the establishment of correspondence and the segmentation itself, and (ii) allows...
In this paper a novel articulated atlas for the fully automated segmentation of the skeleton from head & neck CT datasets is presented. An individual atlas describing the shape and appearance is created for each individual bone. Principal Component Analysis is used to learn spatial relations between those atlases resulting in a unified articulated atlas. Transformations are parameterized using...
This paper proposes a novel pose-invariant segmentation approach for left ventricle in 3D CT images. The proposed formulation is modular with respect to the image support (i.e. landmarks, edges and regional statistics). The prior is represented as a third-order Markov Random Field (MRF) where triplets of points result to a low-rank statistical prior while inheriting invariance to global transformations...
Segmentation of liver from volumetric images forms the basis for surgical planning required for living donor transplantations and tumor resections surgeries. This paper introduces a novel idea of using sparse representations of liver shapes in a learned structured dictionary to produce an accurate preliminary segmentation, which is further evolved using a joint image and shape based level-set framework...
In this paper, we present a new dynamic and probabilistic shape based segmentation method using statistical and variational approaches. We use two models in this paper: i) intensity and ii) shape. In the first phase, the intensity based segmentation is done using a basic statistical level set method. In the second phase, to which we contribute, the shape model is constructed using the implicit representation...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.