In medical imaging, the availability of robust and accurate automatic segmentation methods is very important for a user-independent and time-saving delineation of regions of interest. In this work, we present a new variational formulation for multiclass image segmentation based on active contours and probability density functions demonstrating that the method is fast, accurate, and effective for MRI brain image segmentation. We define an energy function assuming that the regions to segment are independent. The first term of this function measures how much the pixels belong to each class and forces the regions to be disjoint. In order for this term to be outlier-resistant, probability density functions were used allowing to define the structures to be segmented. The second one is the classical regularization term which constrains the border length of each region removing inhomogeneities and noise. Experiments with synthetic and real images showed that this approach is robust to noise and presents an accuracy comparable to other classical segmentation approaches (in average DICE coefficient over 90% and ASD below one pixel), with further advantages related to segmentation speed.
Abstract Image segmentation using a region-based active contour model could present difficulties when its noise distribution is unknown. To overcome this problem, this paper proposes a novel region-based model for the segmentation of objects or structures in images by introducing a local similarity factor, which relies on the local spatial distance within a local window and local intensity difference to improve the segmentation results. By using this local similarity factor, the proposed method ...
Abstract Background The segmentation of brain tissue into cerebrospinal fluid, gray matter, and white matter in magnetic resonance imaging scans is an important procedure to extract regions of interest for quantitative analysis and disease assessment. Manual segmentation requires skilled experts, being a laborious and time-consuming task; therefore, reliable and robust automatic segmentation methods are necessary. New method We propose a segmentation framework based on a Conditional Random Field...
An alternative Active Contour Model solution for medical images is introduced.A multi-population Cuckoo Search Strategy (MCSS) is implemented to boost ACM.Proposed method was applied on Magnetic Resonance Imaging (MRI) data.MCSS outperforms traditional ACM and ACM driven by multi-population PSO. In this paper, an alternative Active Contour Model (ACM) driven by Multi-population Cuckoo Search (CS) algorithm is introduced. This strategy assists the converging of control points towards the global m...
This paper proposes a novel region-based active contour model in the level set formulation for medical image segmentation. We define a unified fitting energy framework based on Gaussian probability distributions to obtain the maximum a posteriori probability (MAP) estimation. The energy term consists of a global energy term to characterize the fitting of global Gaussian distribution according to the intensities inside and outside the evolving curve, as well as a local energy term to characterize...
#1Van-Truong Pham(NCU: National Central University)H-Index: 10
#2Thi-Thao Tran(NCU: National Central University)H-Index: 9
Abstract This paper presents a new active contour model for left ventricle segmentation in cardiac magnetic resonance (MR) images. In this study, the shape prior is coupled with intensity information in the proposed energy functional which includes a data term and a shape term. The data term, inspired from a region-based active contour model, is used to guide the motion of the initial curve to desired object boundaries. Meanwhile, the shape term is utilized to constrain the evolving contour with...
#2Jinxiao Pan(North University of China)H-Index: 3
Abstract The variational level set approaches are widely used in image segmentation. To extract the objects from the inhomogeneity image, an improved local and global binary fitting active contours model is presented in this paper. The energy functional has been defined by a local intensity fitting term and a global intensity fitting term. The generalized Gaussian distribution has been used as the kernel function of the local binary fitting information. The experiment results of the homogeneity ...
Last. Isabelle Bloch(IMT: Institut Mines-Télécom)H-Index: 135
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This paper presents a novel variational segmentation framework combining shape priors and parametric intensity distribution modeling for extracting the fetal envelope on 3D obstetric ultrasound images. To overcome issues related to poor image quality and missing boundaries, we inject three types of information in the segmentation process: tissue-specific parametric modeling of pixel intensities, a shape prior for the fetal envelope and a shape model of the fetus’ back. The shape prior is encoded...
Last. Mikael Persson(Chalmers University of Technology)H-Index: 27
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This paper presents a novel fully automated unsupervised framework for the brain tissue segmentation in magnetic resonance (MR) images. The framework is a combination of Bayesian-based adaptive mean shift, a priori spatial tissue probability maps and fuzzy c-means. Mean shift is employed to cluster the tissues in the joint spatial-intensity feature space and then a fuzzy c-means is applied with initialization by a priori spatial tissue probability maps to assign the clusters into three tissue ty...
#1Ali Ahmadvand(IUST: Iran University of Science and Technology)H-Index: 8
#2Mohammad Reza Daliri(IUST: Iran University of Science and Technology)H-Index: 19
Image segmentation is one of the important parts in medical image analysis. Markov random field (MRF) is one of the successful methods for MRI image segmentation, but conventional MRF methods suffer from high computational cost. MRI images have high level of artifacts such as Partial Volume Effect (PVE), intensity non uniformity (INU) and other noises, so using global optimization methods like simulated annealing (SA) for optimization step is more appropriate than other local optimization method...
#2Scott T. Acton(UVA: University of Virginia)H-Index: 39
We propose a novel region based segmentation method capable of segmenting objects in presence of significant intensity variation. Current solutions use some form of local processing to tackle intra-region inhomogeneity, which makes such methods susceptible to local minima. In this letter, we present a framework which generalizes the traditional Chan-Vese algorithm. In contrast to existing local techniques, we represent the illumination of the regions of interest in a lower dimensional subspace u...
To detect a brain tumor and its growth from MRI of brain image is one of the most common techniques in medical research field. From the internal structure of human brain, the scanning process of brain image gives more detailed information about the growth of brain tumor. Despite many years of research and substantial contributions, brain MRI segmentation is still a very challenging task to suit for range of diagnosis. It has been generalized to detect the brain tumor manually from medical image,...
Last. Robert J. Zawadzki(UC Davis: University of California, Davis)H-Index: 46
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Abstract Background and objective: Automatic segmentation of retinal blood vessels makes a major contribution in CADx of various ophthalmic and cardiovascular diseases. A procedure to segment thin and thick retinal vessels is essential for medical analysis and diagnosis of related diseases. In this article, a novel methodology for robust vessel segmentation is proposed, handling the existing challenges presented in the literature. Methods: The proposed methodology consists of three stages, pre-p...
BACKGROUND For the segmentation of medical imaging data, a multitude of precise but very specific algorithms exist. In previous studies, we investigated the possibility of segmenting MRI data to determine cerebrospinal fluid and brain volume using a classical machine learning algorithm. It demonstrated good clinical usability and a very accurate correlation of the volumes to the single area determination in a reproducible axial layer. This study aims to investigate whether these established segm...
Digital image processing techniques have become an important process within medical images. These techniques allow the improvement of the images in order to facilitate their interpretation for specialists. Within these are the segmentation methods, which help to divide the images by regions based on different approaches, in order to identify details that may be complex to distinguish initially. In this work, it is proposed the implementation of a multilevel threshold segmentation technique appli...
