Improving image segmentation by using energy function based on mixture of Gaussian pre-processing (original) (raw)
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Novel Active Contour Model for Image Segmentation Based on Local Fuzzy Gaussian Distribution Fitting
2012
⎯A novel active contour model is proposed, which incorporates local information distributions in a fuzzy energy function to effectively deal with the intensity inhomogeneity. Moreover, the proposed model is convex with respect to the variable which is used for extracting the contour. This makes the model independent on the initial condition and suitable for an automatic segmentation. Furthermore, the energy function is minimized in a computationally efficient way by calculating the fuzzy energy alterations directly. Experiments are carried out to prove the performance of the proposed model over some existing methods. The obtained results confirm the efficiency of the method.
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Accurate segmentation of anatomical organs in medical images is a complex task due to wide inter-patient variability and several acquisition dependent artefacts. Moreover, image noise, low contrast and intensity inhomogeneity in medical data further amplifies the challenge. In this work, we propose an effective yet simple algorithm based on composite energy metric for precise detection of object boundaries. A number of methods have been proposed in literature for image segmentation; however, these methods employ individual characteristics of image including gradient, regional intensity or texture map. Segmentation based on individual featres often fail for complex images, especially for medical imagery. Accordingly, we propose that the segmentation quality can be improved by integrating local and global image features in the curve evolution. This work employs the classic snake model aka active contour model; however, the curve evolution force has been updated. In contast to the conv...
Image Segmentation using Active Contours Without Edge
In this paper, we propose a segmentation method based on an active contour model without edges which, given an input image or image sequence, generates a mask of the desired object(s). This model is then extended to be used in multi-object tracking. Our method allows us to detect objects which are not necessarily delimited by gradient. In other terms, we do not need to detect edges of the object we want to extract from the image to perform our segmentation. To perform this segmentation, we base our model on techniques of curve evolution, the Mumford-Shah functional and level sets. Our problem can be seen as a particular case of the minimal partition problem in minimizing energy domain. The goal of this survey is also to present an interactive and parametrizable software which helps the understanding of the model and allows to test its limits. After presenting our model, we will give the numerical algorithms and the discrete approximations we used. Finally, various experimental results will be presented and commented.
Image Segmentation Based on Hybrid Adaptive Active Contour
Lecture Notes in Computer Science, 2015
In this paper, we focus on segmentation based active contour model. In fact, we present an hybrid adaptive active contour segmentation algorithm. In this approach, we merge a global and an adaptive local based active contour models in order to segment images. The proposed energy is then minimized based on level set method. Experiments shows the good segmentation results provided by the proposed method.
Fuzzy Energy-Based Active Contours
IEEE Transactions on Image Processing, 2000
This paper presents a novel fast model for active contours to detect objects in an image, based on techniques of curve evolution. The proposed model can detect objects whose boundaries are not necessarily defined by gradient, based on the minimization of a fuzzy energy, which can be seen as a particular case of a minimal partition problem. This fuzzy energy is used as the model motivation power evolving the active contour, which will stop on the desired object boundary. However, the stopping term does not depend on the gradient of the image, as most of the classical active contours, but instead is related to the image color and spatial segments. The fuzziness of the energy provides a balanced technique with a strong ability to reject "weak" local minima. Moreover, this approach converges to the desired object boundary very fast, since it does not solve the Euler-Lagrange equations of the underlying problem, but, instead, calculates the fuzzy energy alterations directly. The theoretical properties and various experiments presented demonstrate that the proposed fuzzy energy-based active contour is better and more robust than classical snake methods based on the gradient or other kind of energies.
2014 IEEE International Conference on Image Processing (ICIP), 2014
This article deals with statistical region-based active contour segmentation using the alpha-divergence family as similarity measure between the density probability functions of the background and the object regions of interest. Following previous publications on that topic, main originality of this contribution is in the proposed joint optimization of the energy steering the evolution of the active curve and the parameter alpha related to the metric of the divergence and closely related to the statistical luminance distribution of the data. Experiments are shown on both synthetic noisy and textured data as well as on real images (natural and medical ones). We show that the joint optimization process leads to satisfying results for every targeted tasks: above all, it is shown that the proposed approach overcome classic statistical-based region active contour approach using Kullback-Leibler divergence as similarity measure, that can stuck in local extrema during the usual optimization process.
An active contour method based on regularized kernel fuzzy C-means clustering
IEEE Access
This research presents hybrid level set evolution for complex and inhomogeneous image segmentation. Firstly, we develop an adaptive force with level set evolution, which is driven by region information. Adaptive force is produced by consolidating local and global force terms in an altered fashion. Besides, to avoid local fitting terms being stuck into a local minimum, we use the swap function to interchange the fitting terms so that fitting values inside the object are always higher. Later for the elimination of the costly contour initialization that existed in previous level set based evolutions, we integrate kernel based fuzzy c-means clustering and intensity-based thresholding framework with the proposed framework to automate the proposed strategy. Finally, for the level set function regularization and the for the elimination of its reinitialization we have used the Gaussian function in the level set evolution. We demonstrate the results on some complex images to show the strong and exact segmentation results that are conceivable with this new class of adaptive active contour model. We have additionally performed statistical analysis on real images and BRATS dataset using Dice index, accuracy, sensitivity, specificity and Jaccard index metrics. Results show that the proposed method gets high Dice index, accuracy, sensitivity, specificity and Jaccard index values compared to the previous state of art methods.
Multiple Active Contour Models based on the EM algorithm
Image Processing, 2005. ICIP 2005. …, 2005
This paper describes an algorithm for the extraction of multiple regions using multiple active contour models (ACMs). The algorithm organizes edge points into strokes and assigns a set of weights summing to one to each stroke. These weights represent the soft assignment of the stroke to each of the ACMs and depend on the distance between the stroke points and the ACM units. Both the weights and the ACMs energy minimization are computed using the expectationmaximization (EM) algorithm. The algorithm described in this paper is an extension of the Adaptive Snakes recently proposed in [9]. Experimental results will be provided to illustrate the performance of the proposed algorithm.
PLOS ONE, 2018
This paper presents a novel two-stage image segmentation method using an edge scaled energy functional based on local and global information for intensity inhomogeneous image segmentation. In the first stage, we integrate global intensity term with a geodesic edge term, which produces a preliminary rough segmentation result. Thereafter, by taking final contour of the first stage as initial contour, we begin second stage segmentation process by integrating local intensity term with geodesic edge term to get final segmentation result. Due to the suitable initialization from the first stage, the second stage precisely achieves desirable segmentation result for inhomogeneous image segmentation. Two stage segmentation technique not only increases the accuracy but also eliminates the problem of initial contour existed in traditional local segmentation methods. The energy function of the proposed method uses both global and local terms incorporated with compacted geodesic edge term in an additive fashion which uses image gradient information to delineate obscured boundaries of objects inside an image. A Gaussian kernel is adapted for the regularization of the level set function and to avoid an expensive re-initialization. The experiments were carried out on synthetic and real images. Quantitative validations were performed on Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) 2015 and PH 2 skin lesion database. The visual and quantitative comparisons will demonstrate the efficiency of the proposed method.