Spatial fuzzy c-mean sobel algorithm with grey wolf optimizer for MRI brain image segmentation

Segmentation is the process of dividing the original image into multiple sub regions called segments in such a way that there is no intersection between any two regions. In medical images, the segmentation is hard to obtain due to the intensity similarity among various regions and the presence of noise in medical images. One of the most popular segmentation algorithms is Spatial Fuzzy C-means (SFCM). Although this algorithm has a good performance in medical images, it suffers from two issues. The first problem is lack of a proper strategy for point initialization step, which must be performed either randomly or manually by human. The second problem of SFCM is having inaccurate segmented edges. The goal of this research is to propose a robust medical image segmentation algorithm that overcomes these weaknesses of SFCM for segmenting magnetic resonance imaging (MRI) brain images with less human intervention. First, in order to find the optimum initial points, a histogram based algorithm in conjunction with Grey Wolf Optimizer (H-GWO) is proposed. The proposed H-GWO algorithm finds the approximate initial point values by the proposed histogram based method and then by taking advantage of GWO, which is a soft computing method, the optimum initial values are found. Second, in order to enhance SFCM segmentation process and achieve higher accurate segmented edges, an edge detection algorithm called Sobel was utilized. Therefore, the proposed hybrid SFCM-Sobel algorithm first finds the edges of the original image by Sobel edge detector algorithm and finally extends the edges of SFCM segmented images to the edges that are detected by Sobel. In order to have a robust segmentation algorithm with less human intervention, the H-GWO and SFCM-Sobel segmentation algorithms are integrated to have a semi-automatic robust segmentation algorithm. The results of the proposed H-GWO algorithms show that optimum initial points are achieved and the segmented images of the SFCM-Sobel algorithm have more accurate edges as compared to recent algorithms. Overall, quantitative analysis indicates that better segmentation accuracy is obtained. Therefore, this algorithm can be utilized to capture more accurate segmented in images in the era of medical imaging

A multiresolution stochastic level set method for Mumford-Shah image segmentation

10.1109/TIP.2008.2005823IEEE Transactions on Image Processing17122289-2300IIPR

Optimal Vibration Control in Structures using Level set Technique

Vibration control is inevitable in many fields, including mechanical and civil engineering. This matter becomes more crucial for lightweight systems, like those made of magnesium. One of the most commonly practiced methods in vibration control is to apply constrained layer damping (CLD) patches to the surface of a structure. In order to consider the weight efficiency of the structure, the best shape and locations of the patches should be determined to achieve the optimum vibration suppression with the lowest amount of damping patch. In most research work done so far, the shape of patches are assumed to be known and only their optimum locations are found. However, the shape of the patches plays an important role in vibration suppression that should be included in the overall optimization procedure. In this research, a novel topology optimization approach is proposed. This approach is capable of finding the optimum shape and locations of the patches simultaneously for a given surface area. In other words, the damping optimization will be formulated in the context of the level set technique, which is a numerical method used to track shapes and locations concurrently. Although level set technique offers several key benefits, its application especially in time-varying problems is somewhat cumbersome. To overcome this issue, a unique programming technique is suggested that utilizes MATLAB© and COMSOL© simultaneously. Different 2D structures will be considered and CLD patches will be optimally located on them to achieve the highest modal loss factor. Optimization will be performed while having different amount of damping patches to check the effectiveness of the technique. In all cases, certain constraints are imposed in order to make sure that the amount of damping material remains constant and equal to the starting value. Furthermore, different natural frequencies will be targeted in the damping optimization, and their effects will also be explained. The level set optimization technique will then be expanded to 3D structures, and a novel approach will be presented for defining an efficient 4D level set function to initialize the optimization process. Vibrations of a satellite dish will be optimally suppressed using CLD patches. Dependency of the optimum shape and location of patches to different parameters of the models such as natural frequencies and initial starting point will be examined. In another practical example, excessive vibrations of an automotive dash panel will be minimized by adding damping materials and their optimal distribution will be found. Finally, the accuracy of the proposed method will be experimentally confirmed through lab tests on a rectangular plate with nonsymmetrical boundary conditions. Different damping configurations, including the optimum one, will be tested. It will be shown that the optimum damping configuration found via level set technique possesses the highest loss factor and reveals the best vibration attenuation. The proposed level set topology optimization method shows high capability of determining the optimum damping set in structures. The effective coding method presented in this research will make it possible to easily extend this method to other physical problems such as image processing, heat transfer, magnetic fields, etc. Being interconnected, the physical part will be modeled in a finite element package like COMSOL and the optimization advances by means of Hamilton-Jacobi partial differential equation. Thus, the application of the proposed method is not confined to damping optimization and can be expanded to many engineering problems. In summary, this research: - offers general solution to 2D and 3D CLD applications and simultaneously finds the best shape and location of the patches for a given surface area (damping material); - extends the level set technique to concurrent shape and location optimization; - proposes a new numerical implementation to handle level set optimization problems in any complicated structure; - makes it possible to perform level set optimization in time dependent problems; - extends level set approach to higher order problems

Development of New Global Optimization Algorithms Using Stochastic Level Set Method with Application in: Topology Optimization, Path Planning and Image Processing

A unique mathematical tool is developed to deal with global optimization of a set of engineering problems. These include image processing, mechanical topology optimization, and optimal path planning in a variational framework, as well as some benchmark problems in parameter optimization. The optimization tool in these applications is based on the level set theory by which an evolving contour converges toward the optimum solution. Depending upon the application, the objective function is defined, and then the level set theory is used for optimization. Level set theory, as a member of active contour methods, is an extension of the steepest descent method in conventional parameter optimization to the variational framework. It intrinsically suffers from trapping in local solutions, a common drawback of gradient based optimization methods. In this thesis, methods are developed to deal with this drawbacks of the level set approach. By investigating the current global optimization methods, one can conclude that these methods usually cannot be extended to the variational framework; or if they can, the computational costs become drastically expensive. To cope with this complexity, a global optimization algorithm is first developed in parameter space and compared with the existing methods. This method is called "Spiral Bacterial Foraging Optimization" (SBFO) method because it is inspired by the aggregation process of a particular bacterium called, Dictyostelium Discoideum. Regardless of the real phenomenon behind the SBFO, it leads to new ideas in developing global optimization methods. According to these ideas, an effective global optimization method should have i) a stochastic operator, and/or ii) a multi-agent structure. These two properties are very common in the existing global optimization methods. To improve the computational time and costs, the algorithm may include gradient-based approaches to increase the convergence speed. This property is particularly available in SBFO and it is the basis on which SBFO can be extended to variational framework. To mitigate the computational costs of the algorithm, use of the gradient based approaches can be helpful. Therefore, SBFO as a multi-agent stochastic gradient based structure can be extended to multi-agent stochastic level set method. In three steps, the variational set up is formulated: i) A single stochastic level set method, called "Active Contours with Stochastic Fronts" (ACSF), ii) Multi-agent stochastic level set method (MSLSM), and iii) Stochastic level set method without gradient such as E-ARC algorithm. For image processing applications, the first two steps have been implemented and show significant improvement in the results. As expected, a multi agent structure is more accurate in terms of ability to find the global solution but it is much more computationally expensive. According to the results, if one uses an initial level set with enough holes in its topology, a single stochastic level set method can achieve almost the same level of accuracy as a multi-agent structure can obtain. Therefore, for a topology optimization problem for which a high level of calculations (at each iteration a finite element model should be solved) is required, only ACSF with initial guess with multiple holes is implemented. In some applications, such as optimal path planning, objective functions are usually very complicated; finding a closed-form equation for the objective function and its gradient is therefore impossible or sometimes very computationally expensive. In these situations, the level set theory and its extensions cannot be directly employed. As a result, the Evolving Arc algorithm that is inspired by "Electric Arc" in nature, is proposed. The results show that it can be a good solution for either unconstrained or constrained problems. Finally, a rigorous convergence analysis for SBFO and ACSF is presented that is new amongst global optimization methods in both parameter and variational framework

Interactive freeform editing techniques for large-scale, multiresolution level set models

Level set methods provide a volumetric implicit surface representation with automatic smooth blending properties and no self-intersections. They can handle arbitrary topology changes easily, and the volumetric implicit representation does not require the surface to be re-adjusted after extreme deformations. Even though they have found some use in movie productions and some medical applications, level set models are not highly utilized in either special effects industry or medical science. Lack of interactive modeling tools makes working with level set models difficult for people in these application areas.This dissertation describes techniques and algorithms for interactive freeform editing of large-scale, multiresolution level set models. Algorithms are developed to map intuitive user interactions into level set speed functions producing specific, desired surface movements. Data structures for efficient representation of very high resolution volume datasets and associated algorithms for rapid access and processing of the information within the data structures are explained. A hierarchical, multiresolution representation of level set models that allows for rapid decomposition and reconstruction of the complete full-resolution model is created for an editing framework that allows level-of-detail editing. We have developed a framework that identifies surface details prior to editing and introduces them back afterwards. Combining these two features provides a detail-preserving level set editing capability that may be used for multi-resolution modeling and texture transfer. Given the complex data structures that are required to represent large-scale, multiresolution level set models and the compute-intensive numerical methods to evaluate them, optimization techniques and algorithms have been developed to evaluate and display the dynamic isosurface embedded in the volumetric data.Ph.D., Computer Science -- Drexel University, 201