Myungeun Lee

Segmentation of Brain MR Images Using an Ant Colony Optimization Algorithm

In this paper, we describe a segmentation method for brain MR images using an ant colony optimization (ACO) algorithm. This is a relatively new meta-heuristic algorithm and a successful paradigm of all the algorithms which take advantage of the insect’s behavior. It has been applied to solve many optimization problems with good discretion, parallel, robustness and positive feedback. As an advanced optimization algorithm, only recently, researchers began to apply ACO to image processing tasks. Hence, we segment the MR brain image using ant colony optimization algorithm. Compared to traditional meta-heuristic segmentation methods, the proposed method has advantages that it can effectively segment the fine details.

Segmentation of interest region in medical volume images using geometric deformable model

In this paper, we present a new segmentation method using the level set framework for medical volume images. The method was implemented using the surface evolution principle based on the geometric deformable model and the level set theory. And, the speed function in the level set approach consists of a hybrid combination of three integral measures derived from the calculus of variation principle. The terms are defined as robust alignment, active region, and smoothing. These terms can help to obtain the precise surface of the target object and prevent the boundary leakage problem. The proposed method has been tested on synthetic and various medical volume images with normal tissue and tumor regions in order to evaluate its performance on visual and quantitative data. The quantitative validation of the proposed segmentation is shown with higher Jaccards measure score (72.52%-94.17%) and lower Hausdorff distance (1.2654 mm-3.1527 mm) than the other methods such as mean speed (67.67%-93.36% and 1.3361mm-3.4463 mm), mean-variance speed (63.44%-94.72% and 1.3361 mm-3.4616 mm), and edge-based speed (0.76%-42.44% and 3.8010 mm-6.5389 mm). The experimental results confirm that the effectiveness and performance of our method is excellent compared with traditional approaches.

Improved Image Thresholding Using Ant Colony Optimization Algorithm

The Ant colony optimization (ACO) algorithm is relatively a new meta-heuristic algorithm and a successful paradigm of all the algorithms which take advantage of the insectpsilas behavior. It has been applied to solve many optimization problems with good discretion, parallel, robustness and positive feedback. As an advanced optimization algorithm, only recently, researchers began to apply ACO to image processing tasks. In this paper, an Improved Image Thresholding Method using Ant Colony Optimization Algorithm is proposed. Compared with traditional thresholding segmentation methods, the proposed method has advantages that it can nicely segment the thin, it can efficiently reduce calculation time, and it has good capability and stabilization nature. The results show that using the proposed method can achieve satisfactory segmentation effect.

Segmentation of medical images using a geometric deformable model and its visualization

An automatic segmentation method for medical images that uses a geometric deformable model is presented, and the segmented results are visualized with the help of a modified marching cubes algorithm. The geometric deformable model is based on evolution theory and the level set method. In particular, the level set method utilizes a new derived speed function to improve the segmentation performance. This function is defined by the linear combination of three terms, namely, the alignment term, the minimal-variance term, and the smoothing term. The alignment term makes a level set as close as possible to the boundary of an object. The minimal-variance term best separates the interior and exterior of the contour. The smoothing term renders a segmented boundary less sensitive to noise. The use of the proposed speed function can improve the segmentation accuracy while making the boundaries of each object much smoother. Finally, it is demonstrated that the design of the speed function plays an important role in the reliable segmentation of synthetic and computed tomography (CT) images, and the segmented results are visualized effectively with the help of a modified marching cubes algorithm.

Improved image segmentation method based on optimized threshold using Genetic Algorithm

In image segmentation, threshold segmentation is becoming more and more widely used because of its simplicity and efficiency. In this paper, an improved image segmentation method based on optimized threshold using genetic algorithm is proposed. Compared with the traditional threshold segmentation methods, this method has advantages that it can nicely segment the thin and it can efficiently reduce calculation time and it has good capability and stabilization nature. The results show that using this proposed method can obtain satisfactory segmentation effect.

Automatic Segmentation Methods for Various CT Images Using Morphology Operation and Statistical Technique

In this paper, we present an automatic segmentation method for medical image based on the statistical technique. Here we use the morphological operations to determine automatically the number of clusters or objects composing a given image without any prior knowledge and adopt the Gaussian mixture model to mode an image statistically. Next, the deterministic annealing expectation maximization algorithm is employed to estimate the parameters of the GMM for the clustering algorithm. We apply the statistical technique for automatic segmentation of input CT image. The experimental results show that our method can segment exactly various CT images.

The viscoelastic bending stiffness of fiber-reinforced composite ilizarov C-rings

Abstract In order to optimize the design of unidirectional fiber-reinforced composite (Ilizarov) C-rings, the viscoelastic load relaxation behavior was analyzed under a point load. Initially, the deflection and bending stiffness were calculated from the Castigliano theorem and the Euler–Bernoulli bending theory for the elastic solution. The viscoelastic relaxation and creep behavior were then derived from the elastic solution by using the correspondence theorem. Besides the orthotropic mechanical properties of the composite, the asymmetric mechanical properties due to different tensile and compressive properties were also considered. With the exception of the deviation, which was affected by a relatively large thickness ratio to the radius of the C-ring, the calculated relaxation showed good agreement with the experimental result.

Salient object detection using recursive regional feature clustering

In the past decade, contrast features, which focus on rarity and uniqueness, have been widely used in the saliency-detection field, but the extreme dependency on the most-highlighted region remains as a limitation for the detection of multiple and complex objects. In particular, the difficulties are commonly observed when a high inter-object dissimilarity exists. Based on this observation, we present a new paradigm for the detection of the salient-object region, whereby only the spatial-saliency clues are interpreted from a multiple-level clustering framework; for this reason, in contrast to the previous methods, the proposed model is not dependent upon the contrast features. The proposed model can be roughly decomposed into the following four main phases: regional feature extraction, homogeneous-region clustering, saliency-score computation, and recursive processing. In particular, a recursive processing for which the salient region is optimized through the improvement of its clustering results is introduced. According to the experiment results, the proposed scheme outperforms the state-of-the-art methods on various benchmark datasets consisting of single, multiple, and complex object images; furthermore, the proposed model is more effective for the detection of multiple objects. To validate the contributions of this study, a multiple salient-object dataset (MSOD) that contains 100 images with more than two objects with a higher dissimilarity was also constructed.

Detection of multiple salient objects through the integration of estimated foreground clues

In this paper, a novel method for the detection of multiple salient regions that is based on the integration of estimated foreground clues is proposed. Although this subject has been very well studied for the detection of salient objects, many technical challenges still exist regarding the multiple-object-detection task; in particular, unlike a single-object-detection problem, a high inter-object dissimilarity causes new difficulties. By analyzing the limitations of the existing models, the following two main frameworks that are based on a multi-level foreground-segmentation strategy are proposed: non-parametric cluster-based saliency (NS) and parametric cluster-based saliency (PS). Each framework consists of a vector classification, a foreground estimation, an energy generation, and an integration process. In contrast to previous models, the proposed method is not dependent upon the contrast features, and is unaffected by the size, thickness, and shape of the objects. In the experiment results, a superior detection accuracy for the SED2 benchmark was achieved with the use of the proposed scheme; furthermore, the corresponding precision and recall are superior to those of the state-of-the-art approaches, and more effective performances were also achieved on the MSRA-ASD, SED2 and CSSD benchmarks. Display Omitted We address the problem of multiple salient region detection.The method consists of the parametric and non-parametric cluster based streams.The limitations of the existing models that are based on contrast are addressed.A spatial objectness is only considered for the computation of saliency scores.

Multimodality Image Registration Using Spatial Procrustes Analysis and Modified Conditional Entropy

In this paper, we propose a new image registration technique using two kinds of information known as object shapes and voxel intensities. The proposed approach consists of two registration steps. First, an initial registration is carried out for two volume images by applying Procrustes analysis theory to the two sets of 3D feature points representing object shapes. During this first stage, a volume image is segmented by using a geometric deformable model. Then, 3D feature points are extracted from the boundary of a segmented object. We conduct an initial registration by applying Procrustes analysis theory with two sets of 3D feature points. Second, a fine registration is followed by using a new measure based on the entropy of conditional probabilities. Here, to achieve the final registration, we define a modified conditional entropy (MCE) computed from the joint histograms for voxel intensities of two given volume images. By using a two step registration method, we can improve the registration precision. To evaluate the performance of the proposed registration method, we conduct various experiments for our method as well as existing methods based on the mutual information (MI) and maximum likelihood (ML) criteria. We evaluate the precision of MI, ML and MCE-based measurements by comparing their registration traces obtained from magnetic resonance (MR) images and transformed computed tomography (CT) images with respect to x-translation and rotation. The experimental results show that our method has great potential for the registration of a variety of medical images.