Sunworl Kim

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.

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.

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.

Detection and recognition of moving objects using the temporal difference method and the hidden Markov model

This paper proposes a new detection and recognition method for moving objects that uses the temporal difference method (TDM) and the hidden Markov model (HMM). First, we apply the concept of entropy to convert the pixel value in the image domain into the amount of energy change in the entropy domain. Second, we use the temporal difference method to quickly detect the region of moving objects in complex images to address the variation in changing environments. Third, we use the discrete wavelet transformation technique to extract proper feature vectors from the detected mask image. Fourth, we use the hidden Markov model to accurately recognize moving objects. The results indicate that our proposed method can effectively and accurately detect and recognize moving objects in image sequences.

Level-Set Segmentation of Brain Tumors Using a New Hybrid Speed Function

This paper presents a new hybrid speed function needed to perform image segmentation within the level-set framework. This speed function provides a general form that incorporates the alignment term as a part of the driving force for the proper edge direction of an active contour by using the probability term derived from the region partition scheme and, for regularization, the geodesics contour term. First, we use an external force for active contours as the Gradient Vector Flow field. This is computed as the diffusion of gradient vectors of a gray level edge map derived from an image. Second, we partition the image domain by progressively fitting statistical models to the intensity of each region. Here we adopt two Gaussian distributions to model the intensity distribution of the inside and outside of the evolving curve partitioning the image domain. Third, we use the active contour model that has the computation of geodesics or minimal distance curves, which allows stable boundary detection when the model’s gradients suffer from large variations including gaps or noise. Finally, we test the accuracy and robustness of the proposed method for various medical images. Experimental results show that our method can properly segment low contrast, complex images.

Detection and tracking of multiple moving objects in video sequence using entropy mask method and fast level set method

In this paper, we propose a novel algorithm for real-time detection and tracking of multiple moving objects, which sequentially integrates the entropy difference method with adaptive threshold and the fast level set method. First, we have applied the Clausius Entropy theory to convert the pixel value in image domain into the amount of energy change in entropy domain. And then we apply the entropy difference detection method to detect the coarse region of the moving objects in this image and we have constructed the mask covering a detected coarse region. Second, taking the initial value of the level set for moving object as the constructed mask region, we have applied the fast level set technique to track rapidly the contour of detected objects. Here, we have used the fast level set method that combines the Fast Marching Method and the Smart Narrow Band. Experiment results demonstrate that our method can detect and track effectively and accurately the motion objects in video sequence.

Segmentation and visualization of anatomical structures from volumetric medical images

This paper presents a method that can extract and visualize anatomical structures from volumetric medical images by using a 3D level set segmentation method and a hybrid volume rendering technique. First, the segmentation using the level set method was conducted through a surface evolution framework based on the geometric variation principle. This approach addresses the topological changes in the deformable surface by using the geometric integral measures and level set theory. These integral measures contain a robust alignment term, an active region term, and a mean curvature term. By using the level set method with a new hybrid speed function derived from the geometric integral measures, the accurate deformable surface can be extracted from a volumetric medical data set. Second, we employed a hybrid volume rendering approach to visualize the extracted deformable structures. Our method combines indirect and direct volume rendering techniques. Segmented objects within the data set are rendered locally by surface rendering on an object-by-object basis. Globally, all the results of subsequent object rendering are obtained by direct volume rendering (DVR). Then the two rendered results are finally combined in a merging step. This is especially useful when inner structures should be visualized together with semi-transparent outer parts. This merging step is similar to the focus-plus-context approach known from information visualization. Finally, we verified the accuracy and robustness of the proposed segmentation method for various medical volume images. The volume rendering results of segmented 3D objects show that our proposed method can accurately extract and visualize human organs from various multimodality medical volume images.

Medical image registration using the modified conditional entropy measure combining the spatial and intensity information

We propose an image registration technique using spatial and intensity information. The registration is conducted by the use of a measure based on the entropy of conditional probabilities. To achieve the registration, we first define a modified conditional entropy (MCE) computed from the joint histograms for the area intensities of two given images. In order to combine the spatial information into a traditional registration measure, we use the gradient vector flow field. Then the MCE is computed from the gradient vector flow intensity (GVFI) combining the gradient information and their intensity values of original images. To evaluate the performance of the proposed registration method, we conduct various experiments with our method as well as existing method based on the mutual information (MI) criteria. We evaluate the precision of MI- and MCE-based measurements by comparing the registration obtained from MR images and transformed CT images. The experimental results show that our proposed method is a more accurate technique.

Extraction of anatomic structures from medical volumetric images

In this paper, we present the extraction method of anatomic structures from volumetric medical images using the level set segmentation method. The segmentation step using the level set method consists of two kinds of processes which are a pre-processing stage for initialization and the final segmentation stage. In the initial segmentation stage, to construct an initial deformable surface, we extract the two dimensional boundary of relevant objects from each slice image consisting of the medical volume dataset and then successively stack the resulting boundary. Here we adopt the statistical clustering technique consisting of the Gaussian mixture model (GMM) and the Deterministic Annealing Expectation Maximization (DAEM) algorithm to segment the boundary of objects from each slice image. Next, we use the surface evolution framework based on the geometric variation principle to achieve the final segmentation. This approach handles topological changes of the deformable surface using geometric integral measures and the level set theory. These integral measures contain the alignment term, a minimal variance term, and the mean curvature term. By using the level set method with a new defined speed function derived from geometric integral measures, the accurate deformable surface can be extracted from the medical volumetric dataset. And we also use the Fast Matching Method that can reduce largely the computing time required to deform the 3D object model. Finally, we use the marching cubes algorithm to visualize the extracted deformable models. The experimental results show that our proposed method can exactly extract and visualize the human organs from the CT volume images.

Bi-directional probabilistic hypergraph matching method using Bayes theorem

Establishing correspondences between two hyper-graphs is a fundamental issue in computer vision, pattern recognition, and machine learning. A hyper-graph is modeled by feature set where the complex relations are represented by hyperedges. Hence, a match between two vertex sets determines a hyper-graph matching problem. We propose a new bidirectional probabilistic hyper-graph matching method using Bayesian inference principle. First, we formulate the corresponding hyper-graph matching problem as the maximization of a matching score function over all permutations of the vertexes. Second, we induce an algebraic relation between the hyper-edge weight matrixes and derive the desired vertex to vertex probabilistic matching algorithm using Bayes theorem. Third, we apply the well known convex relaxation procedure with probabilistic soft matching matrix to get a complete hard matching result. Finally, we have conducted the comparative experiments on synthetic data and real images. Experimental results show that the proposed method clearly outperforms existing algorithms especially in the presence of noise and outliers.