Soonyoung Park

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.

Composition and proposed structure of a water-soluble glycan from the Keumsa Sangwhang Mushroom (Phellinus linteus)

Phellinus linteus is a mushroom which has been prized in Korea for its medicinal properties since ancient times. A glycan preparation from this source has shown promise in experimental anti-tumour and metastasis studies, but the glycan has not been well characterized. In the present work, the glycan fraction of a hot water extract from P. linteus (Keumsa Sangwhang Mushroom, Korea) has been isolated following ammonium sulfate precipitation, dialysis/concentration and anion exchange chromatographic steps. Analyses for monosaccharide composition showed glucose and mannose to be the major constituents. Digestion of the glycan fraction with specific glycosidases and by linkage analysis allowed us to propose a core beta(1-3) linked glucan heavily substituted via (1-6) links with beta(1-3) linked mannose chains. Significant levels of galactose and xylose, present in the glycan fraction, may be associated with this glucomannan or not. These findings are consistent with the view that a core beta(1-3)-linked glucan chain with beta(1-6) branch points is a common feature of mushroom glycans possessing anti-tumour activity.

Watermarking technique for authentication of 3-d polygonal meshes

In this paper, we present a watermarking technique for authentication of 3-D polygonal meshes. The proposed technique is based on a wavelet-based multiresolution analysis to convert an original polygonal mesh model into a simplified mesh model and wavelet coefficient vectors. The embedding procedure is to modify the vertex of a simplified mesh model at a low resolution according to the order of norms of the wavelet coefficient vectors using a look-up table. The watermark extraction process is to restore the binary logo pattern by extracting a binary watermark bit from a look-up table corresponding to the watermarked simple mesh. The experimental results show that the proposed method is invariant to the location, scale and rotation transformation while detecting unauthorized modifications.

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.

FPGA implementation of image watermarking algorithm for a digital camera

In this paper, we present an FPGA implementation of a watermarking-based authentication algorithm for a digital camera to authenticate the snapshots in a manner that any changes of contents in the still image will be reflected in the embedded watermark. All components of a digital camera and a watermark algorithm are implemented in VHDL, simulated, synthesized and loaded into an FPGA device. To achieve the semifragile characteristics that survive a certain amount of compression, we employ the property of DCT coefficients quantization proposed by Lin and Chang (2000). The binary watermark bits are generated by exclusive ORing the binary logo with pseudo random binary sequence. Then watermark bits are embedded into the LSBs of DCT coefficients in the medium frequency range. The system consists of three main parts: image capture and LCD controller, watermark embedding part, and camera control unit. The FPGA implemented digital camera is tested to analyze the performance. It is shown that the watermarking algorithm can embed the watermark into the original image coming from a sensor much faster than the software implementation and the embedded image is easily transmitted to the PC by using the USB interface. The quality of the transmitted image is also comparable to the one implemented by a software algorithm.

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.

Deterministic annealing EM and its application in natural image segmentation

In this paper, we present a color image segmentation algorithm based on a finite mixture model and examine its application to natural scene segmentation. Gaussian mixture model (GMM) is first adopted to represent the statistical distribution of multi-colored objects. Then a deterministic annealing Expectation Maximization (DAEM) formula is used to estimate the parameters of the GMM. The experimental results show that the proposed DAEM can avoid the initialization problem unlike the standard EM algorithm during the maximum likelihood (ML) parameter estimation and natural scenes containing texts are segmented more efficiently than the existing EM technique.

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.

Medical Image Segmentation Using a Geometric Active Contour Model Based on Level Set Method

We present a level set framework for medical image segmentation using a new defined speed function. This function combines the alignment term, which makes a level set as close as possible to a boundary of object, the minimal variance term, which best separates the interior and exterior in the contour and the smoothing term, which makes a segmented boundary become less sensitive to noise. The use of a proposed speed function can improve the segmentation accuracy while making the boundaries of each object much smoother. Finally, we have demonstrated that the design of the speed function plays an important part in segmenting the synthetic and CT images reliably.

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.