• The KIPS Transactions:PartB
• /
• v.18B no.3
• /
• pp.131-138
• /
• 2011

In this paper, we compare our proposed method with previous methods for the volumetric image segmentation using level set. In order to obtain an exact segmentation, the region and boundary information of image object are used in our proposed speed function. The boundary information is defined by the gradient vector flow obtained from the gradient images and the region information is defined by Gaussian distribution information of pixel intensity in a region-of-interest for image segmentation. Also the regular term is used to remove the noise around surface. We show various experimental results of real medical volume images to verify the superiority of proposed method.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.50 no.8
• /
• pp.225-231
• /
• 2013

Medical image segmentation is an image processing technology prior to performing various medical image processing. Therefore, a variety of methods have been researched for fast and accurate medical image segmentation. Accurate judgment of segmentation region is needed to segment the interest region in which patient requested in medical image that various organs exist. However, an case that scanned a part of organs is small occurs. In this case, information to determine the segmentation region is lack. consequently, a removal of segmentation region occurs during the segmentation process. In this paper, we improved segmentation results in a small region using volume data and linear equation. In order to verify the performance of the proposed method, we segmented the lung region of chest CT images. As a result of experiments, we confirmed that image segmentation accuracy rose from 0.978 to 0.981 and standard deviation also improved from 0.281 to 0.187.

• Journal of Biomedical Engineering Research
• /
• v.21 no.4
• /
• pp.433-439
• /
• 2000

Volume rendering is a powerful tool for visualizing sampled scalar values from 3D data without modeling geometric primitives to the data. The volume rendering can describe the surface-detail of a complex object. Owing to this characteristic. volume rendering has been used to visualize medical data. The size of volume data is usually too big to handle in real time. Recently, various volume rendering algorithms have been proposed in order to reduce the rendering time. However, most of the proposed algorithms are not proper for fast rendering of large non-coded volume data. In this paper, we propose a block-based fast volume rendering algorithm using a shear-warp factorization for non-coded volume data. The algorithm performs volume rendering by using the organ segmentation data as well as block-based 3D volume data, and increases the rendering speed for large non-coded volume data. The proposed algorithm is evaluated by rendering 3D X-ray CT body images and MR head images.

• Proceedings of the Korean Information Science Society Conference
• /
• 2000.04b
• /
• pp.592-594
• /
• 2000

변형모델(deformable model)은 볼륨의료영상(volumetric medical image)으로부터 복잡한 인체기관의 3차원적 경계를 분할해내기 위해 효과적인 방법을 제공한다. 그러나, 기존 변형모델은 초기와 의존성, 오목한 경계(concavity) 분할의 비적합성, 그리고 모델내 요소간 자체교차(self-intersection)의 제한점을 가지고 있었다. 본 연구에서는 이러한 제한점을 극복하고, 오목한 구조를 포함하는 복잡한 인체기관의 경계를 분할하기에 적합한 새로운 변형모델을 제안하였다. 제안한 변형모델은 볼륨영상 피라미드(pyramid)를 기반으로 다해상도(multiresolution)의 모델 정제화(refinement)를 수행한다. 다해상도 모델 정제화는 전역적 시셈플링(global resampling) 및 지역적 리샘플링(local resampling)를 통하여 저해상도의 모델로부터 점차 고해상도의 모델로 이동하면서 객체의 경계를 계층적으로 분할해가는 방법이다. 다해상도 모델에 의한 계층적 경계 분할은 초기화 조건에의 의존성을 극복할 수 있게할 뿐 아니라, 빠른 속도로 원하는 객체의 경계에 수렴할 수 있게 한다. 또한 지역적 리샘플링은 모델 구성요소의 정규화를 수행함으로써 객체의 오목한 부분을 성공적으로 분할할 수 있게 한다. 그리고, 제안 모델은 기존 변형모델에서 포함하는 내부 힘(internal force)과 외부 힘(external force)외에 자체교차방지 힘(non-self-intersection force)을 추가함으로서 효과적으로 모델내의 자체교차를 방지할 수 있게 하였다.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.25 no.4B
• /
• pp.715-724
• /
• 2000

This Paper presents 3-D subband filter banks which are effective for progressive and lossless compression of volumetric images. For such a purpose, ORT(Overlapping Rounding Transform), applied so far to 1-D losslesssubband filter banks, is now used to implement two types of 3-D lossless subband filter banks: separable andnon-separable types. Separable fiter banks are implemented form applying 1-D lossless filter banks consecutively.Non-separable later banks are developed by expanding the 1-D ORT into 3-D one. In particular, the proposed ORT based 3-D non-separable filter banks generalizes the 3-D HINT(Hierarchical INTerpolation) algorithm.Through the experiment comparisons on various volumetric medical images, we prove that the proposedseparablefnon-separable filter banks perform better, in terms of compression ratio (first order entropy), than theother lossless compression techniques such as block based transform and conventional 3-D HINT.

• Proceedings of the Korean Information Science Society Conference
• /
• 2006.06b
• /
• pp.268-270
• /
• 2006

본 논문에서는 3차원 볼륨영상에서 객체를 빠르게 분할하고 동시에 대화식으로 분할과정을 가시화하기 위하여 그래픽 하드웨어를 사용한 레벨-셋 방법을 제안한다. 이를 위하여 첫째, GPU 내에서 효율적 연산을 수행하기 위해 메모리 관리방법을 제안한다. 이는 GPU 내 텍스쳐 메모리 형식에 적합하게 데이터를 패킹하고, CPU의 주메모리와 GPU의 텍스쳐 메모리를 관리하는 방법을 제시한다. 둘째, GPU 내에서 레벨-셋 값을 갱신하는 과정을 9가지 경우로 나누어 연산을 수행하게 함으로써 연산의 효율성을 높힌다. 셋째, front의 변화를 대화식으로 확인하고, 파라미터 변경에 따른 분할 과정을 효과적으로 측정하기 위하여 그래픽 하드웨어 기반 빠른 가시화 방법을 제안한다. 본 논문에서는 제안방법을 평가하기 위하여 3차원 폐 CT 영상데이터를 사용하여 육안평가를 수행하고, 기존 소프트웨어 기반 레벨-셋 방법과 수행시간 측면에서 비교 분석한다. 본 제안방법은 소프트웨어 기반 레벨-셋 방법보다 빠르게 영상을 분할하고 동시에 가시화함으로써 데이터 량이 많은 의료응용에 효율적으로 적용이 가능하다.

• Journal of Korea Multimedia Society
• /
• v.15 no.9
• /
• pp.1075-1085
• /
• 2012

In clinical research using medical images, the image segmentation is one of the most important processes. Especially, the hippocampal atrophy is helpful for the clinical Alzheimer diagnosis as a specific marker of the progress of Alzheimer. In order to measure hippocampus volume exactly, segmentation of the hippocampus is essential. However, the hippocampus has some features like relatively low contrast, low signal-to-noise ratio, discreted boundary in MRI images, and these features make it difficult to segment hippocampus. To solve this problem, firstly, We selected region of interest from an experiment image, subtracted a original image from the negative image of the original image, enhanced contrast, and applied anisotropic diffusion filtering and gaussian filtering as preprocessing. Finally, We performed an image segmentation using two level set methods. Through a variety of approaches for the validation of proposed hippocampus segmentation method, We confirmed that our proposed method improved the rate and accuracy of the segmentation. Consequently, the proposed method is suitable for segmentation of the area which has similar features with the hippocampus. We believe that our method has great potential if successfully combined with other research findings.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.8 no.1
• /
• pp.191-197
• /
• 2013

Conventional CT and MRI scans produce cross-section slices of body that are viewed sequentially by radiologists who must imagine or extrapolate from these views what the 3 dimensional anatomy should be. By using sophisticated algorithm and high performance computing, these cross-sections may be rendered as direct 3D representations of human anatomy. The 2D medical image analysis forced to use time-consuming, subjective, error-prone manual techniques, such as slice tracing and region painting, for extracting regions of interest. To overcome the drawbacks of 2D medical image analysis, combining with medical image processing, 3D visualization is essential for extracting anatomical structures and making measurements. We used the gray-level thresholding, region growing, contour following, deformable model to segment human organ and used the feature vectors from texture analysis to detect harmful cancer. We used the perspective projection and marching cube algorithm to render the surface from volumetric MR and CT image data. The 3D visualization of human anatomy and segmented human organ provides valuable benefits for radiation treatment planning, surgical planning, surgery simulation, image guided surgery and interventional imaging applications.