• Journal of KIISE
• /
• v.43 no.8
• /
• pp.869-877
• /
• 2016

In this paper, we propose an automated segmentation method of femoral cartilage in knee MR images using multi-atlas-based locally-weighted voting. The proposed method involves two steps. First, to utilize the shape information to show that the femoral cartilage is attached to a femur, the femur is segmented via volume and object-based locally-weighted voting and narrow-band region growing. Second, the object-based affine transformation of the femur is applied to the registration of femoral cartilage, and the femoral cartilage is segmented via multi-atlas shape-based locally-weighted voting. To evaluate the performance of the proposed method, we compared the segmentation results of majority voting method, intensity-based locally-weighted voting method, and the proposed method with manual segmentation results defined by expert. In our experimental results, the newly proposed method avoids a leakage into the neighboring regions having similar intensity of femoral cartilage, and shows improved segmentation accuracy.

• Journal of the Korean Society of Radiology
• /
• v.14 no.2
• /
• pp.157-168
• /
• 2020

Advancements in segmentation methodology has made automatic segmentation of brain structures using structural images accurate and consistent. One method of automatic segmentation, which involves registering atlas information from template space to subject space, requires a high quality atlas with accurate boundaries for consistent segmentation. The Allen Mouse Brain Atlas, which has been widely accepted as a high quality reference of the mouse brain, has been used in various segmentations and can provide accurate coordinates and boundaries of mouse brain structures for tractography. Through probabilistic tractography, diffusion tensor images can be used to map comprehensive neuronal network of white matter pathways of the brain. Comparisons between neural networks of mouse and human brains showed that various clinical tests on mouse models were able to simulate disease pathology of human brains, increasing the importance of clinical mouse brain studies. However, differences between brain size of human and mouse brain has made it difficult to achieve the necessary image quality for analysis and the conditions for sufficient image quality such as a long scan time makes using live samples unrealistic. In order to secure a mouse brain image with a sufficient scan time, an Ex-vivo experiment of a mouse brain was conducted for this study. Using FSL, a tool for analyzing tensor images, we proposed a semi-automated segmentation and tractography analysis pipeline of the mouse brain and applied it to various mouse models. Also, in order to determine the useful signal-to-noise ratio of the diffusion tensor image acquired for the tractography analysis, images with various excitation numbers were compared.

• Journal of the Korea Computer Graphics Society
• /
• v.24 no.4
• /
• pp.29-38
• /
• 2018

In this paper, we propose an automatic segmentation method of meniscus in knee MR images by automatic meniscus localization, multi-atlas-based locally-weighted voting, and patch-based edge feature classification. First, after segmenting the bone and knee articular cartilage, the volume of interest of the meniscus is automatically localized. Second, the meniscus is segmented by multi-atlas-based locally-weighted voting taking into account the weights of shape and intensity distribution in the volume of interest of the meniscus. Finally, to remove leakage to the collateral ligaments with similar intensity, meniscus is refined using patch-based edge feature classification considering shape and distance weights. Dice similarity coefficient between proposed method and manual segmentation were 80.13% of medial meniscus and 80.81 % for lateral meniscus, and showed better results of 7.25% for medial meniscus and 1.31% for lateral meniscus compared to the multi-atlas-based locally-weighted voting.

• Aerospace Industry
• /
• s.92
• /
• pp.56-57
• /
• 2006

• The KIPS Transactions:PartB
• /
• v.8B no.2
• /
• pp.164-172
• /
• 2001

본 논문에서는 시각화와 공간적, 속성 혼합 쿼리를 수행할 수 있는 관계형 데이터베이스를 설계하고 구현하였다. 쿼리에 사용되는 데이터형은 슬라이스, MPR, 볼륨 렌더링으로 시각화할 수 있으며, 쿼리는 아탈라스를 이용하는 경우와 그렇지 않는 경우를모두 고려하였다. 영상 데이터는 공간충전 곡선으로 공간적으로 클러스트링한 후 무손실 압축하여 데이터베이스에 저장된다. 본 논문은 저장 데이터의 양을 줄이기 위하여 관심영역의 크기에 따라 창의 크기가 변하는 적응적 Hibert 곡선을 제안하였으며, 실험에서 Hibert 곡선의 적용한 데이터보다 약 1.15배 높은 압축율을 보였다. 또한 아틀라스에 대한 뇌종양의 공간적 쿼리 결과를 통하여 본 의료 영상 데이터베이스의 유용성을 보였다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• fall
• /
• pp.126-128
• /
• 2021

MPEG(Moving Picture Experts Group) 비디오 그룹은 사용자에게 움직임 시차(motion parallax)를 제공하면서 3D 공간 내에서 임의의 위치와 방향의 시점(view)을 렌더링(rendering) 가능하게 하는 6DoF(Degree of Freedom)의 몰입형 비디오 부호화 표준인 MIV(MPEG Immersive Video) 표준화를 진행하고 있다. MIV 표준화 과정에서 참조 SW 인 TMIV(Test Model for Immersive Video)도 함께 개발하고 있으며 점진적으로 부호화 성능을 개선하고 있다. TMIV 는 여러 뷰로 구성된 방대한 크기의 6DoF 비디오를 압축하기 위하여 입력되는 뷰 비디오들 간의 중복성을 제거하고 남은 영역들은 각각 개별적인 패치(patch)로 만든 후 아틀라스에 패킹(packing)하여 부호화되는 화소수를 줄인다. 이때 아틀라스 비디오에 패킹된 패치들의 위치 정보를 메타데이터로 압축 비트열과 함께 전송하게 되며, 본 논문에서는 이러한 패킹 정보를 보다 효율적으로 표현하기 위한 방법을 제안한다. 제안방법은 기존 TMIV10.0 에 비해 약 10%의 메타데이터를 감소시키고 종단간 BD-rate 성능을 0.1% 향상시킨다.

• Proceedings of the Korea Society of Design Studies Conference
• /
• 2004.10a
• /
• pp.44-45
• /
• 2004

• Journal of the Korea Computer Graphics Society
• /
• v.17 no.2
• /
• pp.11-16
• /
• 2011

In computer graphics, shadows play a very important role as a hint of inter-object distance as well as themselves in terms of realism. To represent shadows, some traditional methods such as shadow mapping and shadow volume have been frequently used for the purpose. However, the rendering results are not natural since they assume the point light. On the contrary, an area light can render soft-shadows, but its computation is too burdensome due to integral over the whole light source surface. Many alternatives have been introduced, back-projection of occluder onto the light source to get visibility of light or filtering of shadow boundary by calculating size of penumbra. But they also have problems of light bleeding or ringing effects because of low order approximation, or low performance. In this paper, we describe a method to improve those problems using shadow atlas.

• Journal of Broadcast Engineering
• /
• v.27 no.6
• /
• pp.861-871
• /
• 2022

The moving picture experts group (MPEG) developed the MPEG immersive video (MIV) standard for efficient compression of multiple immersive videos representing natural contents and computer graphics. The MIV compresses multiple immersive videos and generates multiple output videos which are defined as atlases. However, there is a synchronization issue of multiple decoders in a legacy device when decoding multiple encoded atlases. This paper proposes and implements the geometry packing method for adaptive control of decoder instances for low-end and high-end devices. The proposed method on the recent version of the MIV reference software worked correctly.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2022.11a
• /
• pp.151-153
• /
• 2022

최근 Moving Picture Experts Group(MPEG)에서는 2차원 비디오 압축 표준인 Versatile Video Coding(VVC)에 이어서 다양한 영상 포맷들에 대한 압축 방식을 표준화하고 있다. 특히, 가상현실, 증강현실, 혼합현실 등의 지원을 위한 Six Degrees of Freedom(6DoF) 입체영상 콘텐츠들이 최근 다양한 분야들에서 활용되고 있는데, 6DoF 입체영상은 일반적으로 복수 시점의 고해상도 칼라영상과 깊이영상으로 구성된다. 이러한 고해상도의 6DoF 몰입형 입체영상을 제한된 네트워크 환경에서 완벽한 서비스를 목표로 MPEG에서는 몰입형 입체영상 압축 기술인 MPEG Immersive Video(MIV) 표준화를 활발하게 진행 중에 있다. MIV에서는 기본 뷰(Basic View)로 이루어진 영상과 추가 뷰(Addtional View)에서 중복성 높은 픽셀들이 제거된 아틀라스 패치로 이루어진 영상을 각각 VVC로 압축한다. 하지만 아틀라스 패치로 이루어진 영상의 경우에는 일반적인 2차원 칼라영상과 다른 특성을 가지기 때문에, VVC 인루프 필터 기술이 비효율적일 수 있다. 따라서, 본 논문에서는 MIV 표준에서의 VVC 인루프 필터들의 성능을 분석한다.