Boundary-Aware Dual Attention Guided Liver Segment Segmentation Model

( Xibin Jia ),( Chen Qian ),( Zhenghan Yang ),( Hui Xu ),( Xianjun Han ),( Hao Ren ),( Xinru Wu ),( Boyang Ma ),( Dawei Yang ),( Hong Min ) 한국인터넷정보학회 2022 KSII Transactions on Internet and Information Syst Vol.16 No.1

Accurate liver segment segmentation based on radiological images is indispensable for the preoperative analysis of liver tumor resection surgery. However, most of the existing segmentation methods are not feasible to be used directly for this task due to the challenge of exact edge prediction with some tiny and slender vessels as its clinical segmentation criterion. To address this problem, we propose a novel deep learning based segmentation model, called Boundary-Aware Dual Attention Liver Segment Segmentation Model (BADA). This model can improve the segmentation accuracy of liver segments with enhancing the edges including the vessels serving as segment boundaries. In our model, the dual gated attention is proposed, which composes of a spatial attention module and a semantic attention module. The spatial attention module enhances the weights of key edge regions by concerning about the salient intensity changes, while the semantic attention amplifies the contribution of filters that can extract more discriminative feature information by weighting the significant convolution channels. Simultaneously, we build a dataset of liver segments including 59 clinic cases with dynamically contrast enhanced MRI(Magnetic Resonance Imaging) of portal vein stage, which annotated by several professional radiologists. Comparing with several state-of-the-art methods and baseline segmentation methods, we achieve the best results on this clinic liver segment segmentation dataset, where Mean Dice, Mean Sensitivity and Mean Positive Predicted Value reach 89.01%, 87.71% and 90.67%, respectively.

Fine Segmentation Using Geometric Attraction-Driven Flow And Edge-Regions

( Joo Young Hahn ),( Chang Ock Lee ) 한국산업응용수학회(구 한국산업정보응용수학회) 2007 한국산업정보응용수학회 Vol.11 No.2

A fine segmentation algorithm is proposed for extracting objects in an image, which have both weak boundaries and highly non-convex shapes. The image has simple background colors or simple object colors. Two concepts, geometric attraction-driven flow (GADF) and edge-regions are combined to detect boundaries of objects in a sub-pixel resolution. The main strategy to segment the boundaries is to construct initial curves close to objects by using edge-regions and then to make a curve evolution in GADF. Since the initial curves are close to objects regardless of shapes, highly non-convex shapes are easily detected and dependence on initial curves in boundary-based segmentation algorithms is naturally removed. Weak boundaries are also detected because the orientation of GADF is obtained regardless of the strength of boundaries. For a fine segmentation, we additionally propose a local region competition algorithm to detect perceptible boundaries which are used for the extraction of objects without visual loss of detailed shapes. We have successfully accomplished the fine segmentation of objects from images taken in the studio and aphids from images of soybean leaves.

Enhanced Graph-Based Method in Spectral Partitioning Segmentation using Homogenous Optimum Cut Algorithm with Boundary Segmentation

S. Syed Ibrahim,G. Ravi International Journal of Computer ScienceNetwork S 2023 International journal of computer science and netw Vol.23 No.7

Image segmentation is a very crucial step in effective digital image processing. In the past decade, several research contributions were given related to this field. However, a general segmentation algorithm suitable for various applications is still challenging. Among several image segmentation approaches, graph-based approach has gained popularity due to its basic ability which reflects global image properties. This paper proposes a methodology to partition the image with its pixel, region and texture along with its intensity. To make segmentation faster in large images, it is processed in parallel among several CPUs. A way to achieve this is to split images into tiles that are independently processed. However, regions overlapping the tile border are split or lost when the minimum size requirements of the segmentation algorithm are not met. Here the contributions are made to segment the image on the basis of its pixel using min-cut/max-flow algorithm along with edge-based segmentation of the image. To segment on the basis of the region using a homogenous optimum cut algorithm with boundary segmentation. On the basis of texture, the object type using spectral partitioning technique is identified which also minimizes the graph cut value.

Segmental Acoustic Correlates Associated with the Korean Lenis Stops

Hongoak Yun 서울대학교 언어교육원 2013 語學硏究 Vol.49 No.1

The purpose of this study was to investigate which acoustic correlates to stop feature distinction were realized due to the presence of a segmentation boundary and how semantic focus information contributed to acoustic correlates associated with segmental units. A production study was conducted using Korean lenis stops. The closure durations and VOTs of lenis stops were longer when they were in boundary-initial positions than when they were in boundary-internal positions. The VOTs became longer as the position of words in the structural hierarchy of sentences grew higher. The F0s of vowels were lower when they followed boundary-initial lenis stops than when they followed boundary-internal lenis stops. The semantic focus information resulted in longer VOTs of initial lenis stops and lower F0s in vowels following initial lenis stops. These results indicated that talkers reliably indicated the onset of words and phrases in the closure durations and the VOTs of lenis stops and the F0s of their following vowels.

Segmental Acoustic Correlates Associated with the Korean Lenis Stops

윤홍옥 서울대학교 언어교육원 2013 語學硏究 Vol.49 No.1

The purpose of this study was to investigate which acoustic correlates to stop feature distinction were realized due to the presence of a segmentation boundary and how semantic focus information contributed to acoustic correlates associated with segmental units. A production study was conducted using Korean lenis stops. The closure durations and VOTs of lenis stops were longer when they were in boundary-initial positions than when they were in boundary-internal positions. The VOTs became longer as the position of words in the structural hierarchy of sentences grew higher. The F0s of vowels were lower when they followed boundary-initial lenis stops than when they followed boundary-internal lenis stops. The semantic focus information resulted in longer VOTs of initial lenis stops and lower F0s in vowels following initial lenis stops. These results indicated that talkers reliably indicated the onset of words and phrases in the closure durations and the VOTs of lenis stops and the F0s of their following vowels.

공간정보를 이용한 3차원 하천 경계선 매핑에 관한 연구

정윤재 ( Yun Jae Choung ),박현철 ( Hyen Cheol Park ),조명희 ( Myung Hee Jo ) 한국지리정보학회 2012 한국지리정보학회지 Vol.15 No.1

A river boundary is defined as the intersection between a main stream of a river and the land. Mapping of the river boundary is important for the protection of the properties in river areas, the prevention of flooding and the monitoring of the topographic changes in river areas. However, the utilization of the ground surveying technologies is not efficient for the mapping of the river boundary due to the irregular surfaces of river zones and the dynamic changes of water level of a river stream. Recently, the spatial information data sets such as the airborne LiDAR and aerial images are widely used for coastal mapping due to the acquisition of the topographic information without human accessibility. Due to these advantages, this research proposes a semi-automatic method for mapping of the river boundary using the spatial information data set such as the airborne LiDAR and the aerial photographs. Multiple image processing technologies such as the image segmentation algorithm and the edge detection algorithm are applied for the generation of the 3D river boundary using the aerial photographs and airborne topographic LiDAR data. Check points determined by the experienced expert are used for the measurement of the horizontal and vertical accuracy of the generated 3D river boundary. Statistical results show that the generated river boundary has a high accuracy in horizontal and vertical direction.

이미지 세그멘테이션 결과로부터 컨테이너 크레인의 장애물 충돌 경계를 추정하는 방법

유은섭(Eun-seop Yu),염충섭(Choong Sub Yeom),유보현(Bo Hyun Ryu) 대한기계학회 2022 大韓機械學會論文集A Vol.46 No.6

항만 컨테이너 크레인은 주행 시 작업자가 주변 환경을 인지하기 어렵기 때문에 2D 레이저 스캐너를 이용하여 주변 장애물을 감지하고 있다. 하지만, 위치가 고정된 컨테이너와 이동이 가능한 트럭을 구분할 수 없기 때문에 장애물이 감지된 경우 무조건 서행하여 작업 효율이 떨어진다. 이러한 문제를 해결하기 위해 본 논문에서는 컨테이너 크레인에 설치된 CCTV의 이미지를 기반으로 주변 장애물을 인식하고 충돌 경계를 추정할 수 있는 방법을 제안한다. 수집된 이미지는 세그멘테이션을 통해 장애물을 인식하고 경계선을 추출한다. 추출된 경계선은 PnP(perspective-n-point)를 통해 이미지 좌표계를 현실 좌표계로 변환하고 추출된 경계선을 기반으로 충돌 발생 근접 거리를 산출한다. 제안된 알고리즘은 컨테이너 크레인과 유사한 실험실 환경에서 3개의 장애물에 대해서 수행하였으며 최종 결과는 LiDAR 측정결과와 비교하여 유효성을 검증하였다. Because a container crane operator struggles to recognize the surrounding environment while driving, the container crane uses a 2D laser scanner to detect surrounding obstacles. However, because a container with a fixed location cannot be distinguished from a truck that can move, when an obstacle is detected, it unconditionally moves slowly, reducing the work efficiency. To solve this problem, this study proposes a method to recognize obstacles and estimate collision boundaries based on the CCTV images installed on container cranes. The collected images recognize obstacles by segmentation and extract boundaries. The extracted boundary line converts the image coordinate system to the real coordinate system using perspective-n-point (PnP) and calculates the collision proximity distance based on the extracted boundary line. The proposed algorithm was performed on three obstacles in a laboratory environment similar to that of a container crane, and the final result was validated by comparing it with the LiDAR measurement result.

Simplified Representation of Image Contour

류석원 국제문화기술진흥원 2018 International Journal of Advanced Culture Technolo Vol.6 No.4

We use edge detection technique for the input image to extract the entire edges of the object in the image and then select only the edges that construct the outline of the object. By examining the positional relation between these pixels composing the outline, a simplified version of the outline of the object in the input image is generated by removing unnecessary pixels while maintaining the condition of connection of the outline. For each pixel constituting the outline, its direction is calculated by examining the positional relation with the next pixel. Then, we group the consecutive pixels with same direction into one and then change them to a line segment instead of a point. Among those line segments composing the outline of the object, a line segment whose length is smaller than a predefined minimum length of acceptable line segment is removed by merging it into one of the adjacent line segments. As a result, an outline composed of line segments of over a certain length is obtained through this process.

Extension of Fast Level Set Method with Relationship Matrix, Modified Chan-Vese Criterion and Noise Reduction Filter

Vu, Dang-Tran,Kim, Jin-Young,Choi, Seung-Ho,Na, Seung-You The Acoustical Society of Korea 2009 韓國音響學會誌 Vol.28 No.e3

The level set based approach is one of active methods for contour extraction in image segmentation. Since Osher and Sethian introduced the level set framework in 1988, the method has made the great impact on image segmentation. However, there are some problems to be solved; such as multi-objects segmentation, noise filtering and much calculation amount. In this paper we address the drawbacks of the previous level set methods and propose an extension of the traditional fast level set to cope with the limitations. We introduce a relationship matrix, a new split-and-merge criterion, a modified Chan-Vese criterion and a novel filtering criterion into the traditional fast level set approach. With the segmentation experiments we evaluate the proposed method and show the promising results of the proposed method.

Simplified Representation of Image Contour

Suk Won Yoo 국제문화기술진흥원 2018 International Journal of Advanced Culture Technolo Vol.6 No.4

We use edge detection technique for the input image to extract the entire edges of the object in the image and then select only the edges that construct the outline of the object. By examining the positional relation between these pixels composing the outline, a simplified version of the outline of the object in the input image is generated by removing unnecessary pixels while maintaining the condition of connection of the outline. For each pixel constituting the outline, its direction is calculated by examining the positional relation with the next pixel. Then, we group the consecutive pixels with same direction into one and then change them to a line segment instead of a point. Among those line segments composing the outline of the object, a line segment whose length is smaller than a predefined minimum length of acceptable line segment is removed by merging it into one of the adjacent line segments. As a result, an outline composed of line segments of over a certain length is obtained through this process.