• Title/Summary/Keyword: left-edge algorithm

Search Result 45, Processing Time 0.022 seconds

Knowledge Based Automated Boundary Detection for Quantifying of Left Ventricular Function in Low Contrast Angiographic Images (저대조 혈관 조영상에서 좌심실 기능의 정량화를 위한 지식 기반의 경계선 자동검출)

  • 전춘기;권용무
    • Journal of Biomedical Engineering Research
    • /
    • v.17 no.1
    • /
    • pp.109-120
    • /
    • 1996
  • Cardiac function is evaluated quantitatively using angiographic images via the analysis of the shape change or the heart wall boundaries. To kin with, boundary defection or ESLV(End Systolic Lert Ventricular) and EDLV(End Diastolic Left Ventricular) is essential for the quantitative analysis of cardiac function. The boundary detection methods proposed in the past were almost semi-automatic. Intervention by a knowledgeable human operator was still required Of con, manual tracing of the boundaries is currently used for subsequent analysis and diagnosis. This method would not cut excessive time, labor, and subjectivity associated with manual intervention by a human operator. EDLV images have noncontiguous and ambiguous edge signal on some boundary regions. In this paper, we propose a new method for automated detection of boundaries in noncontiguous and ambiguous EDLV images. The boundary detection scheme which based on a priori knowledge information is divided into two steps. The first step is to detect the candidate edge points of EDLV using ESLV boundaries. The second step is to correct detected boundaries of EDLV using the LV shape. We developed the algorithm of modifying EDLV boundaries defined adaptive modifier. We experimented the method proposed in this paper and compared our proposed method with the manual method in detecting boundaries of EDLV. In the areas within estimated boundaries of EDLV, the percentage of error was about 1.4%. We verified the useflilness and obtained the satisfying results througll the experiments of the proposed method.

  • PDF

A Study on Edge Detection using Directional Mask in Impulse Noise Image (Salt-and-Pepper 잡음 영상에서 방향성 마스크를 이용한 에지 검출에 관한 연구)

  • Lee, Chang-Young;Kim, Nam-Ho
    • Journal of the Korea Institute of Information and Communication Engineering
    • /
    • v.18 no.12
    • /
    • pp.2982-2988
    • /
    • 2014
  • The edge detection is a pre-processing of such as image segmentation, image recognition, etc, and many related studies are being conducted both in domestic and abroad. Representative edge detection methods are Sobel, Prewitt, Laplacian, Roberts and Canny edge detectors. Such existing methods are possible for superb detections of edges if edges are detected from videos without noises. However, for video degraded by the salt-and-pepper noise, the edge detection characteristic is shown to be insufficient due to the noise influence. Therefore, in this study, the area is separated as the top, down, left and right from the mask's center pixel first to acquire a superb edge detection characteristic from the video damaged by the salt-and-pepper noise. And the algorithm that detects the final edge by applying the directional mask on the assumed factor of mask that is obtained according to the result of determination for the noise status of representative pixel value of each area.

An Effective Steel Plate Detection Using Eigenvalue Analysis (고유값 분석을 이용한 효과적인 후판 인식)

  • Park, Sang-Hyun
    • The Journal of the Korea institute of electronic communication sciences
    • /
    • v.7 no.5
    • /
    • pp.1033-1039
    • /
    • 2012
  • In this paper, a simple and robust algorithm is proposed for detecting each steel plate from a image which contains several steel plates. Steel plate is characterized by line edge, so line detection is a fundamental task for analyzing and understanding of steel plate images. To detect the line edge, the proposed algorithm uses the small eigenvalue analysis. The proposed approach scans an input edge image from the top left corner to the bottom right corner with a moving mask. A covariance matrix of a set of edge pixels over a connected region within the mask is determined and then the statistical and geometrical properties of the small eigenvalue of the matrix are explored for the purpose of straight line detection. Using the detected line edges, each plate is determined based on the directional information and the distance information of the line edges. The results of the experiments emphasize that the proposed algorithm detects each steel plate from a image effectively.

A Watermarking for Text Document Images using Edge Direction Histograms (에지 방향 히스토그램을 이용한 텍스트 문서 영상의 워터마킹)

  • 김영원;오일석
    • Journal of KIISE:Software and Applications
    • /
    • v.31 no.2
    • /
    • pp.203-212
    • /
    • 2004
  • The watermarking is a method to achieve the copyright protection of multimedia contents. Among several media, the left documents show very peculiar properties: block/line/word patterning, clear separation between foreground and background areas. So algorithms specific to the text documents are required that meet those properties. This paper proposes a novel watermarking algorithm for the grayscale text document images. The algorithm inserts the watermark signals through the edge direction histograms. A concept of sub-image consistency is developed that the sub-images have similar shapes in terms of edge direction histograms. Using Korean, Chinese, and English document images, the concept is evaluated and proven to be valid over a wide range of document images. To insert watermark signals, the edge direction histogram is modified slightly. The experiments were performed on various document images and the algorithm was evaluated in terms of imperceptibility and robustness.

Automatic Left Ventricle Segmentation by Edge Classification and Region Growing on Cardiac MRI (심장 자기공명영상의 에지 분류 및 영역 확장 기법을 통한 자동 좌심실 분할 알고리즘)

  • Lee, Hae-Yeoun
    • The KIPS Transactions:PartB
    • /
    • v.15B no.6
    • /
    • pp.507-516
    • /
    • 2008
  • Cardiac disease is the leading cause of death in the world. Quantification of cardiac function is performed by manually calculating blood volume and ejection fraction in routine clinical practice, but it requires high computational costs. In this study, an automatic left ventricle (LV) segmentation algorithm using short-axis cine cardiac MRI is presented. We compensate coil sensitivity of magnitude images depending on coil location, classify edge information after extracting edges, and segment LV by applying region-growing segmentation. We design a weighting function for intensity signal and calculate a blood volume of LV considering partial voxel effects. Using cardiac cine SSFP of 38 subjects with Cornell University IRB approval, we compared our algorithm to manual contour tracing and MASS software. Without partial volume effects, we achieved segmentation accuracy of $3.3mL{\pm}5.8$ (standard deviation) and $3.2mL{\pm}4.3$ in diastolic and systolic phases, respectively. With partial volume effects, the accuracy was $19.1mL{\pm}8.8$ and $10.3mL{\pm}6.1$ in diastolic and systolic phases, respectively. Also in ejection fraction, the accuracy was $-1.3%{\pm}2.6$ and $-2.1%{\pm}2.4$ without and with partial volume effects, respectively. Results support that the proposed algorithm is exact and useful for clinical practice.

Object Recognition Using Hausdorff Distance and Image Matching Algorithm (Hausdorff Distance와 이미지정합 알고리듬을 이용한 물체인식)

  • Kim, Dong-Gi;Lee, Wan-Jae;Gang, Lee-Seok
    • Transactions of the Korean Society of Mechanical Engineers A
    • /
    • v.25 no.5
    • /
    • pp.841-849
    • /
    • 2001
  • The pixel information of the object was obtained sequentially and pixels were clustered to a label by the line labeling method. Feature points were determined by finding the slope for edge pixels after selecting the fixed number of edge pixels. The slope was estimated by the least square method to reduce the detection error. Once a matching point was determined by comparing the feature information of the object and the pattern, the parameters for translation, scaling and rotation were obtained by selecting the longer line of the two which passed through the matching point from left and right sides. Finally, modified Hausdorff Distance has been used to identify the similarity between the object and the given pattern. The multi-label method was developed for recognizing the patterns with more than one label, which performs the modified Hausdorff Distance twice. Experiments have been performed to verify the performance of the proposed algorithm and method for simple target image, complex target image, simple pattern, and complex pattern as well as the partially hidden object. It was proved via experiments that the proposed image matching algorithm for recognizing the object had a good performance of matching.

Robust Lane Detection Method Under Severe Environment (악 조건 환경에서의 강건한 차선 인식 방법)

  • Lim, Dong-Hyeog;Tran, Trung-Thien;Cho, Sang-Bock
    • Journal of the Institute of Electronics and Information Engineers
    • /
    • v.50 no.5
    • /
    • pp.224-230
    • /
    • 2013
  • Lane boundary detection plays a key role in the driver assistance system. This study proposes a robust method for detecting lane boundary in severe environment. First, a horizontal line detects form the original image using improved Vertical Mean Distribution Method (iVMD) and the sub-region image which is under the horizontal line, is determined. Second, we extract the lane marking from the sub-region image using Canny edge detector. Finally, K-means clustering algorithm classifi left and right lane cluster under variant illumination, cracked road, complex lane marking and passing traffic. Experimental results show that the proposed method satisfie the real-time and efficient requirement of the intelligent transportation system.

A Study on Tracking and Quantitative Analysis of Regional Left Ventricular Wall Motion in Echocardiography (심초음파에서 국소 좌심실벽 운동 추적 및 정량적 분석에 관한 연구)

  • 신동규;김동윤;최경훈;박광훈
    • Progress in Medical Physics
    • /
    • v.10 no.3
    • /
    • pp.115-123
    • /
    • 1999
  • The two dimensional echocardiography is widely used to evaluate regional wall motion abnormality, because of its abilities to depict left ventricular wall motion. A number of researches have been processed for evaluation and quantitative analysis of left ventricular wall motion functions. In this paper, we proposed an algorithm which detects automatically and analyze quantitatively endocardial wall motion during systole. The echocardiograms were obtained in the short-axis views in normal subjects. Automated edge detection and endocardial contour tracking algorithm was applied to each frames, quantitative analysis based on segmentation was performed, pre-defined color overlays superimposed on the gray scale images, and the images was animated. The proposed algorithm provided automated, quantitative diagnosis of regional wall motion abnormality.

  • PDF

Real-time Forward Vehicle Detection Method based on Extended Edge (확장 에지 분석을 통한 실시간 전방 차량 검출 기법)

  • Ji, Young-Suk;Han, Young-Joon;Hahn, Hern-Soo
    • Journal of the Korea Society of Computer and Information
    • /
    • v.15 no.10
    • /
    • pp.35-47
    • /
    • 2010
  • To complement inaccurate edge information and detect correctly the boundary of a vehicle in an image, an extended edge analysis technique is presented in this paper. The vehicle is detected using the bottom boundary generated by a vehicle and the road surface and the left and right side boundaries of the vehicle. The proposed extended edge analysis method extracts the horizontal edge by merging or dividing the nearby edges inside the region of interest set beforehand because various noises deteriorates the horizontal edge which can be a bottom boundary. The horizontal edge is considered as the bottom boundary and the vertical edges as the side boundaries of a vehicle if the extracted horizontal edge intersects with two vertical edges which satisfy the vehicle width condition at the height of the horizontal edge. This proposed algorithm is more efficient than the other existing methods when the road surface is complex. It is proved by the experiments executed on the roads having various backgrounds.

Effective Line Detection of Steel Plates Using Eigenvalue Analysis (고유값 분석을 이용한 효과적인 후판의 직선 검출)

  • Park, Sang-Hyun;Kim, Jong-Ho;Kang, Eui-Sung
    • Journal of the Korea Institute of Information and Communication Engineering
    • /
    • v.15 no.7
    • /
    • pp.1479-1486
    • /
    • 2011
  • In this paper, a simple and robust algorithm is proposed for detecting straight line segments in a steel plate image. Line detection from a steel plate image is a fundamental task for analyzing and understanding of the image. The proposed algorithm is based on small eigenvalue analysis. The proposed approach scans an input edge image from the top left comer to the bottom right comer with a moving mask. A covariance matrix of a set of edge pixels over a connected region within the mask is determined and then the statistical and geometrical properties of the small eigenvalue of the matrix are explored for the purpose of straight line detection. Before calculating the eigenvalue, each line segment is separated from the edge image where several line segments are overlapped to increase the accuracy of the line detection. Additionally, unnecessary line segments are eliminated by the number of pixels and the directional information of the detected line edges. The respects of the experiments emphasize that the proposed algorithm outperforms the existing algorithm which uses small eigenvalue analysis.