• Title/Summary/Keyword: Vessel Segmentation

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Pulmonary vascular Segmentation and Refinement On the CT Scans (컴퓨터 단층 촬영 영상에서의 폐혈관 분할 및 정제)

  • Shin, Min-Jun;Kim, Do-Yeon
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.16 no.3
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    • pp.591-597
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    • 2012

  • Medical device performance has been advanced while images are expected to be acquired with further higher quality and pertinent applicability as images have been increasing in importance in analyzing major organs. Recent high frequency of image processing by MATLAB in image analysis area accounts for the intent of this study to segment pulmonary vessels by means of MATLAB. This study is to consist of 3 phases including pulmonary region segmentation, pulmonary vessel segmentation and three dimensional connectivity assessment, in which vessel was segmented, using threshold level, from the pulmonary region segmented, vessel thickness was measured as two dimensional refining process and three dimensional connectivity was assessed as three dimensional refining process. It is expected that MATLAB-based image processing should contribute to diversity and reliability of medical image processing and that the study results may lay a foundation for chest CT images-related researches.

  • https://doi.org/10.6109/jkiice.2012.16.3.591 인용 PDF KSCI

Performance evaluation of vessel extraction algorithm applied to Aortic root segmentation in CT Angiography (CT Angiography 영상에서 대동맥 추출을 위한 혈관 분할 알고리즘 성능 평가)

  • Kim, Tae-Hyong;Hwang, Young-sang;Shin, Ki-Young
    • The Journal of Korea Institute of Information, Electronics, and Communication Technology
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    • v.9 no.2
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    • pp.196-204
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    • 2016

  • World Health Organization reported that heart-related diseases such as coronary artery stenoses show the highest occurrence rate which may cause heart attack. Using Computed Tomography angiography images will allow radiologists to detect and have intervention by creating 3D roadmapping of the vessels. However, it is often complex and difficult do reconstruct 3D vessel which causes very large amount of time and previous researches were studied to segment vessels more accurate automatically. Therefore, in this paper, Region Competition, Geodesic Active Contour (GAC), Multi-atlas based segmentation and Active Shape Model algorithms were applied to segment aortic root from CTA images and the results were analyzed by using mean Hausdorff distance, volume to volume measure, computational time, user-interaction and coronary ostium detection rate. As a result, Extracted 3D aortic model using GAC showed the highest accuracy but also showed highest user-interaction results. Therefore, it is important to improve automatic segmentation algorithm in future

  • https://doi.org/10.17661/jkiiect.2016.9.2.196 인용 PDF KSCI

Blood Vessel Enhancement by Directed Diffusion

  • Intajag, S.;Tipsuwanporn, V.
    • 제어로봇시스템학회:학술대회논문집
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    • 2004.08a
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    • pp.101-106
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    • 2004

  • In this paper, a blood vessel in an angiographic image, which plays an importance role in the diagnose diseases including in the eyes, brain and heart, is enhanced by using a directed diffusion technique. A fundamental component of the angiographic analysis is vessel segmentation that the proposed method provides a preprocessing of the image into a form suitable for human analysis, or more importantly, for machine analysis such the segmentation. Vessel enhancement is a challenging problem due to the complex nature of vascular trees and to imaging imperfections. Some parts of the inherent imperfections in angiography are the intensity inhomogeneity between the larger and smaller vessels, and another imperfection is the leakage of contrast agent into the background tissue that provides to low contrast between vessels and tissue. In the proposed scheme, the directed diffusion solves the problem by formulating a local geometric structure, which consists of direction and scale of the blood vessels. The diffusion process uses the local structure to enhance by a diffusivity tensor. The proposed algorithm can be applied to maintain sharpness and coherence-smooth the intra-regions into homogeneity better than traditional diffusion methods, which are Gaussian regulation and coherence enhancing diffusion.

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Retinal Blood Vessel Segmentation using Deep Learning (딥러닝 기법을 이용한 망막 혈관 분할)

  • Kim, Beomsang;Lee, Ik Hyun
    • The Journal of Korean Institute of Information Technology
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    • v.17 no.5
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    • pp.77-82
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    • 2019

  • Diabetic retinopathy is a complicated form of diabetes due to circulatory disorder in the peripheral blood vessels of the retina. We segment the microvessel for diagnosing diabetic retinophathy. The conventional methods using filter and features can segment the thick blood vessels, but it has relatively weak for segmenting fine blood vessels. In pre-processing step, noise reduction filter and histogram equalization are applied to suppress the noise and enhance the image contrast. Then, deep learning technique is used for pixel-by-pixel segmentation. The accuracy of conventional methods is between 90% to 94%, while the proposed method has improved as 95% accuracy. There is a problem of segmentation error around the optic disc and exudate due to the network depth. However the accuracy can be improved by modifying the network architecture in the future.

  • https://doi.org/10.14801/jkiit.2019.17.5.77 인용

Generation of Triangular Mesh of Coronary Artery Using Mesh Merging (메쉬 병합을 통한 관상동맥의 삼각 표면 메쉬 모델 생성)

  • Jang, Yeonggul;Kim, Dong Hwan;Jeon, Byunghwan;Han, Dongjin;Shim, Hackjoon;Chang, Hyuk-jae
    • Journal of KIISE
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    • v.43 no.4
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    • pp.419-429
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    • 2016

  • Generating a 3D surface model from coronary artery segmentation helps to not only improve the rendering efficiency but also the diagnostic accuracy by providing physiological informations such as fractional flow reserve using computational fluid dynamics (CFD). This paper proposes a method to generate a triangular surface mesh using vessel structure information acquired with coronary artery segmentation. The marching cube algorithm is a typical method for generating a triangular surface mesh from a segmentation result as bit mask. But it is difficult for methods based on marching cube algorithm to express the lumen of thin, small and winding vessels because the algorithm only works in a three-dimensional (3D) discrete space. The proposed method generates a more accurate triangular surface mesh for each singular vessel using vessel centerlines, normal vectors and lumen diameters estimated during the process of coronary artery segmentation as the input. Then, the meshes that are overlapped due to branching are processed by mesh merging and merged into a coronary mesh.

  • https://doi.org/10.5626/JOK.2016.43.4.419 인용 PDF KSCI