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Accurate Vessel Segmentation with Progressive Contrast Enhancement and Canny Refinement

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Computer Vision -- ACCV 2014 (ACCV 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9005))

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Abstract

Vessel segmentation is a key step for various medical applications, such as diagnosis assistance, quantification of vascular pathology, and treatment planning. This paper describes an automatic vessel segmentation framework which can achieve highly accurate segmentation even in regions of low contrast and signal-to-noise-ratios (SNRs) and at vessel boundaries with disturbance induced by adjacent non-vessel pixels. There are two key contributions of our framework. The first is a progressive contrast enhancement method which adaptively improves contrast of challenging pixels that were otherwise indistinguishable, and suppresses noises by weighting pixels according to their likelihood to be vessel pixels. The second contribution is a method called canny refinement which is based on a canny edge detection algorithm to effectively re-move false positives around boundaries of vessels. Experimental results on a public retinal dataset and our clinical cerebral data demonstrate that our approach outperforms state-of-the-art methods including the vesselness based method [1] and the optimally oriented flux (OOF) based method [2].

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Acknowledgement

This work was supported in part by the Society of Interventional Radiology (SIR) Foundation Dr. Ernest J. Ring Academic Development Grant, The Aneurysm and AVM Foundation (TAAF) Cerebrovascular Research Grant, and a UCLA Radiology Exploratory Research Grant.

Author information

Authors and Affiliations

  1. Department of Electronics Information Engineering, HUST, Wuhan, China

    Xin Yang

  2. Department of Electrical Computer Engineering, UCSB, Santa Barbara, CA, USA

    Xin Yang & Kwang-Ting Tim Cheng

  3. Division of Interventional Neuroradiology, UCLA, Medical School, Los Angeles, CA, USA

    Aichi Chien

Authors
  1. Xin Yang
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  2. Kwang-Ting Tim Cheng
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  3. Aichi Chien
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Corresponding author

Correspondence to Xin Yang .

Editor information

Editors and Affiliations

  1. Technische Universität München, Garching, Bayern, Germany

    Daniel Cremers

  2. University of Adelaide, Adelaide, South Australia, Australia

    Ian Reid

  3. Keio University, Yokohama, Kanagawa, Japan

    Hideo Saito

  4. University of California at Merced, Merced, California, USA

    Ming-Hsuan Yang

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Yang, X., Cheng, KT., Chien, A. (2015). Accurate Vessel Segmentation with Progressive Contrast Enhancement and Canny Refinement. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision -- ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9005. Springer, Cham. https://doi.org/10.1007/978-3-319-16811-1_1

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  • DOI: https://doi.org/10.1007/978-3-319-16811-1_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16810-4

  • Online ISBN: 978-3-319-16811-1

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