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Impact of Enhancement for Coronary Artery Segmentation Based on Deep Learning Neural Network

  • Ahmed Ghazi BlaiechEmail author
  • Asma Mansour
  • Asma Kerkeni
  • Mohamed Hédi Bedoui
  • Asma Ben Abdallah
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11868)

Abstract

X-ray Coronary angiograms are intended to specify the global state of the artery system and therefore to detect and locate the zones of narrowing. Accurate coronary artery segmentation is a fundamental step in computer aided diagnosis of many diseases. In this paper, deep neural network based on U-Net architecture is proposed in order to improve the segmentation task for coronary images. In this context, various enhancement methods, like the adaptive histogram equalization and the multiscale technique with a Frangi filter are tested not only in normal conditions but also in the presence of noise to improve the system performance and to ensure its robustness against real conditions. Promising result are obtained and discussed for different performance criteria. This work will serve as a reference and motivation for researchers interested in the field of blood vessel segmentation by deep learning neural networks.

Keywords

Coronary artery segmentation Enhancement Deep learning 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ahmed Ghazi Blaiech
    • 1
    • 2
    Email author
  • Asma Mansour
    • 1
    • 3
  • Asma Kerkeni
    • 1
    • 3
  • Mohamed Hédi Bedoui
    • 1
  • Asma Ben Abdallah
    • 1
    • 3
  1. 1.Laboratoire de Technologie et Imagerie Médicale, Faculté de Médecine de MonastirUniversité de MonastirMonastirTunisia
  2. 2.Institut Supérieur des Sciences Appliquées et de Technologie de SousseUniversité de SousseSousseTunisia
  3. 3.Institut supérieur d’informatique et de MathématiquesUniversité de MonastirMonastirTunisia

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