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Tracking Anterior Mitral Leaflet in Echocardiographic Videos Using Morphological Operators and Active Contours

  • Malik Saad SultanEmail author
  • Nelson Martins
  • Eva Costa
  • Diana Veiga
  • Manuel João Ferreira
  • Sandra Mattos
  • Miguel Tavares Coimbra
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 881)

Abstract

Rheumatic heart disease is the result of damage to the heart valves, more often the mitral valve. The heart valves leaflets get inflamed, scarred and stretched which interrupts the normal blood flow, resulting into serious health condition. Measuring and quantifying clinically relevant features, like thickness, mobility and shape can help to analyze the functionality of the valve, identify early cases of disease and reduce the disease burden. To obtain these features, the first step is to automatically delineate the relevant structures, such as the anterior mitral valve leaflet, throughout the echocardiographic video. In this work, we proposed a near real time method to track the anterior mitral leaflet in ultrasound videos using the parasternal long axis view. The method is semi-automatic, requiring a manual delineation of the anterior mitral leaflet in the first frame of the video. The method uses mathematical morphological techniques to obtain the rough boundaries of the leaflet and are further refined by the localized active contour framework. The mobility of the leaflet was also obtained, providing us the base to analyze the functionality of the valve (opening and closing). The algorithm was tested on 67 videos with 6432 frames. It outperformed with respect to the time consumption (0.4 s/frame), with the extended modified Hausdorff distance error of 3.7 pixels and the improved tracking performance (less failure).

Keywords

Ultrasound images Medical image processing Active contours Segmentation and tracking Mitral valve 

Notes

Acknowledgements

This article is a result of the project NORTE-01-0247-FEDER-003507-RHDecho, co-funded by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). This work also had the collaboration of the Fundação para a Ciência a e Tecnologia (FCT) grant no: PD/BD/105761/2014 and has contributions from the project NanoSTIMA, NORTE-01-0145-FEDER-000016, supported by Norte Portugal Regional Operational Programme (NORTE 2020), through Portugal 2020 and the European Regional Development Fund (ERDF).

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Malik Saad Sultan
    • 1
    Email author
  • Nelson Martins
    • 1
    • 2
  • Eva Costa
    • 2
  • Diana Veiga
    • 2
  • Manuel João Ferreira
    • 2
    • 3
  • Sandra Mattos
    • 4
  • Miguel Tavares Coimbra
    • 1
  1. 1.Instituto de TelecomunicaçõesFaculdade de Ciências da Universidade do PortoPortoPortugal
  2. 2.Neadvance - Machine Vision, S.A.BragaPortugal
  3. 3.Centro AlgoritmiUniversity of MinhoGuimarãesPortugal
  4. 4.Círculo do Coração de PernambucoRecifeBrazil

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