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Segmentation of Lungs from Chest X Rays Using Firefly Optimized Fuzzy C-Means and Level Set Algorithm

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Recent Trends in Image Processing and Pattern Recognition (RTIP2R 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1036))

Abstract

Segmentation of lungs from chest x ray is a non trivial task required as a preprocessing step for detection of different diseases like cardiomelagy, tuberculosis, pneumonia. High accuracy in segmentation of lung results in high accuracy of detection of diseases from lungs. For the past four decades multiple techniques were proposed for automatic segmentation of lungs. In this paper, we propose a hybrid segmentation technique based on firefly optimized fuzzy c-means clustering algorithm. The output of the fuzzy c-means is given to level set to finalize the segmentation of the lungs. The performance of the proposed technique is evaluated using two public chest x ray datasets: JRST and Montgomery County. JRST contains 247 chest x-rays and MC dataset contains 138 chest x-rays. The Jaccard coefficient for the proposed segmentation technique is 95.1 which is on par with the state of art segmentation techniques.

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Author information

Authors and Affiliations

  1. Vignan’s Foundation for Science Technology and Research, Guntur, Andhra Pradesh, India

    Ebenezer Jangam

  2. IIT (ISM) Dhanbad, Dhanbad, Jharkhand, India

    A. C. S. Rao

Authors
  1. Ebenezer Jangam
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  2. A. C. S. Rao
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Corresponding author

Correspondence to Ebenezer Jangam .

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of South Dakota, Vermillion, SD, USA

    K. C. Santosh

  2. Solapur University, Solapur, India

    Ravindra S. Hegadi

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Jangam, E., Rao, A.C.S. (2019). Segmentation of Lungs from Chest X Rays Using Firefly Optimized Fuzzy C-Means and Level Set Algorithm. In: Santosh, K., Hegadi, R. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2018. Communications in Computer and Information Science, vol 1036. Springer, Singapore. https://doi.org/10.1007/978-981-13-9184-2_27

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  • DOI: https://doi.org/10.1007/978-981-13-9184-2_27

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-9183-5

  • Online ISBN: 978-981-13-9184-2

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