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A Novel Approach to Enhance Effectiveness of Image Segmentation Techniques on Extremely Noisy Medical Images

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Recent Trends in Image Processing and Pattern Recognition (RTIP2R 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1704))

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Abstract

Through this study, I contribute towards segmentation of liver areas and have proposed additional improvements, which positively influence image segmentation. In this study, I have subjected medical images from LiTS - Liver Tumour Segmentation Challenge, which are extremely noisy, to various image segmentation techniques belonging to fully automatic and semi-automatic categories. These varied techniques implement different approaches towards image segmentation problem. All the techniques had initially failed to segment the images with very poor results. Commonly used filters for pre-processing, such as median filter, top hat filter, wiener filter, etc., were ineffective in reducing the noise effectively. Through this study, I have introduced a new combinatorial approach which not only is easier to implement but also much faster as well and resulted in much more enhanced input image quality that significantly improved the segmentation outcomes. Our approach has reduced noise, sharpened the edges, “localized” the segmentation problem before subjecting to various segmentation techniques. The techniques which had failed previously now could segment the images with improved speed of execution, efficiency and accuracy. I have studied our approach on 10 well known image segmentation techniques. Accuracy of these segmentation techniques was determined by computing Jaccard Index, Dice Coefficient and Hausdorff Distance.

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Acknowledgements

The author would like to acknowledge the organizing committee of LiTS - Liver Tumor Segmentation Challenge (https://competitions.codalab.org/competitions/17094) for making dataset along with ground truth publicly available for research purposes.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. Department of Electronics, LAD College, Seminary Hills Campus, Nagpur, 440006, Maharashtra, India

    Anuja Deshpande

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  1. Anuja Deshpande
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Correspondence to Anuja Deshpande .

Editor information

Editors and Affiliations

  1. University of South Dakota, Vermillion, SD, USA

    KC Santosh

  2. Texas A&M University, College Station, TX, USA

    Ayush Goyal

  3. University of Luxembourg, Luxembourg, Luxembourg

    Djamila Aouada

  4. University of Derby, Derby, UK

    Aaisha Makkar

  5. University of Minnesota, Minneapolis, MN, USA

    Yao-Yi Chiang

  6. IIIT Allahabad, Allahabad, India

    Satish K Singh

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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Deshpande, A. (2023). A Novel Approach to Enhance Effectiveness of Image Segmentation Techniques on Extremely Noisy Medical Images. In: Santosh, K., Goyal, A., Aouada, D., Makkar, A., Chiang, YY., Singh, S.K. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2022. Communications in Computer and Information Science, vol 1704. Springer, Cham. https://doi.org/10.1007/978-3-031-23599-3_8

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  • DOI: https://doi.org/10.1007/978-3-031-23599-3_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-23598-6

  • Online ISBN: 978-3-031-23599-3

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