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Automated Lesion Image Segmentation Based on Novel Histogram-Based K-Means Clustering Using COVID-19 Chest CT Images

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Third Congress on Intelligent Systems (CIS 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 613))

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Abstract

COVID-19 has wreaked havoc in the world, causing epidemic scenarios in the majority of countries. As a result, medical practitioners urgently seek an early detection, rapid, and precise diagnosis technique for COVID-19 infection and also find a reliable automated method for identifying and quantifying infected lung areas would be extremely beneficial. Discovering infected regions in chest Computed Tomography (CT) scan images is the greatest approach to halt the transmission of pathogens. The analysis of lung CT scan images is the first step segmentation of lung CT images in lung image analysis. Due to intensity inhomogeneity and the existence of artifacts, the primary problems of segmentation algorithms have been accentuated. This research proposes a novel image segmentation method, Novel Histogram-Based K-Means Clustering (NHKMC) to localize the diseased lesion. The average Dice Similarity Coefficient (DSC), Accuracy, Sensitivity, Specificity, and Structural Similarity Index Method (SSIM) for the proposed NHKMC segmentation task are obtained as 86.00%, 82.00%, 86.07%, 86.18%, 85.09%, and 86.04%, respectively. The outcomes of the experiments demonstrate that the proposed approach Novel Histogram-Based K-Means Clustering (NHKMC) performs well and has a great deal of potential for segmenting the COVID-19 lesion region of the CT scan image.

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Acknowledgements

The foremost writer is extremely grateful for the monetary support provided by University Research Fellowship Program, Periyar University, Salem, for carryout this research in the Department of Computer Science, Periyar University, Salem, Tamil Nadu, India. The next writer gratefully recognizes the UGC-Special Assistance Program (SAP) for funding at the level of DRS-II in the Department of Computer Science, Periyar University, Salem, Tamil Nadu, India.

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Authors and Affiliations

  1. Department of Computer Science, Periyar University, Salem, India

    S. Nivetha & H. Hannah Inbarani

Authors
  1. S. Nivetha
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  2. H. Hannah Inbarani
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Corresponding author

Correspondence to H. Hannah Inbarani .

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Editors and Affiliations

  1. Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bengaluru, Karnataka, India

    Sandeep Kumar

  2. Department of Computer Science and Engineering, Rajasthan Technical University, Kota, Rajasthan, India

    Harish Sharma

  3. Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bengaluru, Karnataka, India

    K. Balachandran

  4. School of Civil, Environmental and Architectural Engineering, Korea University, Seoul, Korea (Republic of)

    Joong Hoon Kim

  5. South Asian University, New Delhi, Delhi, India

    Jagdish Chand Bansal

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Nivetha, S., Hannah Inbarani, H. (2023). Automated Lesion Image Segmentation Based on Novel Histogram-Based K-Means Clustering Using COVID-19 Chest CT Images. In: Kumar, S., Sharma, H., Balachandran, K., Kim, J.H., Bansal, J.C. (eds) Third Congress on Intelligent Systems. CIS 2022. Lecture Notes in Networks and Systems, vol 613. Springer, Singapore. https://doi.org/10.1007/978-981-19-9379-4_55

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