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Automatic segmentation of liver & lesion detection using H-minima transform and connecting component labeling

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Abstract

Automatic segmentation of the liver and the Lesion detection can be a very challenging task due to its variability in size, shape, position and the presence of other organs with similar intensities. Manual segmentation and detection of a tumor is a time-consuming task and greatly depends upon the expertise and experience of the physician. We proposed a method which consists of automatic segmentation and detection of liver and lesion using CT scan modality. H-minima transform filter, Otsu global thresholds, Morphological opening by reconstruction and modified Connected Component Labeling algorithms are applied for liver segmentation. To keep the technique simple and effective, an appropriate range of threshold values are defined to detect different types of lesions. Performance of the proposed system is evaluated and compared with the state-of-the art algorithms. The results of the comparison show that the proposed approach is robust and efficient due to its simplicity. The dice coefficient score for the hepatic segmentation is 94% while sensitivity and specificity for hepatic lesion are 93% and 87% respectively.

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Authors and Affiliations

  1. Center of excellence in Information Technology, Institute of Management Sciences, Peshawar, Pakistan

    Nazish Khan, Imran Ahmed, Mahreen Kiran & Hamoodur Rehman

  2. School of Computer Science and Engineering, Kyungpook National University, Daegu, Korea

    Sadia Din & Anand Paul

  3. Department of Computer Science and Engineering, National Institute of Technology, Andhra Pradesh, India

    Alavalapati Goutham Reddy

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  1. Nazish Khan
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  2. Imran Ahmed
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  3. Mahreen Kiran
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  4. Hamoodur Rehman
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  5. Sadia Din
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  6. Anand Paul
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  7. Alavalapati Goutham Reddy
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Correspondence to Alavalapati Goutham Reddy.

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Khan, N., Ahmed, I., Kiran, M. et al. Automatic segmentation of liver & lesion detection using H-minima transform and connecting component labeling. Multimed Tools Appl 79, 8459–8481 (2020). https://doi.org/10.1007/s11042-019-7347-4

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  • DOI: https://doi.org/10.1007/s11042-019-7347-4

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