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Modified Otsu thresholding based level set and local directional ternary pattern technique for liver tumor segmentation

Author

Listed:
  • Deepak S. Uplaonkar

    (Poojya Doddappa Appa College of Engineering)

  • Virupakshappa

    (Sharnbasva University)

  • Nagabhushan Patil

    (Poojya Doddappa Appa College of Engineering)

Abstract

In recent times, the liver tumors are one of the leading causes of death, hence automated segmentation of liver tumors helps physicians in early diagnosis and treatment options. In this paper, a novel segmentation technique is proposed for accurate segmentation of tumor regions from the liver Ultrasound images. Initially, liver Ultrasound images are collected from a real time dataset, which comprises of 105 liver metastases images. Then, label removal is accomplished by using binary thresholding and morphological operation to remove text from the liver Ultrasound images. Additionally, the quality of liver Ultrasound images is improved by applying contrast limited adaptive histogram equalization that improves original image contrast and preserves the image brightness. After image enhancement, Otsu thresholding based level set with enhanced edge indicator function and local directional ternary pattern technique is proposed for segmenting liver lesion/tumor region from the enhanced images. In the experimental phase, the proposed technique performance is validated in light of Matthews’s correlation coefficient, Jaccard coefficient, Dice coefficient, accuracy, precision and f-score. The simulation result showed that the proposed technique achieved 99.43% of segmentation accuracy, which is 5.43% higher than the existing graph based approach.

Suggested Citation

  • Deepak S. Uplaonkar & Virupakshappa & Nagabhushan Patil, 2024. "Modified Otsu thresholding based level set and local directional ternary pattern technique for liver tumor segmentation," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 15(1), pages 73-83, January.
    • Handle: RePEc:spr:ijsaem:v:15:y:2024:i:1:d:10.1007_s13198-022-01637-x
    DOI: 10.1007/s13198-022-01637-x
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    References listed on IDEAS

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    1. Surya Kant & Irshad Ahmad Ansari, 2016. "An improved K means clustering with Atkinson index to classify liver patient dataset," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 7(1), pages 222-228, December.
    2. Shilpa Srivastava & Millie Pant & Ritu Agarwal, 2020. "Role of AI techniques and deep learning in analyzing the critical health conditions," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 11(2), pages 350-365, April.
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