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Proceedings of the 5th International Conference on Frontiers in Intelligent Computing: Theory and Applications pp 633–639Cite as

Level Set Based Liver Segmentation and Classification by SVM

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 516))

Abstract

Liver segmentation from CT image is the key exploration works in representing a liver, which has incredible effect on the examination of liver issue. Hence, numerous computer-aided segmentation approaches have been proposed to partition liver locale from medical image automatically in the past numerous years. A method for liver segmentation system is proposed by consolidating level set based method with Pseudo Zenerike moment and GLDM Features. The objective of proposed algorithm is to solve the segmentation issue which is created by indistinguishable intensities between liver region and its adjacent tissues. Radial Basis Function SVM is used in this work to classify the type of the tumor.

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Author information

Authors and Affiliations

  1. Department of ISE, Dayananda Sagar College of Engineering, Bengaluru, India

    Mallikarjun Kesaratti, Sunanda Dixit & B. I. Khodanpur

Authors
  1. Mallikarjun Kesaratti
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  2. Sunanda Dixit
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  3. B. I. Khodanpur
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Corresponding author

Correspondence to Mallikarjun Kesaratti .

Editor information

Editors and Affiliations

  1. Anil Neerukonda Inst. of Tech. & Sci., Prof., Dept. of Computer Sci. & Engg. Anil Neerukonda Inst. of Tech. & Sci., Vishakapatnam, Andhra Pradesh, India

    Suresh Chandra Satapathy

  2. Professional Colleges (SRMGPC), Shri Ramswaroop Memorial Group of Professional Colleges (SRMGPC), Lucknow, Uttar Pradesh, India

    Vikrant Bhateja

  3. SCIS, University of Hyderabad , Hyderabad, India

    Siba K. Udgata

  4. KIIT University, School of Computer Engineering KIIT University, Bhubaneswar, Odisha, India

    Prasant Kumar Pattnaik

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© 2017 Springer Nature Singapore Pte Ltd.

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Kesaratti, M., Dixit, S., Khodanpur, B.I. (2017). Level Set Based Liver Segmentation and Classification by SVM. In: Satapathy, S., Bhateja, V., Udgata, S., Pattnaik, P. (eds) Proceedings of the 5th International Conference on Frontiers in Intelligent Computing: Theory and Applications . Advances in Intelligent Systems and Computing, vol 516. Springer, Singapore. https://doi.org/10.1007/978-981-10-3156-4_67

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  • DOI: https://doi.org/10.1007/978-981-10-3156-4_67

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-3155-7

  • Online ISBN: 978-981-10-3156-4

  • eBook Packages: EngineeringEngineering (R0)

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