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Cancer Tissue Segmentation in Various Conditions with Semiautomatic and Automatic Approach

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Advanced Prognostic Predictive Modelling in Healthcare Data Analytics

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 64))

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Abstract

Medical image segmentation is an essential and complex task due to the complexity of images obtained from different modalities. The basic methods used for segmentation are discussed in this chapter. For semiautomatic approaches, human intervention is needed as guidance of initial points. Fully automatic methods do not require the prior information for the segmentation process. The researcher used various machine learning algorithms to make the segmentation process automatic or semiautomatic. But the single method is insufficient to segment the medical images; hence, multiple algorithms with modification in original algorithms have been proposed by the researchers. These methods surely have been given more accurate results.

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Author information

Authors and Affiliations

  1. Terna Engineering College, Navi Mumbai, India

    Ujwala Gaikwad

  2. Thakur College of Engineering and Technology, Mumbai, India

    Ujwala Gaikwad & Kamal Shah

Authors
  1. Ujwala Gaikwad
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  2. Kamal Shah
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Corresponding author

Correspondence to Ujwala Gaikwad .

Editor information

Editors and Affiliations

  1. Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA

    Sudipta Roy

  2. Department of Computer Engineering, J. C. Bose University of Science and Technology, YMCA, Faridabad, India

    Lalit Mohan Goyal

  3. Department of Computer Science and Engineering, G. B. Pant Government Engineering College, New Delhi, India

    Mamta Mittal

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Gaikwad, U., Shah, K. (2021). Cancer Tissue Segmentation in Various Conditions with Semiautomatic and Automatic Approach. In: Roy, S., Goyal, L.M., Mittal, M. (eds) Advanced Prognostic Predictive Modelling in Healthcare Data Analytics. Lecture Notes on Data Engineering and Communications Technologies, vol 64. Springer, Singapore. https://doi.org/10.1007/978-981-16-0538-3_8

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