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Effectiveness of Region Growing Based Segmentation Technique for Various Medical Images - A Study

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Data Science and Analytics (REDSET 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 799))

Abstract

Due to rapid and continuous progress along with higher fidelity rate, medical imaging is becoming one of the most crucial fields in scientific imaging. Both microscopic and macroscopic modalities are probed and their resulting images are analyzed and interpreted in medical imaging for the early detection, diagnosis, and treatment of various ailments like a tumor, cancer, gallstones, etc. Although the field of medical image processing is growing significantly and persistently, there still exist a number of challenges in this field. Among these challenges, the frequently occurring and critically significant one is image segmentation. The theme work presented in this paper includes challenges involved and comparative analysis of segmentation using region growing techniques frequently utilized in various biomedical images like retinal vessel image, mammograms, magnetic resonance images, PET-CT image, coronary artery image, microscopy image, ultrasound image, etc. It discusses the effectiveness of the region growing technique applied on various medical images.

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Authors and Affiliations

  1. The NorthCap University, Gurgaon, 121102, Haryana, India

    Manju Dabass, Sharda Vashisth & Rekha Vig

Authors
  1. Manju Dabass
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  2. Sharda Vashisth
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  3. Rekha Vig
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Correspondence to Manju Dabass or Sharda Vashisth .

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Editors and Affiliations

  1. University of Arkansas, Fayetteville, AR, USA

    Brajendra Panda

  2. GD Goenka University, Gurugram, Haryana, India

    Sudeep Sharma

  3. GD Goenka University, Gurugram, Haryana, India

    Nihar Ranjan Roy

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Dabass, M., Vashisth, S., Vig, R. (2018). Effectiveness of Region Growing Based Segmentation Technique for Various Medical Images - A Study. In: Panda, B., Sharma, S., Roy, N. (eds) Data Science and Analytics. REDSET 2017. Communications in Computer and Information Science, vol 799. Springer, Singapore. https://doi.org/10.1007/978-981-10-8527-7_21

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  • DOI: https://doi.org/10.1007/978-981-10-8527-7_21

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  • Print ISBN: 978-981-10-8526-0

  • Online ISBN: 978-981-10-8527-7

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