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Multi-atlas Segmentation: Label Propagation and Fusion Based Approach

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Advances in Computer Communication and Computational Sciences

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 760))

Abstract

To understand the profundity of the subject in importance, revision of previous work has always played a vigorous role in developing interest and curiosity. For morphological assessment and measurement of quantitative parameters of biomedical structures, segmentation is done. Number of segmentation techniques has been widely used in the field of image processing since four decades. However, the problems related to segmentation still remain candid providing no optimum solution. Segmentation process is always considered as difficult due to a variation in medical images, image resolution, pixel intensity, signal variability, noise, and other artifacts. From the previous study, multi-atlas segmentation (MAS) techniques have proven to be a flexible and robust approach for medical images. Multi-atlas segmentation works in two steps: first propagation of the manually labeled images to the target image and then combining the transfer images to get the best segmentation result. Label propagation and label fusion using multiple atlases have made multi-atlas segmentation approach as forefront of segmentation research. This survey paper provides a snapshot of the current progress in the field of segmentation, registration, and label propagation.

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Notes

  1. 1.

    Atlases refers to model for a collection of images with parameters that learned from training data set. These are the certain type of manually delineated images, which are labeled by experts. Atlas is known with various names as training images, reference images, template images.

  2. 2.

    Voxels is a unit of graphic information. It defines a point in three-dimensional space with its x, y, and z coordinates. These coordinates are position, color, and density.

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Authors and Affiliations

  1. Amity University Haryana, Gurgaon, India

    Shruti Karkra & Janak Kumar B. Patel

Authors
  1. Shruti Karkra
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  2. Janak Kumar B. Patel
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Corresponding author

Correspondence to Shruti Karkra .

Editor information

Editors and Affiliations

  1. Department of Mathematics & Computer Science, University of Missouri, St. Louis, Missouri, USA

    Sanjiv K. Bhatia

  2. Computer Science Engineering Department, ABES Engineering College, Ghaziabad, India

    Shailesh Tiwari

  3. Department of Computer Science & Engineering, Motilal Nehru National Institute of Technology, Allahabad, Uttar Pradesh, India

    Krishn K. Mishra

  4. Department of Computer Science & Engineering, ABES Engineering College, Ghaziabad, India

    Munesh C. Trivedi

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Karkra, S., Patel, J.K.B. (2019). Multi-atlas Segmentation: Label Propagation and Fusion Based Approach. In: Bhatia, S., Tiwari, S., Mishra, K., Trivedi, M. (eds) Advances in Computer Communication and Computational Sciences. Advances in Intelligent Systems and Computing, vol 760. Springer, Singapore. https://doi.org/10.1007/978-981-13-0344-9_28

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