Image-selective segmentation model for multi-regions within the object of interest with application to medical disease

Ali, Haider and Faisal, Shah and Chen, Ke and Rada, Lavdie (2021) Image-selective segmentation model for multi-regions within the object of interest with application to medical disease. Visual Computer, 37 (5). pp. 939-955. ISSN 1432-2315 (https://doi.org/10.1007/s00371-020-01845-1)

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Abstract

Detection and extraction of an object of interest and accurate boundaries segmentation in a given image has been of interest in the last decades due to its application in different fields. To successfully segment a single object, interactive/selective segmentation techniques has been developed as a supplement to the existing global segmentation techniques. Even though existing interactive/selective segmentation techniques perform well in segmenting the images with prominent edges, those methods are less efficient or even fail in segmenting images having multi-regions of different intensity scale. In this paper, we design a new variational selective segmentation model which incorporates the idea of area-based fitting term along with a signed pressure force function based on a generalized average into a variational energy function. The new model is capable to capture the object of interest which can be single or multi-region within the object of interest. To evaluate the performance of our new model, we compare our results with state of the art models by showing same efficiency and reliability on detecting single-region and an outperforming for multi-region selective segmentation. Comparison tests were carried out on synthetic and real data images.

ORCID iDs

Ali, Haider, Faisal, Shah, Chen, Ke ORCID logoORCID: https://orcid.org/0000-0002-6093-6623 and Rada, Lavdie;
  • Item type:Article
    ID code:87542
    Dates:
    Date
    Event
    31 May 2021
    Published
    25 April 2020
    Published Online
    25 April 2020
    Accepted
    Notes:Copyright © 2020 Springer-Verlag. This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at https://doi.org/10.1007/s00371-020-01845-1.
    Subjects:Science > Mathematics > Electronic computers. Computer science
    Science > Mathematics
    Department:Faculty of Science > Mathematics and Statistics
    Depositing user: Pure Administrator
    Date deposited:07 Dec 2023 15:32
    Last modified:11 Nov 2024 14:07
    URI:https://strathprints.strath.ac.uk/id/eprint/87542
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