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Computer-aided breast cancer diagnosis based on image segmentation and interval analysis

Automatika, 2020-07, Vol.61 (3), p.496-506 [Peer Reviewed Journal]

2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group 2020 ;2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This work is licensed under the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 0005-1144 ;EISSN: 1848-3380 ;DOI: 10.1080/00051144.2020.1785784

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  • Title:
    Computer-aided breast cancer diagnosis based on image segmentation and interval analysis
  • Author: Liu, Qing ; Liu, Zhigang ; Yong, Shenghui ; Jia, Kun ; Razmjooy, Navid
  • Subjects: Breast cancer ; computer-aided diagnosis ; Digitization ; image edge detection ; Image processing ; Image segmentation ; Independent variables ; interval analysis ; Lower bounds ; Mammography ; Medical imaging ; Noise intensity ; Taylor Inclusion Functions ; Uncertainty analysis
  • Is Part Of: Automatika, 2020-07, Vol.61 (3), p.496-506
  • Description: Uncertainties are one principal part of any practical problem. Like any application, image processing process has different unknown parts as uncertainties which are derived from different reasons like initial digitalization, sampling to noise, special domain, and intensity. This study presents strong image segmentation for the breast cancer mammography images by considering the interval uncertainties. To consider the system uncertainties, interval analysis has been proposed. The main prominence of this method is taking into account errors in independent variables. An unclear method has the element of subjectivity, while the deterministic methods are not applicable in all cases. Besides, this method is always guaranteed to include the exact result, no matter that its upper and lower bounds happen to be overestimated. The principle theory here is to develop the traditional Laplacian of Gaussian filter based on interval analysis to consider the intensity uncertainties. Experimental results are applied on MIAS that is a popular breast cancer database for medical image segmentation. The performance of the system has been compared with Prewitt, LoG and canny filters based on PSNR.
  • Publisher: Ljubljana: Taylor & Francis
  • Language: English;Croatian
  • Identifier: ISSN: 0005-1144
    EISSN: 1848-3380
    DOI: 10.1080/00051144.2020.1785784
  • Source: Taylor & Francis Open Access
    Alma/SFX Local Collection
    ProQuest Central
    DOAJ Directory of Open Access Journals
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