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Colonoscopy contrast-enhanced by intuitionistic fuzzy soft sets for polyp cancer localization

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F20%3A10245956" target="_blank" >RIV/61989100:27240/20:10245956 - isvavai.cz</a>

  • Result on the web

    <a href="https://www.sciencedirect.com/science/article/pii/S1568494620304312?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S1568494620304312?via%3Dihub</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1016/j.asoc.2020.106492" target="_blank" >10.1016/j.asoc.2020.106492</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Colonoscopy contrast-enhanced by intuitionistic fuzzy soft sets for polyp cancer localization

  • Original language description

    Medical images often suffer from low contrast, irregular gray-level spacing and contain a lot of uncertainties due to constraints of imaging devices and environment (various lighting conditions) when capturing images. In order to achieve any clinical-diagnosis method for medical imaging with better comprehensibility, image contrast enhancement algorithms would be appropriate to improve the visual quality of medical images. In this paper, an automated image enhancement method is presented for colonoscopy images based on the intuitionistic fuzzy soft set. The fuzzy soft set is used to model the intuitionistic fuzzy soft image matrix based on a set of soft features of the colonoscopy images. The technique decomposes the fuzzy image into multiple blocks and estimates a soft-score based on an adaptive soft parametric hesitancy map by using the hesitant entropy for each block to quantify the uncertainties. In the processing stage, an adaptive intensity modification process is done for each block according to its soft-score. These scores are accurately addressed the gray-level ambiguities in colonoscopy images that lead to better results. Finally, the enhanced image achieved by performing a defuzzification together with all unprocessed blocks. Qualitative and quantitative assessments demonstrate that the proposed method improves image contrast and region-of-interest of polyps in colonogram. Experimental results on enhancing a large CVC-Clinic-DB and ASU-Mayo clinic colonoscopy benchmark datasets show that the proposed method outperforms the state-of-the-art medical image enhancement methods. (C) 2020 Elsevier B.V. All rights reserved.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

    <a href="/en/project/EF16_027%2F0008463" target="_blank" >EF16_027/0008463: Science without borders</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Others

  • Publication year

    2020

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    Applied Soft Computing

  • ISSN

    1568-4946

  • e-ISSN

  • Volume of the periodical

    95

  • Issue of the periodical within the volume

    October

  • Country of publishing house

    US - UNITED STATES

  • Number of pages

    20

  • Pages from-to

  • UT code for WoS article

    000576775900016

  • EID of the result in the Scopus database

    2-s2.0-85086996601