research-article

Dynamic weighted histogram equalization for contrast enhancement using for Cancer Progression Detection in medical imaging

Authors:

Gohar Rehman Chughtai,

Bin LuoAuthors Info & Claims

SPML '18: Proceedings of the 2018 International Conference on Signal Processing and Machine Learning

Pages 93 - 98

https://doi.org/10.1145/3297067.3297086

Published: 28 November 2018 Publication History

Abstract

Contrast-enhancement is very essential and ideal to produce a maximum contrast of many computer-vision and image-processing applications with minimum brightness error. Moreover, there is no mechanism to control the brightness error, contrast in conventional histogram equalization and mean shift problem that is usually occurs when the histogram equalization based contrast enhancement methods has used. The purpose of this research is to devise an intelligently robust framework based on the image data that is collected during several phases of Ultrasound (US) cancer image by automating the real-time image enhancement, segmentation, classification and progression the widely spreading of cancer disease at initial stages moreover, we have proposed a new methodology of contrast optimization that overcomes the mean-shift problem. The data is collected and preprocessed, while image segmentation techniques has used to partition and extract the concerned object from the enhanced image.

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Cited By

Gusa RHidayat RNugroho H(2023)Performance Analysis of Enhancement Methods on Fetal Ultrasound Images2023 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)10.1109/IAICT59002.2023.10205792(326-331)Online publication date: 13-Jul-2023
https://doi.org/10.1109/IAICT59002.2023.10205792
Ghaffar Nia NKaplanoglu ENasab A(2023)Evaluation of artificial intelligence techniques in disease diagnosis and predictionDiscover Artificial Intelligence10.1007/s44163-023-00049-53:1Online publication date: 30-Jan-2023
https://doi.org/10.1007/s44163-023-00049-5
Iqbal MAbbasi RBin Heyat MAkhtar FAbdelgeliel AAlbogami SFayad EIqbal M(2022)Recognition of mRNA N4 Acetylcytidine (ac4C) by Using Non-Deep vs. Deep LearningApplied Sciences10.3390/app1203134412:3(1344)Online publication date: 27-Jan-2022
https://doi.org/10.3390/app12031344
Show More Cited By

Index Terms

Dynamic weighted histogram equalization for contrast enhancement using for Cancer Progression Detection in medical imaging
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Classification and regression trees

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cover image ACM Other conferences

SPML '18: Proceedings of the 2018 International Conference on Signal Processing and Machine Learning

November 2018

177 pages

ISBN:9781450366052

DOI:10.1145/3297067

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 28 November 2018

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The National High Technology Research and Development program of China (863 program)

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SPML '18

SPML '18: 2018 International Conference on Signal Processing and Machine Learning

November 28 - 30, 2018

Shanghai, China

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Cited By

Gusa RHidayat RNugroho H(2023)Performance Analysis of Enhancement Methods on Fetal Ultrasound Images2023 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)10.1109/IAICT59002.2023.10205792(326-331)Online publication date: 13-Jul-2023
https://doi.org/10.1109/IAICT59002.2023.10205792
Ghaffar Nia NKaplanoglu ENasab A(2023)Evaluation of artificial intelligence techniques in disease diagnosis and predictionDiscover Artificial Intelligence10.1007/s44163-023-00049-53:1Online publication date: 30-Jan-2023
https://doi.org/10.1007/s44163-023-00049-5
Iqbal MAbbasi RBin Heyat MAkhtar FAbdelgeliel AAlbogami SFayad EIqbal M(2022)Recognition of mRNA N4 Acetylcytidine (ac4C) by Using Non-Deep vs. Deep LearningApplied Sciences10.3390/app1203134412:3(1344)Online publication date: 27-Jan-2022
https://doi.org/10.3390/app12031344
Nawabi AJinfang SAbbasi RIqbal MHeyat MAkhtar FWu KTwumasi B(2022)Segmentation of Drug-Treated Cell Image and Mitochondrial-Oxidative Stress Using Deep Convolutional Neural NetworkOxidative Medicine and Cellular Longevity10.1155/2022/56417272022(1-14)Online publication date: 26-May-2022
https://doi.org/10.1155/2022/5641727
Abbasi RChen JAl-Otaibi YRehman AAbbas ACui W(2021)RDH-based dynamic weighted histogram equalization using for secure transmission and cancer predictionMultimedia Systems10.1007/s00530-020-00718-wOnline publication date: 4-Jan-2021
https://doi.org/10.1007/s00530-020-00718-w
Rani GAgarwal M(2020)Contrast Enhancement Using Optimum Threshold SelectionInternational Journal of Software Innovation10.4018/IJSI.20200701078:3(96-118)Online publication date: Jul-2020
https://doi.org/10.4018/IJSI.2020070107

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