research-article

An improved method of breast MRI segmentation with simplified K-means clustered images

Authors:

Chang Oan Sung,

Hyung Do ChoiAuthors Info & Claims

RACS '11: Proceedings of the 2011 ACM Symposium on Research in Applied Computation

Pages 226 - 231

https://doi.org/10.1145/2103380.2103425

Published: 02 November 2011 Publication History

Abstract

The segmentation of breast Magnetic Resonance Imaging (MRI) has been a long term challenge due to the fuzzy boundaries among objects, small spots, and irregular object shapes in breast MRI. Even though intensity-based clustering algorithms such as K-means clustering and Fuzzy C-means clustering have been used widely for basic image segmentation, they resulted in complicated patterns for computer aided breast MRI diagnosis.

In this paper, we propose a new segmentation algorithm to improve the clustering results from K-means clustering algorithm with breast MRI. The major contribution of the proposed algorithm is that it simplifies breast MRI for the computer aided object analysis without loss of original MRI information. The proposed algorithm follows K-means clustering algorithm and explores neighbors and boundary information to redistribute unexpectedly clustered pixels and merge over-segmented objects from K-means clustering algorithm. We will discuss the results from the proposed algorithm and compare them with the result of K-means clustering algorithm.

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Cited By

Preetha RPriyadarsini MNisha J(2023)Comparative Study on Architecture of Deep Neural Networks for Segmentation of Brain Tumor using Magnetic Resonance ImagesIEEE Access10.1109/ACCESS.2023.334044311(138549-138567)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3340443
Mohammed YEl Garouani SJellouli I(2023)A survey of methods for brain tumor segmentation-based MRI imagesJournal of Computational Design and Engineering10.1093/jcde/qwac14110:1(266-293)Online publication date: 11-Jan-2023
https://doi.org/10.1093/jcde/qwac141
Si TPatra DMondal SMukherjee P(2022)Breast Lesion Segmentation in DCE-MRI using Multi-Objective Clustering with NSGA-II2022 International Conference on Innovative Trends in Information Technology (ICITIIT)10.1109/ICITIIT54346.2022.9744148(1-6)Online publication date: 12-Feb-2022
https://doi.org/10.1109/ICITIIT54346.2022.9744148
Show More Cited By

Index Terms

An improved method of breast MRI segmentation with simplified K-means clustered images
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Image segmentation
        Video segmentation

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Published In

cover image ACM Conferences

RACS '11: Proceedings of the 2011 ACM Symposium on Research in Applied Computation

November 2011

355 pages

ISBN:9781450310871

DOI:10.1145/2103380

General Chairs:
Rex E. Gantenbein
University of Wyoming
,
Tei-Wei Kuo
National Taiwan University, Taiwan

Copyright © 2011 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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CUSST: University of Suwon: Center for U-city Security & Surveillance Technology of the University of Suwon
KIISE: Korean Institute of Information Scientists and Engineers
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Association for Computing Machinery

New York, NY, United States

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Published: 02 November 2011

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RACS '11: Research in Applied Computation Symposium

November 2 - 5, 2011

Florida, Miami

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Overall Acceptance Rate 393 of 1,581 submissions, 25%

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Cited By

Preetha RPriyadarsini MNisha J(2023)Comparative Study on Architecture of Deep Neural Networks for Segmentation of Brain Tumor using Magnetic Resonance ImagesIEEE Access10.1109/ACCESS.2023.334044311(138549-138567)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3340443
Mohammed YEl Garouani SJellouli I(2023)A survey of methods for brain tumor segmentation-based MRI imagesJournal of Computational Design and Engineering10.1093/jcde/qwac14110:1(266-293)Online publication date: 11-Jan-2023
https://doi.org/10.1093/jcde/qwac141
Si TPatra DMondal SMukherjee P(2022)Breast Lesion Segmentation in DCE-MRI using Multi-Objective Clustering with NSGA-II2022 International Conference on Innovative Trends in Information Technology (ICITIIT)10.1109/ICITIIT54346.2022.9744148(1-6)Online publication date: 12-Feb-2022
https://doi.org/10.1109/ICITIIT54346.2022.9744148
Sravan VSwaraja KMeenakshi KKora PSamson M(2020)Magnetic Resonance Images Based Brain Tumor Segmentation- A critical survey2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184)10.1109/ICOEI48184.2020.9143045(1063-1068)Online publication date: Jun-2020
https://doi.org/10.1109/ICOEI48184.2020.9143045
Zhou YYu JXu ZWang YCao Q(2019)Improvement of Millimeter Wave Imaging by Using K-means Clustering2019 IEEE International Conference on Computational Electromagnetics (ICCEM)10.1109/COMPEM.2019.8778915(1-3)Online publication date: Mar-2019
https://doi.org/10.1109/COMPEM.2019.8778915
Saman SJamjala Narayanan S(2018)Survey on brain tumor segmentation and feature extraction of MR imagesInternational Journal of Multimedia Information Retrieval10.1007/s13735-018-0162-2Online publication date: 11-Dec-2018
https://doi.org/10.1007/s13735-018-0162-2
Niukkanen AArponen ONykänen AMasarwah ASutela ALiimatainen TVanninen RSudah M(2017)Quantitative Volumetric K-Means Cluster Segmentation of Fibroglandular Tissue and Skin in Breast MRIJournal of Digital Imaging10.1007/s10278-017-0031-131:4(425-434)Online publication date: 18-Oct-2017
https://doi.org/10.1007/s10278-017-0031-1
Taheri MHamer GSon SShin S(2016)Automated Single and Multi-Breast Tumor Segmentation Using Improved Watershed Technique in 2D MRI ImagesProceedings of the International Conference on Research in Adaptive and Convergent Systems10.1145/2987386.2987421(61-66)Online publication date: 11-Oct-2016
https://dl.acm.org/doi/10.1145/2987386.2987421
Kanadam KChereddy S(2016)Mammogram classification using sparse-ROIExpert Systems with Applications: An International Journal10.1016/j.eswa.2016.03.03757:C(204-213)Online publication date: 15-Sep-2016
https://dl.acm.org/doi/10.1016/j.eswa.2016.03.037
Moftah HAzar AAl-Shammari EGhali NHassanien AShoman M(2014)Adaptive k-means clustering algorithm for MR breast image segmentationNeural Computing and Applications10.1007/s00521-013-1437-424:7-8(1917-1928)Online publication date: 1-Jun-2014
https://dl.acm.org/doi/10.1007/s00521-013-1437-4

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