Breast Tumor Segmentation Using K-Means Clustering and Cuckoo Search Optimization

Amir Arjmand; Saeed Meshgini; Reza Afrouzian; Ali Farzamnia

doi:10.1109/ICCKE48569.2019.8964794

Breast Tumor Segmentation Using K-Means Clustering and Cuckoo Search Optimization

Amir Arjmand, Saeed Meshgini, Reza Afrouzian, Ali Farzamnia

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

21 Citations (Scopus)

Abstract

Today, there are various methods for detecting tumors in breasts. But researchers are still trying to find an exact automatic way to segment the tumors from breast images. In this paper we propose a clustering-based algorithm for automatic tumor segmentation in the MRI samples. In the proposed method, we use k-means clustering algorithm for segmentation and also we use cuckoo search optimization (CSO) algorithm to initialize centroids in the k-means algorithm. We have used RIDER breast dataset to evaluate the proposed method and results clearly show that our algorithm outperforms similar methods such as simple k-means clustering algorithm and Fuzzy C-Means (FCM).

Original language	English
Title of host publication	2019 9th International Conference on Computer and Knowledge Engineering, ICCKE 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	305-308
Number of pages	4
ISBN (Electronic)	9781728150758
ISBN (Print)	9781728150765
DOIs	https://doi.org/10.1109/ICCKE48569.2019.8964794
Publication status	Published - 23 Jan 2020
Externally published	Yes
Event	9th International Conference on Computer and Knowledge Engineering - Mashhad, Iran, Islamic Republic of Duration: 24 Oct 2019 → 25 Oct 2019 Conference number: 9

Publication series

Name	International Conference on Computer and Knowledge Engineering, ICCKE
Publisher	IEEE
Volume	2020
ISSN (Print)	2375-1304
ISSN (Electronic)	2643-279X

Conference

Conference	9th International Conference on Computer and Knowledge Engineering
Abbreviated title	ICCKE 2019
Country/Territory	Iran, Islamic Republic of
City	Mashhad
Period	24/10/19 → 25/10/19

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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Arjmand, A., Meshgini, S., Afrouzian, R., & Farzamnia, A. (2020). Breast Tumor Segmentation Using K-Means Clustering and Cuckoo Search Optimization. In 2019 9th International Conference on Computer and Knowledge Engineering, ICCKE 2019 (pp. 305-308). [8964794] (International Conference on Computer and Knowledge Engineering, ICCKE ; Vol. 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCKE48569.2019.8964794

Arjmand, Amir ; Meshgini, Saeed ; Afrouzian, Reza et al. / Breast Tumor Segmentation Using K-Means Clustering and Cuckoo Search Optimization. 2019 9th International Conference on Computer and Knowledge Engineering, ICCKE 2019. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 305-308 (International Conference on Computer and Knowledge Engineering, ICCKE ).

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title = "Breast Tumor Segmentation Using K-Means Clustering and Cuckoo Search Optimization",

abstract = "Today, there are various methods for detecting tumors in breasts. But researchers are still trying to find an exact automatic way to segment the tumors from breast images. In this paper we propose a clustering-based algorithm for automatic tumor segmentation in the MRI samples. In the proposed method, we use k-means clustering algorithm for segmentation and also we use cuckoo search optimization (CSO) algorithm to initialize centroids in the k-means algorithm. We have used RIDER breast dataset to evaluate the proposed method and results clearly show that our algorithm outperforms similar methods such as simple k-means clustering algorithm and Fuzzy C-Means (FCM).",

keywords = "Clustering, Cuckoo Search Optimization (CSO), K-means algorithm, Magnetic Resonance Imaging (MRI), Tumor Segmentation",

author = "Amir Arjmand and Saeed Meshgini and Reza Afrouzian and Ali Farzamnia",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 9th International Conference on Computer and Knowledge Engineering, ICCKE 2019 ; Conference date: 24-10-2019 Through 25-10-2019",

year = "2020",

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doi = "10.1109/ICCKE48569.2019.8964794",

language = "English",

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Arjmand, A, Meshgini, S, Afrouzian, R & Farzamnia, A 2020, Breast Tumor Segmentation Using K-Means Clustering and Cuckoo Search Optimization. in 2019 9th International Conference on Computer and Knowledge Engineering, ICCKE 2019., 8964794, International Conference on Computer and Knowledge Engineering, ICCKE , vol. 2020, Institute of Electrical and Electronics Engineers Inc., pp. 305-308, 9th International Conference on Computer and Knowledge Engineering, Mashhad, Iran, Islamic Republic of, 24/10/19. https://doi.org/10.1109/ICCKE48569.2019.8964794

Breast Tumor Segmentation Using K-Means Clustering and Cuckoo Search Optimization. / Arjmand, Amir; Meshgini, Saeed; Afrouzian, Reza et al.

2019 9th International Conference on Computer and Knowledge Engineering, ICCKE 2019. Institute of Electrical and Electronics Engineers Inc., 2020. p. 305-308 8964794 (International Conference on Computer and Knowledge Engineering, ICCKE ; Vol. 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Breast Tumor Segmentation Using K-Means Clustering and Cuckoo Search Optimization

AU - Arjmand, Amir

AU - Meshgini, Saeed

AU - Afrouzian, Reza

AU - Farzamnia, Ali

N1 - Conference code: 9

PY - 2020/1/23

Y1 - 2020/1/23

N2 - Today, there are various methods for detecting tumors in breasts. But researchers are still trying to find an exact automatic way to segment the tumors from breast images. In this paper we propose a clustering-based algorithm for automatic tumor segmentation in the MRI samples. In the proposed method, we use k-means clustering algorithm for segmentation and also we use cuckoo search optimization (CSO) algorithm to initialize centroids in the k-means algorithm. We have used RIDER breast dataset to evaluate the proposed method and results clearly show that our algorithm outperforms similar methods such as simple k-means clustering algorithm and Fuzzy C-Means (FCM).

AB - Today, there are various methods for detecting tumors in breasts. But researchers are still trying to find an exact automatic way to segment the tumors from breast images. In this paper we propose a clustering-based algorithm for automatic tumor segmentation in the MRI samples. In the proposed method, we use k-means clustering algorithm for segmentation and also we use cuckoo search optimization (CSO) algorithm to initialize centroids in the k-means algorithm. We have used RIDER breast dataset to evaluate the proposed method and results clearly show that our algorithm outperforms similar methods such as simple k-means clustering algorithm and Fuzzy C-Means (FCM).

KW - Clustering

KW - Cuckoo Search Optimization (CSO)

KW - K-means algorithm

KW - Magnetic Resonance Imaging (MRI)

KW - Tumor Segmentation

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U2 - 10.1109/ICCKE48569.2019.8964794

DO - 10.1109/ICCKE48569.2019.8964794

M3 - Conference contribution

AN - SCOPUS:85079242299

SN - 9781728150765

T3 - International Conference on Computer and Knowledge Engineering, ICCKE

SP - 305

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BT - 2019 9th International Conference on Computer and Knowledge Engineering, ICCKE 2019

PB - Institute of Electrical and Electronics Engineers Inc.

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Arjmand A, Meshgini S, Afrouzian R, Farzamnia A. Breast Tumor Segmentation Using K-Means Clustering and Cuckoo Search Optimization. In 2019 9th International Conference on Computer and Knowledge Engineering, ICCKE 2019. Institute of Electrical and Electronics Engineers Inc. 2020. p. 305-308. 8964794. (International Conference on Computer and Knowledge Engineering, ICCKE ). doi: 10.1109/ICCKE48569.2019.8964794

Breast Tumor Segmentation Using K-Means Clustering and Cuckoo Search Optimization

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