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 proceedingConference contributionpeer-review

18 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 languageEnglish
Title of host publication2019 9th International Conference on Computer and Knowledge Engineering, ICCKE 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages305-308
Number of pages4
ISBN (Electronic)9781728150758
ISBN (Print)9781728150765
DOIs
Publication statusPublished - 23 Jan 2020
Externally publishedYes
Event9th International Conference on Computer and Knowledge Engineering - Mashhad, Iran, Islamic Republic of
Duration: 24 Oct 201925 Oct 2019
Conference number: 9

Publication series

NameInternational Conference on Computer and Knowledge Engineering, ICCKE
PublisherIEEE
Volume2020
ISSN (Print)2375-1304
ISSN (Electronic)2643-279X

Conference

Conference9th International Conference on Computer and Knowledge Engineering
Abbreviated titleICCKE 2019
Country/TerritoryIran, Islamic Republic of
CityMashhad
Period24/10/1925/10/19

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