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

Arjmand, Amir; Meshgini, Saeed; Afrouzian, Reza; Farzamnia, Ali

doi:10.1109/iccke48569.2019.8964794

Cited by 20 publications

(7 citation statements)

References 11 publications

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“…Such methods group a set of data objects of the whole image/volume into clusters by maximizing intraclass similarity and minimizing interclass similarity. The proposed approaches using K-means in [55,56] and Fuzzy C-means in [57,58] outperformed standard techniques and showed high accuracy in segmenting breast tumours. A semi-automatic algorithm using a marker-controlled watershed method proposed in [59] was proven more efficient in connecting disjoint areas in lesions compared to classical K-means clustering and Gaussian Mixture Model clustering [60].…”

Section: Tumour Segmentationmentioning

confidence: 97%

“…This task is very challenging as breast lesions widely vary in shape and intensity distribution. Strategies based on data clustering, particularly unsupervised clustering methods such as K-means and Fuzzy C-means, have been used for breast tumour segmentation using MRI exams [55][56][57][58]. Such methods group a set of data objects of the whole image/volume into clusters by maximizing intraclass similarity and minimizing interclass similarity.…”

Section: Tumour Segmentationmentioning

confidence: 99%

See 1 more Smart Citation

Development of 3D MRI-Based Anatomically Realistic Models of Breast Tissues and Tumours for Microwave Imaging Diagnosis

Pelicano

Gonçalves

Godinho

et al. 2021

Sensors

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Breast cancer diagnosis using radar-based medical MicroWave Imaging (MWI) has been studied in recent years. Realistic numerical and physical models of the breast are needed for simulation and experimental testing of MWI prototypes. We aim to provide the scientific community with an online repository of multiple accurate realistic breast tissue models derived from Magnetic Resonance Imaging (MRI), including benign and malignant tumours. Such models are suitable for 3D printing, leveraging experimental MWI testing. We propose a pre-processing pipeline, which includes image registration, bias field correction, data normalisation, background subtraction, and median filtering. We segmented the fat tissue with the region growing algorithm in fat-weighted Dixon images. Skin, fibroglandular tissue, and the chest wall boundary were segmented from water-weighted Dixon images. Then, we applied a 3D region growing and Hoshen-Kopelman algorithms for tumour segmentation. The developed semi-automatic segmentation procedure is suitable to segment tissues with a varying level of heterogeneity regarding voxel intensity. Two accurate breast models with benign and malignant tumours, with dielectric properties at 3, 6, and 9 GHz frequencies have been made available to the research community. These are suitable for microwave diagnosis, i.e., imaging and classification, and can be easily adapted to other imaging modalities.

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Section: Tumour Segmentationmentioning

confidence: 97%

Section: Tumour Segmentationmentioning

confidence: 99%

Development of 3D MRI-Based Anatomically Realistic Models of Breast Tissues and Tumours for Microwave Imaging Diagnosis

Pelicano

Gonçalves

Godinho

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Arjmand et al (9) proposed automatic method for segmenting tumor from breast image. In this method, clustering algorithm k-means hybridized with cukoo search optimization (CSO) algorithm have been used.…”

Section: Patel Et Al (mentioning

confidence: 99%

A Survey on Segmentation Techniques for Breast Cancer Detection

Utreja¹,

Sharma²,

Wason³

2022

ECS Trans.

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Breast cancer is second most rank disease after lung cancer between ladies around the world. Early detection declines the cancer death rate among women. Computer aided detection (CAD) system have been emerged which help the radiologists by specifying tumor region and reducing error mistake. Segmentation plays an important role in finding tumor area, i.e. Region of Interest (ROI). This paper investigates various segmentation techniques for breast cancer detection. Also, two segmentation techniques, Fuzzy C-Mean (FCM) and K-means, have been applied on mammogram images taken from MIAS database. Results shows that K-means is capable of estimating tumor region boundary as compared to FCM.

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“…Singh and Solanki [116] integrate K-means with a modified cuckoo search algorithm (K-means modified cuckoo search) to achieve a global optimum solution in a recommender system. Arjmand et al [117] proposed a hybrid clustering algorithm that combined the K-means clustering algorithm used for segmentation with cuckoo search optimization for generating the initial centroids for the K-means algorithm in breast tumor segmentation. García, Yepes, and Martí [118] proposed a K-means cuckoo search hybrid algorithm with the cuckoo search metaheuristics serving as the continuous space optimization mechanism and using the learning technique of the unsupervised K-means algorithm in the discretization of the obtained solution.…”

Section: Cuckoo Search Algorithmmentioning

confidence: 99%

K-Means-Based Nature-Inspired Metaheuristic Algorithms for Automatic Data Clustering Problems: Recent Advances and Future Directions

2021

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K-means clustering algorithm is a partitional clustering algorithm that has been used widely in many applications for traditional clustering due to its simplicity and low computational complexity. This clustering technique depends on the user specification of the number of clusters generated from the dataset, which affects the clustering results. Moreover, random initialization of cluster centers results in its local minimal convergence. Automatic clustering is a recent approach to clustering where the specification of cluster number is not required. In automatic clustering, natural clusters existing in datasets are identified without any background information of the data objects. Nature-inspired metaheuristic optimization algorithms have been deployed in recent times to overcome the challenges of the traditional clustering algorithm in handling automatic data clustering. Some nature-inspired metaheuristics algorithms have been hybridized with the traditional K-means algorithm to boost its performance and capability to handle automatic data clustering problems. This study aims to identify, retrieve, summarize, and analyze recently proposed studies related to the improvements of the K-means clustering algorithm with nature-inspired optimization techniques. A quest approach for article selection was adopted, which led to the identification and selection of 147 related studies from different reputable academic avenues and databases. More so, the analysis revealed that although the K-means algorithm has been well researched in the literature, its superiority over several well-established state-of-the-art clustering algorithms in terms of speed, accessibility, simplicity of use, and applicability to solve clustering problems with unlabeled and nonlinearly separable datasets has been clearly observed in the study. The current study also evaluated and discussed some of the well-known weaknesses of the K-means clustering algorithm, for which the existing improvement methods were conceptualized. It is noteworthy to mention that the current systematic review and analysis of existing literature on K-means enhancement approaches presents possible perspectives in the clustering analysis research domain and serves as a comprehensive source of information regarding the K-means algorithm and its variants for the research community.

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

Cited by 20 publications

References 11 publications

Development of 3D MRI-Based Anatomically Realistic Models of Breast Tissues and Tumours for Microwave Imaging Diagnosis

Development of 3D MRI-Based Anatomically Realistic Models of Breast Tissues and Tumours for Microwave Imaging Diagnosis

A Survey on Segmentation Techniques for Breast Cancer Detection

K-Means-Based Nature-Inspired Metaheuristic Algorithms for Automatic Data Clustering Problems: Recent Advances and Future Directions

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