Lesion Detection from the Ultrasound Images Using K-Means Algorithm

Çıklaçandır, Fatma Günseli Yaşar; Ertaylan, Akin; BİNZAT, Uğur; Kut, Alp

doi:10.1109/tiptekno.2019.8895050

Cited by 7 publications

(9 citation statements)

References 4 publications

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“…This induces a division of the objects into groups from which the metric to be minimized can be calculated [65][66][67]. There are two known characteristics of the K-means clustering algorithm: one is that it requires a predefined…”

Section: K-means Clustering Algorithmmentioning

confidence: 99%

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Medical Images Breast Cancer Segmentation Based on K-Means Clustering Algorithm: A Review

Hassan

Abdulazeez

Zeebaree

et al. 2021

AJRCoS

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Early diagnosis is considered important for medical images of breast cancer, the rate of recovery and safety of affected women can be improved. It is also assisting doctors in their daily work by creating algorithms and software to analyze the medical images that can identify early signs of breast cancer. This review presents a comparison has been done in term of accuracy among many techniques used for detecting breast cancer in medical images. Furthermore, this work describes the imaging process, and analyze the advantages and disadvantages of the used techniques for mammography and ultrasound medical images. K-means clustering algorithm has been specifically used to analyze the medical image along with other techniques. The results of the K-means clustering algorithm are discussed and evaluated to show the capacity of this technique in the diagnosis of breast cancer and its reliability to identify a malignant from a benign tumor.

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Section: K-means Clustering Algorithmmentioning

confidence: 99%

“…Çiklaçandir et al, [83] Proposed a method to help the doctor diagnose a lesion in breast cancer. Ultrasonography is the imaging method used to detect breast cancer.…”

Section: Related Workmentioning

confidence: 99%

Medical Images Breast Cancer Segmentation Based on K-Means Clustering Algorithm: A Review

Hassan

Abdulazeez

Zeebaree

et al. 2021

AJRCoS

View full text Add to dashboard Cite

show abstract

“…They used Convolutional Neural Network (CNN) and Support Vector Machine (SVM) for tumor classification. Çiklaçandir et al used k-means for the early detection of breast cancer in [15]. The study proposed a system for identifying the lesion in the breast.…”

Section: Related Workmentioning

confidence: 99%

Early Diagnosis of Alzheimer’s Disease using Unsupervised Clustering

Farouk

Rady

2020

IJICIS

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Alzheimer's disease (AD) is a progressive brain disorder and a very common form of dementia. Neuroimaging techniques, such as Magnetic Resonance Imaging (MRI), produce detailed 3dimensional images of the brain showing insights for amyloid deposits and inflammatory alterations as disease markers. The early diagnosis of AD using MRI provides a good chance for patients to prevent further brain deterioration by stopping the loss of nerve cells. This paper explores the use of unsupervised clustering approaches for the early diagnosis of AD. Though it is very common to use classification techniques for identifying medical diseases, the lack or the inaccuracies of labeled data can generate a problem. In this work, the k-means and k-medoids are compared while employing the Voxel Based Morphometry (VBM) features extracted from the MRI images. The effect of choosing certain local regions of interest (ROIs) for the analysis is also compared to the global whole-brain analysis. The results show that the proposed approach can perform an early diagnosis of AD with an accuracy of 76%.

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“…Çiklaçandir et al [19] recommended K-Means grouping calculation to find the damage in the pictures. The outcomes of three unlike filters (Median, Laplace, Sobel) were researched in the study.…”

Section: ) K-meansmentioning

confidence: 99%

“…Thus, it is crucial to have computer systems that are able to assist junior radiologists in terms of image processing in the detection of breast cancer. CAD systems were developed to produce nonbiased diagnosis, reliable, and accurate diagnosis as a secondary judgment to form strong reasoning for doctors to single out benign breast tumors from malignant breast tumors [19,20]. The most critical and overwhelming stage in the processing of images is image segmentation.…”

Section: Introductionmentioning

confidence: 99%

A Review on Region of Interest Segmentation Based on Clustering Techniques for Breast Cancer Ultrasound Images

Muhammad

Zeebaree

Brifcani

et al. 2020

JASTT

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The most prevalent cancer amongst women is woman breast cancer. Ultrasound imaging is a widely employed method for identifying and diagnosing breast abnormalities. Computer-aided diagnosis technologies have lately been developed with ultrasound images to help radiologists enhance the accuracy of the diagnosis. This paper presents several ultrasound image segmentation techniques, mainly focus on eight clustering methods over the last 10 years, and it shows the advantages and disadvantages of these approaches. Breast ultrasound image segmentation is, therefore, still an accessible and challenging issue due to numerous ultrasound artifacts introduced in the imaging process, including high speckle noise, poor contrast, blurry edges, weak signal-to-noise ratio, and intensity inhomogeneity.

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Lesion Detection from the Ultrasound Images Using K-Means Algorithm

Cited by 7 publications

References 4 publications

Medical Images Breast Cancer Segmentation Based on K-Means Clustering Algorithm: A Review

Medical Images Breast Cancer Segmentation Based on K-Means Clustering Algorithm: A Review

Early Diagnosis of Alzheimer’s Disease using Unsupervised Clustering

A Review on Region of Interest Segmentation Based on Clustering Techniques for Breast Cancer Ultrasound Images

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