Adaptive k-means clustering algorithm for MR breast image segmentation

Moftah, Hossam M.; Azar, Ahmad Taher; Al-Shammari, Eiman Tamah; Ghali, Neveen I.; Hassanien, Aboul Ella; Shoman, Mahmoud

doi:10.1007/s00521-013-1437-4

Cited by 124 publications

(51 citation statements)

References 28 publications

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“…When a new tool is available the problem should be reexamined to find better and more accurate solutions. In recent years, Soft Computing and Intelligent algorithms are gaining more importance and giving promising results in medical applications [16,17]. These issues motivate in applying intelligent and soft computing paradigms for analyzing and improving the performance of detection and classification of abnormal and normal tissues.…”

Section: Breast Cancer Signs In Digital Mammogramsmentioning

confidence: 99%

Computer Aided Intelligent Breast Cancer Detection: Second Opinion for Radiologists—A Prospective Study

Dheeba

Singh²

2014

Studies in Computational Intelligence

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Breast cancer is the common form of cancer and leading cause of mortality among woman, especially in developed countries. In western countries about 53-92 % of the population has this disease. As with any form of cancer, early detection and diagnosis of breast cancer can increase the survival rate. Mammography is the current diagnostic method for early detection of breast cancer. Breast parenchymal patterns are not stable between patients, between left and right breasts, and even within the same breast from year to year in the same patient. Breast cancer has a varied appearance on mammograms, from the obvious spiculated masses, to very subtle asymmetries noted on only one view, to faint calcifications seen only with full digital resolution or a magnifying glass. The large volume of cases requiring interpretation in many practices is also daunting, given the number of women in the population for whom yearly screening mammography is recommended. It seems obvious that this difficult task could likely be made less error prone with the help of computer algorithms. Computer-aided detection (CAD) systems have been shown to be capable of reducing false-negative rates in the detection of breast cancer by highlighting suspicious masses and microcalcifications on mammograms. These systems aid the radiologist as a 'second opinion' in detecting cancers and the final decision is taken by the radiologist. A supervised machine learning algorithm is investigated-Differential Evolution Optimized Wavelet Neural Network (DEOWNN) for detection of abnormalities in mammograms. Differential Evolution (DE) is a population based optimization algorithm based on the principle of natural evolution, which optimizes real parameters and real valued functions. By utilizing the DE algorithm, the parameters of the Wavelet Neural Network (WNN) are optimized. To increase the detection accuracy a feature extraction methodology is used to extract the texture based features of the abnormal

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Section: Breast Cancer Signs In Digital Mammogramsmentioning

confidence: 99%

Computer Aided Intelligent Breast Cancer Detection: Second Opinion for Radiologists—A Prospective Study

Dheeba

Singh²

2014

Studies in Computational Intelligence

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“…The purpose of Image Segmentation is to divide an image into semantically interpretable regions with regard to a particular application and to identify homogeneous regions within the image as discrete and belonging to distinct objects [1].…”

Section: Image Segmentationmentioning

confidence: 99%

“…Clustering in image processing is basically defined as the technique in which groups of identical image primitive are identified [1].Clustering is a method in which objects are unified into groups based on their characteristics. A cluster is basically an assembly of objects which are similar between them and are not similar to the objects fitting to additional clusters.…”

Section: Clustering Algorithmmentioning

confidence: 99%

“…The Fuzzy C-Means algorithm (normally abbreviated as FCM) is basically an iterative algorithm [1] that helps to find clusters in data and in which the idea of fuzzy membership is used. As an alternative of giving a pixel to a single cluster, each pixel can have distinctive membership values on each cluster [12].Fuzzy Cmeans(FCM) is a system of clustering which lets one piece of data to belong to two or extra clusters.…”

Section: Fuzzy C-means Clusteringmentioning

confidence: 99%

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A Review on Image Segmentation with its Clustering Techniques

Sharma¹,

Suji²

2013

IJSIP

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“…Toward this aim, several works have been proposed. For instance, Patel et al proposed a new method for breast image segmentation for early detection of breast cancer based on the detection of micro-calcification and a computer-based decision system using an adaptive k-means clustering algorithm [22]. A new different CAD scheme focusing on clinical applications was proposed by Doi Kunio et al [23], proposing a methodology to distinguish—in similar radiological images—the malignant lesions from benign lesions, thus achieving a diagnosis of breast cancer.…”

Section: Introductionmentioning

confidence: 99%

Multivariate Feature Selection of Image Descriptors Data for Breast Cancer with Computer-Assisted Diagnosis

et al. 2017

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Breast cancer is an important global health problem, and the most common type of cancer among women. Late diagnosis significantly decreases the survival rate of the patient; however, using mammography for early detection has been demonstrated to be a very important tool increasing the survival rate. The purpose of this paper is to obtain a multivariate model to classify benign and malignant tumor lesions using a computer-assisted diagnosis with a genetic algorithm in training and test datasets from mammography image features. A multivariate search was conducted to obtain predictive models with different approaches, in order to compare and validate results. The multivariate models were constructed using: Random Forest, Nearest centroid, and K-Nearest Neighbor (K-NN) strategies as cost function in a genetic algorithm applied to the features in the BCDR public databases. Results suggest that the two texture descriptor features obtained in the multivariate model have a similar or better prediction capability to classify the data outcome compared with the multivariate model composed of all the features, according to their fitness value. This model can help to reduce the workload of radiologists and present a second opinion in the classification of tumor lesions.

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Adaptive k-means clustering algorithm for MR breast image segmentation

Cited by 124 publications

References 28 publications

Computer Aided Intelligent Breast Cancer Detection: Second Opinion for Radiologists—A Prospective Study

Computer Aided Intelligent Breast Cancer Detection: Second Opinion for Radiologists—A Prospective Study

A Review on Image Segmentation with its Clustering Techniques

Multivariate Feature Selection of Image Descriptors Data for Breast Cancer with Computer-Assisted Diagnosis

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