Comparative study of clustering medical images

Fayez, Maria; Safwat, Soha; Hassanein, Ehab E.

doi:10.1109/sai.2016.7556000

Cited by 12 publications

(7 citation statements)

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“…As shown in the figure 8, the MRI of human brain image is used for testing the efficiency of algorithms to automatically detect a tumor. Clustering algorithms are compared [4] . Image segmentation of input images is done based on parameters such as Rand Index(RI), Variation of Information(VOI) and Global Consistency Error(GCE) is shown in Table 1.…”

Section: Resultsmentioning

confidence: 99%

Clustering Techniques for Medical Imaging

2019

IJITEE

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Nowadays medical imaging is becoming one of the popular techniques used to monitor human body to diagnose diseases, detect and treat injuries so that it can be treated. It helps in fetching desired information from the medical images. Clustering techniques in medical imaging is used to assist image based analysis of heterogeneous ailments by creating clusters of given population into homogeneous sub populations which helps in better understanding of the disease within each sub population. In this paper, we have discussed and compared various clustering techniques such as Fuzzy C Means clustering (FCM), Spatial Fuzzy C Means clustering(SFCM), K-Means and Particle Swarm Optimization Incorporative Fuzzy C Means clustering (PSOFCM), Gustafson Kessel (GK) clustering and Density Based Clustering of Applications with Noise (DBSCAN) to detect a tumor in human brain based on various image segmentation parameters. Accuracy of these algorithms is tested using MRI brain image.

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Section: Resultsmentioning

confidence: 99%

Clustering Techniques for Medical Imaging

2019

IJITEE

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“…. GLCM is one of the most widespread techniques of texture analysis that quantitatively measured the frequency of different combinations of pixel brightness values (gray levels) which occur in an image, and it has been used in a number of applications, e.g., [42][43][44][45][46][47][48]. In this step, texture features that contain information about the image are computed by GLCM to extract second-order statistic texture features (Table 1).…”

Section: Gray Level Cooccurrence Matrix (Glcm)mentioning

confidence: 99%

A Novel Intelligent System for Brain Tumor Diagnosis Based on a Composite Neutrosophic-Slantlet Transform Domain for Statistical Texture Feature Extraction

Wady

Yousif

Hasan

2020

BioMed Research International

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Discrete wavelet transform (DWT) is often implemented by an iterative filter bank; hence, a lake of optimization of a discrete time basis is observed with respect to time localization for a constant number of zero moments. This paper discusses and presents an improved form of DWT for feature extraction, called Slantlet transform (SLT) along with neutrosophy, a generalization of fuzzy logic, which is a relatively new logic. Thus, a novel composite NS-SLT model has been suggested as a source to derive statistical texture features that used to identify the malignancy of brain tumor. The MR images in the neutrosophic domain are defined using three membership sets, true (T), false (F), and indeterminate (I); then, SLT was applied to each membership set. Three statistical measurement-based methods are used to extract texture features from images of brain MRI. One-way ANOVA has been applied as a method of reducing the number of extracted features for the classifiers; then, the extracted features are subsequently provided to the four neural network classification techniques, Support Vector Machine Neural Network (SVM-NN), Decision Tree Neural Network (DT-NN), K-Nearest Neighbor Neural Network (KNN-NN), and Naive Bayes Neural Networks (NB-NN), to predict the type of the brain tumor. Meanwhile, the performance of the proposed model is assessed by calculating average accuracy, precision, sensitivity, specificity, and Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) curve. The experimental results demonstrate that the proposed approach is quite accurate and efficient for diagnosing brain tumors when the Gray Level Run Length Matrix (GLRLM) features derived from the composite NS-SLT technique is used.

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“…Image clustering also summarizes and displays the content of images. Moreover, it can be used for unsupervised clustering of an image set or database, image categorization, picture segmentation, content-based image retrieval, and image categorization [3]. In medical image datasets, unsupervised clustering is needed to organize and store many medical images quickly and easily.…”

Section: Introductionmentioning

confidence: 99%

“…In medical image datasets, unsupervised clustering is needed to organize and store many medical images quickly and easily. This makes it easier to classify medical images based on their content [3,4]. In addition, clustering is the most important part of data analysis and mining.…”

Section: Introductionmentioning

confidence: 99%

Clustering of Medical Images Using Multiwavelet Transform and K-Means Algorithms

Al-Jawher¹,

SHABAN²

2022

J.port.sci.res.

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Imaging data and biological records are both included in the field of medical informatics. Medical image clustering is an important area of research that is getting increased interest in both academia and the health professions. It addresses the issues of medical diagnosis, analysis, and education. Several medical imaging methods and applications based on data mining have been made and tested to deal with these problems. This paper looks at how image classification techniques diagnose diseases in the human body. It concerns the imaging modalities, dataset, and the pros and cons of each method. An optimal clustering technique of medical images using multiwavelet transforms proposed that combines the multiwavelet transform filterbanks with the k-means clustering algorithm to improve the performance and get a clinically meaningful clustering shape. In comparison with other methods of clustering, it was shown that this method has a much higher cluster classification than those published before. A user-friendly Matlab program has been constructed to test and get the results of the proposed algorithms

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Comparative study of clustering medical images

Cited by 12 publications

References 0 publications

Clustering Techniques for Medical Imaging

Clustering Techniques for Medical Imaging

A Novel Intelligent System for Brain Tumor Diagnosis Based on a Composite Neutrosophic-Slantlet Transform Domain for Statistical Texture Feature Extraction

Clustering of Medical Images Using Multiwavelet Transform and K-Means Algorithms

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