K-means algorithm with level set for brain tumor segmentation

Abdelaziz, Samah; Lu, Songfeng

doi:10.11591/ijeecs.v15.i2.pp991-1000

Cited by 5 publications

(5 citation statements)

References 20 publications

(30 reference statements)

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“…These algorithms effectively classify images as normal or abnormal and further distinguish between benign and malignant tumors. Overall, the combination of techniques, including K-means clustering for segmentation, DWT for feature extraction, and PCA for dimensionality reduction, allows accurate analysis of brain tumor MRI scans [26]. This integrated approach aids in the identification, segmentation, and classification of brain tumors, facilitating more precise diagnosis and treatment planning for patients.…”

Section: Literature Surveymentioning

confidence: 99%

Distance Analysis and Dimensionality Reduction using PCA on Brain Tumour MRI Scans

Jhariya,

Parekh,

Lobo

et al. 2024

EAI Endorsed Trans Perv Health Tech

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INTRODUCTION: Compression of MRI images while maintaining essential information, makes it easier to distinguish between different types of brain tumors. It also assesses the effect of PCA on picture representation modification and distance analysis between tumor classes.OBJECTIVES: The objective of this work is to enhance the interpretability and classification accuracy of highdimensional MRI scans of patients with brain tumors by utilising Principle Component Analysis (PCA) to reduce their complexity.METHODS:This study uses PCA to compress high-dimensional MRI scans of patients with brain tumors, focusing on improving classification using dimensionality reduction approaches and making the scans easier to understand.RESULTS: PCA efficiently reduced MRI data, enabling better discrimination between different types of brain tumors and significant changes in distance matrices, which emphasize structural changes in the data.CONCLUSION: PCA is crucial for improving the interpretability of MRI data.

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Section: Literature Surveymentioning

confidence: 99%

Distance Analysis and Dimensionality Reduction using PCA on Brain Tumour MRI Scans

Jhariya,

Parekh,

Lobo

et al. 2024

EAI Endorsed Trans Perv Health Tech

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“…We then applied various clustering algorithms available in the scikit-learn (sklearn) library in Python [26]. We evaluated and compared three widely-used clustering algorithms: K-means [27,28], Gaussian mixture [29,30], and agglomerative clustering [31], utilizing different linkage methods (complete, ward, average, and single) [32]). These algorithms were selected for their proven effectiveness in handling diverse data types and their widespread use in similar studies.…”

Section: Data Scalingmentioning

confidence: 99%

Multi-Dimensional Validation of the Integration of Syntactic and Semantic Distance Measures for Clustering Fibromyalgia Patients in the Rheumatic Monitor Big Data Study

Goldstein,

Shahar,

Weisman Raymond

et al. 2024

Bioengineering

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This study primarily aimed at developing a novel multi-dimensional methodology to discover and validate the optimal number of clusters. The secondary objective was to deploy it for the task of clustering fibromyalgia patients. We present a comprehensive methodology that includes the use of several different clustering algorithms, quality assessment using several syntactic distance measures (the Silhouette Index (SI), Calinski–Harabasz index (CHI), and Davies–Bouldin index (DBI)), stability assessment using the adjusted Rand index (ARI), and the validation of the internal semantic consistency of each clustering option via the performance of multiple clustering iterations after the repeated bagging of the data to select multiple partial data sets. Then, we perform a statistical analysis of the (clinical) semantics of the most stable clustering options using the full data set. Finally, the results are validated through a supervised machine learning (ML) model that classifies the patients back into the discovered clusters and is interpreted by calculating the Shapley additive explanations (SHAP) values of the model. Thus, we refer to our methodology as the clustering, distance measures and iterative statistical and semantic validation (CDI-SSV) methodology. We applied our method to the analysis of a comprehensive data set acquired from 1370 fibromyalgia patients. The results demonstrate that the K-means was highly robust in the syntactic and the internal consistent semantics analysis phases and was therefore followed by a semantic assessment to determine the optimal number of clusters (k), which suggested k = 3 as a more clinically meaningful solution, representing three distinct severity levels. the random forest model validated the results by classification into the discovered clusters with high accuracy (AUC: 0.994; accuracy: 0.946). SHAP analysis emphasized the clinical relevance of "functional problems" in distinguishing the most severe condition. In conclusion, the CDI-SSV methodology offers significant potential for improving the classification of complex patients. Our findings suggest a classification system for different profiles of fibromyalgia patients, which has the potential to improve clinical care, by providing clinical markers for the evidence-based personalized diagnosis, management, and prognosis of fibromyalgia patients.

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“…The technique of registration of images can be divided into three types, the optimization of similarity measures, geometric transformation, and interpolation. The measure of similarity represents the key step in the recording of images [12]- [14]. The registration procedure is of immense importance.…”

Section: Introductionmentioning

confidence: 99%

The IoT and registration of MRI brain diagnosis based on genetic algorithm and convolutional neural network

Ahmed

Salah

2022

IJEECS

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The technology <span>of the multimodal brain image registration is the key method for accurate and rapid diagnosis and treatment of brain diseases. For achieving high-resolution image registration, a fast sub pixel registration algorithm is used based on single-step discrete wavelet transform (DWT) combined with phase convolution neural network (CNN) to classify the registration of brain tumors. In this work apply the genetic algorithm and CNN clasifcation in registration of magnetic resonance imaging (MRI) image. This approach follows eight steps, reading the source of MRI brain image and loading the reference image, enhencment all MRI images by bilateral filter, transforming DWT image by applying the DWT2, evaluating (fitness function) each MRI image by using entropy, applying the genetic algorithm, by selecting the two images based on rollout wheel and crossover of the two images, the CNN classify the result of subtraction to normal or abnormal, “in the eighth one,” the Arduino and global system for mobile (GSM) 8080 are applied to send the message to patient. The proposed model is tested on MRI Medical City Hospital in Baghdad database consist 550 normal and 350 abnormal and split to 80% training and 20 testing, the proposed model result achieves the 98.8% </span>accuracy.

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K-means algorithm with level set for brain tumor segmentation

Cited by 5 publications

References 20 publications

Distance Analysis and Dimensionality Reduction using PCA on Brain Tumour MRI Scans

Distance Analysis and Dimensionality Reduction using PCA on Brain Tumour MRI Scans

Multi-Dimensional Validation of the Integration of Syntactic and Semantic Distance Measures for Clustering Fibromyalgia Patients in the Rheumatic Monitor Big Data Study

The IoT and registration of MRI brain diagnosis based on genetic algorithm and convolutional neural network

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