Tumor detection in T1, T2, FLAIR and MPR brain images using a combination of optimization and fuzzy clustering improved by seed‐based region growing algorithm

Vishnuvarthanan, G.; Rajasekaran, M. Pallikonda; Vishnuvarthanan, N. Anitha; Prasath, T. Arun; Kannan, M.

doi:10.1002/ima.22208

Cited by 14 publications

(12 citation statements)

References 31 publications

(67 reference statements)

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“…In this study, three datasets including FLAIR, T1, and T2 are utilized for the method analysis 39 . These datasets are collected from the Siemens Medical System.…”

Section: Resultsmentioning

confidence: 99%

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Brain tumor diagnosis based on discrete wavelet transform, gray-level co-occurrence matrix, and optimal deep belief network

2020

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Brain tumors are a group of cancers that originate from different cells of the central nervous system or cancers of other tissues in the brain. Excessive cell growth in the brain is called a tumor. Tumor cells need food and blood to survive. Growth and proliferation of tumor cells in the cranial space, cause strain inside the brain and thus disrupt vital human structures. Therefore, diagnosis in the early stages of brain tumors is crucial. This study introduces a new optimized method for early diagnosis of the brain tumor. The method has five main parts of noise reduction, tumor segmentation, morphology, feature extraction based on wavelet and gray-level co-occurrence matrix, and classification based on an optimized deep belief network. For optimizing the classifier network, an enhanced version of the moth search algorithm is utilized. Simulation results are applied to three different datasets, FLAIR, T1, and T2, and the accuracy results of the presented method are compared with two other metaheuristics, particle swarm optimization and Bat algorithms. The final results showed that the presented technique has good achievements toward the compared methods.

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“…In this study, three datasets including FLAIR, T1, and T2 are utilized for the method analysis 39 . These datasets are collected from the Siemens Medical System.…”

Section: Resultsmentioning

confidence: 99%

“…By considering the previously mentioned cases, the update formulation for two updating models is as in Equations (39) and (40):…”

Section: Improved Moth Search Algorithm (Msa)mentioning

confidence: 99%

Brain tumor diagnosis based on discrete wavelet transform, gray-level co-occurrence matrix, and optimal deep belief network

2020

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“…All the databases have 68 images, so the total number of them is 204 brain MR tumor images. Images are collected by Siemens Medical Systems …”

Section: Simulation Resultsmentioning

confidence: 99%

“…Images are collected by Siemens Medical Systems. 56 Simulation results are implemented by MATLAB R2017b platform and based on a laptop with Intel®, Core TM i7, and 16 GB RAM.…”

Section: Simulation Resultsmentioning

confidence: 99%

Brain tumor diagnosis based on artificial neural network and a chaos whale optimization algorithm

Gong

Gao

Abza

2019

Computational Intelligence

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Accurate and early detection of the brain tumor region has a great impact on the choice of treatment, its success rate, and the follow‐up of the disease process over time. This study presents a new bioinspired technique for the early detection of the brain tumor area to improve the chance of completely healing. The study presents a multistep technique to detect the brain tumor area. Herein, after image preprocessing and image feature extraction, an artificial neural network is used to determine the tumor area in the image. The method is based on using an improved version of the whale optimization algorithm for optimal selection of the features and optimizing the artificial neural network weights for classification. Simulation results of the proposed method are applied to FLAIR, T1, and T2 datasets and are compared with different algorithms. Three performance indexes including correct detection rate, false acceptance rate, and false rejection rate are selected for the system performance analysis. Final results showed the superiority of the proposed method toward the other similar methods.

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“…Mean squared error (MSE) is used to measure the estimated volume with respect to the square of the difference between the input image I ( i , j ) and the segmented image O ( i , j ). The MSE value should range from 0 to 1. The MSE value is denoted using Equation .

italicMSE = \frac{1}{mn} \sum_{i = 1}^{m - 1} \sum_{j = 1}^{n - 1} {[I (i, j) - O (i, j)]}^{2}

…”

Section: The Contribution Of Our Articlementioning

confidence: 99%

Unsupervised learning‐based clustering approach for smart identification of pathologies and segmentation of tissues in brain magnetic resonance imaging

Vigneshwaran

Govindaraj

Murugan

et al. 2019

Int J Imaging Syst Tech

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Human‐made/developed algorithms provide automatic identification and segmentation of the tissues, lesions and tumor regions available in brain magnetic resonance scan images, which invocates predicaments such as high computational cost and low accuracy rate. Such hassles are reconciled with the utilization of an unsupervised approach in combination with clustering techniques. Initially, static features are chosen from the input image, which is fed to the self‐organizing map (SOM), where the algorithm employs the dimensionality reduction of input images. Consecutively, the reduced SOM prototype of data is clustered by the modified fuzzy K‐means (MFKM) algorithm. The MFKM algorithm can be modified in terms of membership variables because it operates with spatial information and converges quickly, and this would be of greater benefit to radiologists as they reduce the wrong predictions and voluminous time that normally occur owing to human involvement. The proposed algorithm provides 98.77% sensitivity and 97.5% specificity, which are better than any other traditional algorithms mentioned in this article.

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Tumor detection in T1, T2, FLAIR and MPR brain images using a combination of optimization and fuzzy clustering improved by seed‐based region growing algorithm

Cited by 14 publications

References 31 publications

Brain tumor diagnosis based on discrete wavelet transform, gray-level co-occurrence matrix, and optimal deep belief network

Brain tumor diagnosis based on discrete wavelet transform, gray-level co-occurrence matrix, and optimal deep belief network

Brain tumor diagnosis based on artificial neural network and a chaos whale optimization algorithm

Unsupervised learning‐based clustering approach for smart identification of pathologies and segmentation of tissues in brain magnetic resonance imaging

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