Method of multi‐region tumour segmentation in brain MRI images using grid‐based segmentation and weighted bee swarm optimisation

Mano, Abhisha; Anand, S.

doi:10.1049/iet-ipr.2019.1234

Cited by 10 publications

(3 citation statements)

References 19 publications

(23 reference statements)

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“…A fully automated, fast, and accurate method to segment tumours in the brain MRI volume was presented which detects and extracts whole tumours from 3D MRI [38]. To increase the accuracy, sensitivity, specificity, and precision, combinational methods were considered like fractional Jaya whale optimizer deep convolutional neural network with the DeepJoint segmentation method [39], and Grid‐based image segmentation‐weighted bee swarm optimization‐K‐means clustering [40]. Some researchers used a set of handcrafted features using a segmentation‐based localized region to train and test the SVM and multilayer perceptron classifiers, but the accuracy parameters showed some errors in the classification [41].…”

Section: Related Workmentioning

confidence: 99%

Brain tumour detection in magnetic resonance imaging using Levenberg–Marquardt backpropagation neural network

Ghahramani

Shiri

2022

IET Image Processing

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Magnetic resonance imaging (MRI) is a high-quality medical image that is used to detect brain tumours in a complex and time-consuming manner. In this study, a back propagation neural network (BPNN) along with the Levenberg-Marquardt algorithm (LMA) is proposed to classify MRIs and diagnose brain tumours in a simple and fast process. The BPNN has 10 neurons in the hidden layer, and the default function of the feedforward feeds is mean squared error (MSE). The LMA is optimized as a multivariable adaptive approach and considerably decreases the MSE of the BPNN, so the errors of the tumour classification are diminished. The proposed method follows four steps including preprocessing, skull removal, feature extraction, and classification. The input MRIs are converted to greyscale, resized, and thresholding is performed in the preprocessing step and followed by skull removal. Morphological operations of closing, opening, and dilation are used to segment abnormal areas in the MRIs, and the opening operator recognizes the tumour more accurately. Using statistical analysis and a grey-level co-occurrence matrix (GLCM) 12 features are extracted from the MRIs and used as the inputs of the BPNN. To evaluate the proposed method, 670 normal and 670 abnormal brain MRIs are used as input data, and the classification is performed in 0.494 s. The accuracy, sensitivity, specificity, precision, dice, recall, and MSE are 98.7%, 97.61%, 99.7%, 97.61%, 98.6%, 97.61%, and 0.005, respectively. The approach is accurate and fast for medical images classification.

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Section: Related Workmentioning

confidence: 99%

Brain tumour detection in magnetic resonance imaging using Levenberg–Marquardt backpropagation neural network

Ghahramani

Shiri

2022

IET Image Processing

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“…For example, segmentation of the hippocampus is a step involved in the automated diagnosis of Alzheimer's Disease (AD) from MRI [18]. Similarly, accurate segmentation of brain tumors is an important step in MRI-guided automated surgery and radiation treatment planning [19]. Contrast boosting is helpful to improve the efficiency of segmentation algorithms.…”

Section: Background and Problem Domainmentioning

confidence: 99%

A Post-Processing Algorithm for Boosting Contrast of MRI Images

Shiney¹,

Saleem²,

Rajinikanth³

et al. 2022

Computers, Materials &Amp; Continua

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Low contrast of Magnetic Resonance (MR) images limits the visibility of subtle structures and adversely affects the outcome of both subjective and automated diagnosis. State-of-the-art contrast boosting techniques intolerably alter inherent features of MR images. Drastic changes in brightness features, induced by post-processing are not appreciated in medical imaging as the grey level values have certain diagnostic meanings. To overcome these issues this paper proposes an algorithm that enhance the contrast of MR images while preserving the underlying features as well. This method termed as Power-law and Logarithmic Modification-based Histogram Equalization (PLMHE) partitions the histogram of the image into two sub histograms after a power-law transformation and a log compression. After a modification intended for improving the dispersion of the sub-histograms and subsequent normalization, cumulative histograms are computed. Enhanced grey level values are computed from the resultant cumulative histograms. The performance of the PLMHE algorithm is compared with traditional histogram equalization based algorithms and it has been observed from the results that PLMHE can boost the image contrast without causing dynamic range compression, a significant change in mean brightness, and contrast-overshoot.

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“…Other basic patterns are identified in the 3D image using the syntactic pattern approach, and then, finally, these patterns and frequency of these patterns are calculated for the classification purpose. Deep learning-based algorithms play an essential role in the medical field, and according to the literature survey, the AlexNet-based architecture detects cancer in the early stage more accurately [3,[18][19][20][21][22][23][24]. erefore, the 3D AlexNet architecture is used for the classification of tumor type.…”

Section: Introductionmentioning

confidence: 99%

Efficient 3D AlexNet Architecture for Object Recognition Using Syntactic Patterns from Medical Images

Rani

Ghai

Kumar

et al. 2022

Computational Intelligence and Neuroscience

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In computer vision and medical image processing, object recognition is the primary concern today. Humans require only a few milliseconds for object recognition and visual stimulation. This led to the development of a computer-specific pattern recognition method in this study for identifying objects in medical images such as brain tumors. Initially, an adaptive median filter is used to remove the noise from MRI images. Thereafter, the contrast image enhancement technique is used to improve the quality of the image. To evaluate the wireframe model, the cellular logic array processing (CLAP)-based algorithm is then applied to images. The basic patterns of three-dimensional (3D) images are then identified from the input image by scanning the whole image. The frequency of these patterns is also used for object classification. A deep neural network is then utilized for the classification of brain tumor. In the proposed model, the syntactic pattern recognition technique is used to find the feature vector and 3D AlexNet is used for brain tumor classification. To evaluate the performance of the proposed work, three benchmark brain tumor datasets are used, i.e., Figshare, Brain MRI Kaggle, and Medical MRI datasets and BraTS 2019 dataset. The comparative analyses reveal that the proposed brain tumor classification model achieves significantly better performance than the existing models.

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Method of multi‐region tumour segmentation in brain MRI images using grid‐based segmentation and weighted bee swarm optimisation

Cited by 10 publications

References 19 publications

Brain tumour detection in magnetic resonance imaging using Levenberg–Marquardt backpropagation neural network

Brain tumour detection in magnetic resonance imaging using Levenberg–Marquardt backpropagation neural network

A Post-Processing Algorithm for Boosting Contrast of MRI Images

Efficient 3D AlexNet Architecture for Object Recognition Using Syntactic Patterns from Medical Images

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