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Article
Publication date: 26 July 2019

Ayalapogu Ratna Raju, Suresh Pabboju and Ramisetty Rajeswara Rao

Brain tumor segmentation and classification is the interesting area for differentiating the tumorous and the non-tumorous cells in the brain and classifies the tumorous…

Abstract

Purpose

Brain tumor segmentation and classification is the interesting area for differentiating the tumorous and the non-tumorous cells in the brain and classifies the tumorous cells for identifying its level. The methods developed so far lack the automatic classification, consuming considerable time for the classification. In this work, a novel brain tumor classification approach, namely, harmony cuckoo search-based deep belief network (HCS-DBN) has been proposed. Here, the images present in the database are segmented based on the newly developed hybrid active contour (HAC) segmentation model, which is the integration of the Bayesian fuzzy clustering (BFC) and the active contour model. The proposed HCS-DBN algorithm is trained with the features obtained from the segmented images. Finally, the classifier provides the information about the tumor class in each slice available in the database. Experimentation of the proposed HAC and the HCS-DBN algorithm is done using the MRI image available in the BRATS database, and results are observed. The simulation results prove that the proposed HAC and the HCS-DBN algorithm have an overall better performance with the values of 0.945, 0.9695 and 0.99348 for accuracy, sensitivity and specificity, respectively.

Design/methodology/approach

The proposed HAC segmentation approach integrates the properties of the AC model and BFC. Initially, the brain image with different modalities is subjected to segmentation with the BFC and AC models. Then, the Laplacian correction is applied to fuse the segmented outputs from each model. Finally, the proposed HAC segmentation provides the error-free segments of the brain tumor regions prevailing in the MRI image. The next step is to extract the useful features, based on scattering transform, wavelet transform and local Gabor binary pattern, from the segmented brain image. Finally, the extracted features from each segment are provided to the DBN for the training, and the HCS algorithm chooses the optimal weights for DBN training.

Findings

The experimentation of the proposed HAC with the HCS-DBN algorithm is analyzed with the standard BRATS database, and its performance is evaluated based on metrics such as accuracy, sensitivity and specificity. The simulation results of the proposed HAC with the HCS-DBN algorithm are compared against existing works such as k-NN, NN, multi-SVM and multi-SVNN. The results achieved by the proposed HAC with the HCS-DBN algorithm are eventually higher than the existing works with the values of 0.945, 0.9695 and 0.99348 for accuracy, sensitivity and specificity, respectively.

Originality/value

This work presents the brain tumor segmentation and the classification scheme by introducing the HAC-based segmentation model. The proposed HAC model combines the BFC and the active contour model through a fusion process, using the Laplacian correction probability for segmenting the slices in the database.

Details

Sensor Review, vol. 39 no. 4
Type: Research Article
ISSN: 0260-2288

Keywords

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Article
Publication date: 4 September 2019

Li Na, Xiong Zhiyong, Deng Tianqi and Ren Kai

The precise segmentation of brain tumors is the most important and crucial step in their diagnosis and treatment. Due to the presence of noise, uneven gray levels, blurred…

Abstract

Purpose

The precise segmentation of brain tumors is the most important and crucial step in their diagnosis and treatment. Due to the presence of noise, uneven gray levels, blurred boundaries and edema around the brain tumor region, the brain tumor image has indistinct features in the tumor region, which pose a problem for diagnostics. The paper aims to discuss these issues.

Design/methodology/approach

In this paper, the authors propose an original solution for segmentation using Tamura Texture and ensemble Support Vector Machine (SVM) structure. In the proposed technique, 124 features of each voxel are extracted, including Tamura texture features and grayscale features. Then, these features are ranked using the SVM-Recursive Feature Elimination method, which is also adopted to optimize the parameters of the Radial Basis Function kernel of SVMs. Finally, the bagging random sampling method is utilized to construct the ensemble SVM classifier based on a weighted voting mechanism to classify the types of voxel.

Findings

The experiments are conducted over a sample data set to be called BraTS2015. The experiments demonstrate that Tamura texture is very useful in the segmentation of brain tumors, especially the feature of line-likeness. The superior performance of the proposed ensemble SVM classifier is demonstrated by comparison with single SVM classifiers as well as other methods.

Originality/value

The authors propose an original solution for segmentation using Tamura Texture and ensemble SVM structure.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 12 no. 4
Type: Research Article
ISSN: 1756-378X

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Article
Publication date: 10 February 2021

Sathies Kumar Thangarajan and Arun Chokkalingam

The purpose of this paper is to develop an efficient brain tumor detection model using the beneficial concept of hybrid classification using magnetic resonance imaging…

Abstract

Purpose

The purpose of this paper is to develop an efficient brain tumor detection model using the beneficial concept of hybrid classification using magnetic resonance imaging (MRI) images Brain tumors are the most familiar and destructive disease, resulting to a very short life expectancy in their highest grade. The knowledge and the sudden progression in the area of brain imaging technologies have perpetually ready for an essential role in evaluating and concentrating the novel perceptions of brain anatomy and operations. The system of image processing has prevalent usage in the part of medical science for enhancing the early diagnosis and treatment phases.

Design/methodology/approach

The proposed detection model involves five main phases, namely, image pre-processing, tumor segmentation, feature extraction, third-level discrete wavelet transform (DWT) extraction and detection. Initially, the input MRI image is subjected to pre-processing using different steps called image scaling, entropy-based trilateral filtering and skull stripping. Image scaling is used to resize the image, entropy-based trilateral filtering extends to eradicate the noise from the digital image. Moreover, skull stripping is done by Otsu thresholding. Next to the pre-processing, tumor segmentation is performed by the fuzzy centroid-based region growing algorithm. Once the tumor is segmented from the input MRI image, feature extraction is done, which focuses on the first-order and higher-order statistical measures. In the detection side, a hybrid classifier with the merging of neural network (NN) and convolutional neural network (CNN) is adopted. Here, NN takes the first-order and higher-order statistical measures as input, whereas CNN takes the third level DWT image as input. As an improvement, the number of hidden neurons of both NN and CNN is optimized by a novel meta-heuristic algorithm called Crossover Operated Rooster-based Chicken Swarm Optimization (COR-CSO). The AND operation of outcomes obtained from both optimized NN and CNN categorizes the input image into two classes such as normal and abnormal. Finally, a valuable performance evaluation will prove that the performance of the proposed model is quite good over the entire existing model.

Findings

From the experimental results, the accuracy of the suggested COR-CSO-NN + CNN was seemed to be 18% superior to support vector machine, 11.3% superior to NN, 22.9% superior to deep belief network, 15.6% superior to CNN and 13.4% superior to NN + CNN, 11.3% superior to particle swarm optimization-NN + CNN, 9.2% superior to grey wolf optimization-NN + CNN, 5.3% superior to whale optimization algorithm-NN + CNN and 3.5% superior to CSO-NN + CNN. Finally, it was concluded that the suggested model is superior in detecting brain tumors effectively using MRI images.

Originality/value

This paper adopts the latest optimization algorithm called COR-CSO to detect brain tumors using NN and CNN. This is the first study that uses COR-CSO-based optimization for accurate brain tumor detection.

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Article
Publication date: 22 July 2020

Jiten Chaudhary, Rajneesh Rani and Aman Kamboj

Brain tumor is one of the most dangerous and life-threatening disease. In order to decide the type of tumor, devising a treatment plan and estimating the overall survival…

Abstract

Purpose

Brain tumor is one of the most dangerous and life-threatening disease. In order to decide the type of tumor, devising a treatment plan and estimating the overall survival time of the patient, accurate segmentation of tumor region from images is extremely important. The process of manual segmentation is very time-consuming and prone to errors; therefore, this paper aims to provide a deep learning based method, that automatically segment the tumor region from MR images.

Design/methodology/approach

In this paper, the authors propose a deep neural network for automatic brain tumor (Glioma) segmentation. Intensity normalization and data augmentation have been incorporated as pre-processing steps for the images. The proposed model is trained on multichannel magnetic resonance imaging (MRI) images. The model outputs high-resolution segmentations of brain tumor regions in the input images.

Findings

The proposed model is evaluated on benchmark BRATS 2013 dataset. To evaluate the performance, the authors have used Dice score, sensitivity and positive predictive value (PPV). The superior performance of the proposed model is validated by training very popular UNet model in the similar conditions. The results indicate that proposed model has obtained promising results and is effective for segmentation of Glioma regions in MRI at a clinical level.

Practical implications

The model can be used by doctors to identify the exact location of the tumorous region.

Originality/value

The proposed model is an improvement to the UNet model. The model has fewer layers and a smaller number of parameters in comparison to the UNet model. This helps the network to train over databases with fewer images and gives superior results. Moreover, the information of bottleneck feature learned by the network has been fused with skip connection path to enrich the feature map.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 13 no. 4
Type: Research Article
ISSN: 1756-378X

Keywords

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Article
Publication date: 1 June 2021

Na Li and Kai Ren

Automatic segmentation of brain tumor from medical images is a challenging task because of tumor's uneven and irregular shapes. In this paper, the authors propose an…

Abstract

Purpose

Automatic segmentation of brain tumor from medical images is a challenging task because of tumor's uneven and irregular shapes. In this paper, the authors propose an attention-based nested segmentation network, named DAU-Net. In total, two types of attention mechanisms are introduced to make the U-Net network focus on the key feature regions. The proposed network has a deep supervised encoder–decoder architecture and a redesigned dense skip connection. DAU-Net introduces an attention mechanism between convolutional blocks so that the features extracted at different levels can be merged with a task-related selection.

Design/methodology/approach

In the coding layer, the authors designed a channel attention module. It marks the importance of each feature graph in the segmentation task. In the decoding layer, the authors designed a spatial attention module. It marks the importance of different regional features. And by fusing features at different scales in the same coding layer, the network can fully extract the detailed information of the original image and learn more tumor boundary information.

Findings

To verify the effectiveness of the DAU-Net, experiments were carried out on the BRATS 2018 brain tumor magnetic resonance imaging (MRI) database. The segmentation results show that the proposed method has a high accuracy, with a Dice similarity coefficient (DSC) of 89% in the complete tumor, which is an improvement of 8.04 and 4.02%, compared with fully convolutional network (FCN) and U-Net, respectively.

Originality/value

The experimental results show that the proposed method has good performance in the segmentation of brain tumors. The proposed method has potential clinical applicability.

Details

International Journal of Intelligent Computing and Cybernetics, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1756-378X

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Article
Publication date: 5 June 2020

Hiren Mewada, Amit V. Patel, Jitendra Chaudhari, Keyur Mahant and Alpesh Vala

In clinical analysis, medical image segmentation is an important step to study the anatomical structure. This helps to diagnose and classify abnormality in the image. The…

Abstract

Purpose

In clinical analysis, medical image segmentation is an important step to study the anatomical structure. This helps to diagnose and classify abnormality in the image. The wide variations in the image modality and limitations in the acquisition process of instruments make this segmentation challenging. This paper aims to propose a semi-automatic model to tackle these challenges and to segment medical images.

Design/methodology/approach

The authors propose Legendre polynomial-based active contour to segment region of interest (ROI) from the noisy, low-resolution and inhomogeneous medical images using the soft computing and multi-resolution framework. In the first phase, initial segmentation (i.e. prior clustering) is obtained from low-resolution medical images using fuzzy C-mean (FCM) clustering and noise is suppressed using wavelet energy-based multi-resolution approach. In the second phase, resultant segmentation is obtained using the Legendre polynomial-based level set approach.

Findings

The proposed model is tested on different medical images such as x-ray images for brain tumor identification, magnetic resonance imaging (MRI), spine images, blood cells and blood vessels. The rigorous analysis of the model is carried out by calculating the improvement against noise, required processing time and accuracy of the segmentation. The comparative analysis concludes that the proposed model withstands the noise and succeeds to segment any type of medical modality achieving an average accuracy of 99.57%.

Originality/value

The proposed design is an improvement to the Legendre level set (L2S) model. The integration of FCM and wavelet transform in L2S makes model insensitive to noise and intensity inhomogeneity and hence it succeeds to segment ROI from a wide variety of medical images even for the images where L2S failed to segment them.

Details

Engineering Computations, vol. 37 no. 9
Type: Research Article
ISSN: 0264-4401

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Article
Publication date: 27 April 2020

Igor Georgievich Khanykov, Ivan Mikhajlovich Tolstoj and Dmitriy Konstantinovich Levonevskiy

The purpose of this paper is the image segmentation algorithms (ISA) classification analysis, providing for advanced research and design of new computer vision algorithms.

Abstract

Purpose

The purpose of this paper is the image segmentation algorithms (ISA) classification analysis, providing for advanced research and design of new computer vision algorithms.

Design/methodology/approach

For the development of the required algorithms a three-stage flowchart is suggested. An algorithm of quasi-optimal segmentation is discussed as a possible implementation of the suggested flowchart. A new attribute is introduced reflecting the specific hierarchical algorithm group, which the proposed algorithm belongs to. The introduced attribute refines the overall classification scheme and the requirements for the algorithms under development.

Findings

Optimal approximation generation is a computationally intensive task. The computational complexity can be reduced using a hierarchical data framework and a set of auxiliary algorithms, contributing to overall quality improvement. Because hierarchical solutions usually are distinctively suboptimal, further optimization to them was applied. A new classification attribute, proposed in this paper allows to discover previously hidden «blank spots», having decomposed the two-tier ISA classification scheme. The new classification attribute allows to aggregate algorithms, yielding multiple partitions at output and assign them to a dedicated group.

Originality/value

The originality of the paper consists in development of a high-level ISA classification, as well in introduction of a new classification attribute, pertinent to iterative algorithm groups and to hierarchically structured data presentation algorithms.

Details

International Journal of Intelligent Unmanned Systems, vol. 8 no. 2
Type: Research Article
ISSN: 2049-6427

Keywords

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Article
Publication date: 1 February 2021

K.M. Baalamurugan, Priyamvada Singh and Vijay Ramalingam

One of the foremost research disciplines in medical image processing is to identify tumors, which is a challenging task practicing traditional methods. To overcome this…

Abstract

Purpose

One of the foremost research disciplines in medical image processing is to identify tumors, which is a challenging task practicing traditional methods. To overcome this, various research studies have been done effectively.

Design/methodology/approach

Medical image processing is evolving swiftly with modern technologies being developed every day. The advanced technologies improve medical fields in diagnosing diseases at the more advanced stages and serve to provide proper treatment.

Findings

Either the mass growth or abnormal growth concerning the cells in the brain is called a brain tumor.

Originality/value

The brain tumor can be categorized into two significant varieties, non-cancerous and cancerous. The carcinogenic tumors or cancerous is termed as malignant and non-carcinogenic tumors are termed benign tumors. If the cells in the tumor are healthy then it is a benign tumor, whereas, the abnormal growth or the uncontrollable growth of the cell is indicated as malignant. To find the tumor the magnetic resonance imaging (MRI) is carried out which is a tiresome and monotonous task done by a radiologist. In-order to diagnosis the brain tumor at the initial stage effectively with improved accuracy, the computer-aided robotic research technology is incorporated. There are numerous segmentation procedures, which help in identifying tumor cells from MRI images. It is necessary to select a proper segmentation mechanism to detect brain tumors effectively that can be aided with robotic systems. This research paper focuses on self-organizing map (SOM) by applying the adaptive network-based fuzzy inference system (ANFIS). The execution measures are determined to employ the confusion matrix, accuracy, sensitivity, and furthermore, specificity. The results achieved conclusively explicate that the proposed model presents more reliable outcomes when compared to existing techniques.

Details

International Journal of Intelligent Unmanned Systems, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2049-6427

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Article
Publication date: 18 January 2011

Ali K. Kamrani and Maryam Azimi

Most of the current radiation treatment planning systems use pre‐treatment computed tomography (CT) images to detect the tumor location and then plan the radiation therapy…

Abstract

Purpose

Most of the current radiation treatment planning systems use pre‐treatment computed tomography (CT) images to detect the tumor location and then plan the radiation therapy to be delivered during the treatment period. It is assumed that the tumor geometry will not change throughout the treatment course; however, tumor geometry is shown to be changing over time. The purpose of this paper is to present results of an ongoing research in 3‐D modeling and reconstruction of head and neck cancer tumors. The results from this phase of the project will be used in developing a prediction model for tumor deformation during radiation treatment of cancer patients.

Design/methodology/approach

By using CT scan data in the 3‐D ASCII format, the tumor's progressive geometric changes during the treatment period are quantified. After constructing slice contours, both triangular and rectangular patch approaches are applied to map and analyze the tumor surface and volume. The changes in tumor location are calculated based on a reference feature on the top of the spine canal. MATLAB routines are developed to perform the required calculations. A set of prototype mockups of different stages are used for the purpose of validation and verification of the proposed methodology.

Findings

The proposed method is applied to calculate volume, surface, and displacement of the tumor, using patients’ data obtained from the University of Texas‐MD Anderson Cancer Center. The results are consistent with the actual data.

Originality/value

The proposed methodology increases the accuracy of treatment planning by predicting the changes in tumor geometry. The literature survey reveals that no work has been devoted to mathematically model the geometrical changes that a tumor might go through after each radiation.

Details

Rapid Prototyping Journal, vol. 17 no. 1
Type: Research Article
ISSN: 1355-2546

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Article
Publication date: 1 October 2000

D. Dutta Majumder and M. Bhattacharya

The cybernetic approach differs significantly from the conventional reductionist methods of natural and biological sciences. Norbert Wiener established the theory of…

Abstract

The cybernetic approach differs significantly from the conventional reductionist methods of natural and biological sciences. Norbert Wiener established the theory of cybernetics as a science of control and communication process in living beings (human and animals) and machines. Dutta Majumder in his Norbert Wiener Award winning paper extended the approach to include integrated complex human machine systems and functions with general systems theory as a unitary science laying the mathematical foundation for unifying observing systems, observed systems and the act of observing as indicated in von Foerster’s concept of second‐order cybernetics. Both from the point of view of ontology and that of epistemology the cybernetic approach now enables computer technology to incorporate artificial intelligence (AI) and expert system (ES) for knowledge based instrumentation for diagnostics and therapy planning. Presents the results of a project for development of a knowledge based framework for combining different modalities of medical image processing such as CT, MR(T1), MR(T2), SPECT, PET, USG etc. whichever is relevant for particular pathological investigation for diagnostics and therapeutic planning. Experiments were conducted with (a) Alzheimer’s patient data and (b) detection and grading of malignancy with oncological data for the cancer screening system.

Details

Kybernetes, vol. 29 no. 7/8
Type: Research Article
ISSN: 0368-492X

Keywords

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