Brain tumour classification using two‐tier classifier with adaptive segmentation technique

Accurate medical images analysis plays a vital role for several clinical applications. Nevertheless, the immense and complex data volume to be processed make difficult the design of effective algorithms. The first aim of this paper is to examine this area of research and to provide some relevant reference sources related to the context of medical image analysis. Then, an effective hybrid solution to further improve the expected results is proposed here. It allows to consider the benefits of the cooperation of different complementary approaches such as statistical-based, variational-based and atlas-based techniques and to reduce their drawbacks. In particular, a pipeline framework that involves different steps such as a preprocessing step, a classification step and a refinement step with variational-based method is developed to identify accurately pathological regions in biomedical images. The preprocessing step has the role to remove noise and improve the quality of the images. Then the classification is based on both symmetry axis detection step and non linear learning with SVM algorithm. Finally, a level set-based model is performed to refine the boundary detection of the region of interest. In this work we will show that an accurate initialization step could enhance final performances. Some obtained results are exposed which are related to the challenging application of brain tumor segmentation.

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“…Anitha et al [67] 85.0 Bourouis et al [50] 78.5 Zikic et al [68] 71 Bauer et al [69] 62 Njeh et al [70] 89 Our framework 80.9…”

Section: Approach Similarity Index(%)mentioning

confidence: 89%

Toward Effective Medical Image Analysis Using Hybrid Approaches—Review, Challenges and Applications

et al. 2020

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“…V. Anitha, and S. Murugavalli [18] presented an Adaptive Pillar K-Means (APKM) scheme used for successful segmentation. Classification methodology was done by two-tier classification approach.…”

Section: Literature Surveymentioning

confidence: 99%

Genetic Algorithm Based Feature Selection Brain Tumour Segmentation and Classification

Kumar¹,

Krishna²,

Kusumavathi³

2019

IJIES

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Brain tumour segmentation is a significant task in the Medical Image Processing (MIP). Early diagnosis of the brain tumours plays an important role in improving treatment possibilities and maximizes the survival rate of the patients. Manual segmentation of the brain tumours for the cancer diagnosis form large amount of MRI images that generated in clinical routine, its complex and time consuming task. So, the automatic brain tumour image segmentation process is required. In this research, a tumour portion is segmented from the brain image by using Adaptively Regularized Kernel-Based Fuzzy C-Means (ARKFCM) algorithm. The input images are resized like 256×256 in the pre -processing stage. The pre-processed MRI image segmented by ARKFCM, which is a flexible high-level Machine Learning (ML) technique to locate the object in a complex template. Next, Hybrid Feature Extraction (HFE) performed on the segmented image to improve the feature subsets. The feature selection process was performed by Kernel Nearest Neighbour (KNN) based Genetic Algorithm (GA) in order to acquire the best feature values. The best feature values given to the Deep Neural Network (DNN) classifier as an input, which is classified into Meningioma, Glioma and Pituitary regions in the MRI images. The performance of proposed ARKFCM -HFE -DNN method is validated by T1-WCEMRI dataset. The experimental outcome showed that the ARKFCM -HFE -DNN method improved the average classification accuracy up to 6.19 % than existing classification techniques such as Convolution Neural Network (CNN) and Support Vector Machine (SVM).

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“…The authors extracted gray level co-occurrence matrix (GLCM) features from both trained and test brain image and all these features were classified using the classifier. Anitha et al 3 developed an algorithm for brain tumor detection using classification approach. The authors used K-means algorithm for segmenting the abnormal tissues in brain MRI image incorporating self-organizing map neural network.…”

Section: R E L a T E D W O R K Smentioning

confidence: 99%

An efficient and automatic glioblastoma brain tumor detection using shift‐invariant shearlet transform and neural networks

Arunachalam

Savarimuthu²

2017

Int J Imaging Syst Tech

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The detection and segmentation of tumor region in brain image is a critical task due to the similarity between abnormal and normal region. In this article, a computer‐aided automatic detection and segmentation of brain tumor is proposed. The proposed system consists of enhancement, transformation, feature extraction, and classification. The shift‐invariant shearlet transform (SIST) is used to enhance the brain image. Further, nonsubsampled contourlet transform (NSCT) is used as multiresolution transform which transforms the spatial domain enhanced image into multiresolution image. The texture features from grey level co‐occurrence matrix (GLCM), Gabor, and discrete wavelet transform (DWT) are extracted with the approximate subband of the NSCT transformed image. These extracted features are trained and classified into either normal or glioblastoma brain image using feed forward back propagation neural networks. Further, K‐means clustering algorithm is used to segment the tumor region in classified glioblastoma brain image. The proposed method achieves 89.7% of sensitivity, 99.9% of specificity, and 99.8% of accuracy.

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Brain tumour classification using two‐tier classifier with adaptive segmentation technique

Cited by 198 publications

References 24 publications

Toward Effective Medical Image Analysis Using Hybrid Approaches—Review, Challenges and Applications

Toward Effective Medical Image Analysis Using Hybrid Approaches—Review, Challenges and Applications

Genetic Algorithm Based Feature Selection Brain Tumour Segmentation and Classification

An efficient and automatic glioblastoma brain tumor detection using shift‐invariant shearlet transform and neural networks

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