An Automated Brain Image Analysis System for Brain Cancer using Shearlets  

作　　者：R.Muthaiyan Dr M.Malleswaran 

机构地区：[1]Department of Electronics and Communication Engineering,University College of Engineering Thirukkuvalai,Tamilnadu,610204,India [2]Department of Electronics and Communication Engineering,University College of Engineering Kancheepuram,Kancheepuram,631552,India

出　　处：《Computer Systems Science & Engineering》2022年第1期299-312,共14页计算机系统科学与工程（英文）

摘　　要：In this paper,an Automated Brain Image Analysis(ABIA)system that classifies the Magnetic Resonance Imaging(MRI)of human brain is presented.The classification of MRI images into normal or low grade or high grade plays a vital role for the early diagnosis.The Non-Subsampled Shearlet Transform(NSST)that captures more visual information than conventional wavelet transforms is employed for feature extraction.As the feature space of NSST is very high,a statistical t-test is applied to select the dominant directional sub-bands at each level of NSST decomposition based on sub-band energies.A combination of features that includes Gray Level Co-occurrence Matrix(GLCM)based features,Histograms of Positive Shearlet Coefficients(HPSC),and Histograms of Negative Shearlet Coefficients(HNSC)are estimated.The combined feature set is utilized in the classification phase where a hybrid approach is designed with three classifiers;k-Nearest Neighbor(kNN),Naive Bayes(NB)and Support Vector Machine(SVM)classifiers.The output of individual trained classifiers for a testing input is hybridized to take a final decision.The quantitative results of ABIA system on Repository of Molecular Brain Neoplasia Data(REMBRANDT)database show the overall improved performance in comparison with a single classifier model with accuracy of 99% for normal/abnormal classification and 98% for low and high risk classification.

关 键 词：Brain image analysis WAVELETS Shearlet multi-scale analysis hybrid classification 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]