A hybrid method for MRI brain image classification
Highlights
► We proposed an MR brain image classifier whose accuracy is 100%. ► SCG is used to find the optimal weights of NN. ► The computation time per image is 0.0451s.
Introduction
Magnetic resonance imaging (MRI) is an imaging technique that produces high quality images of the anatomical structures of the human body, especially in the brain, and provides rich information for clinical diagnosis and biomedical research. The diagnostic values of MRI are greatly magnified by the automated and accurate classification of the MRI images. In recent years, researchers have proposed a lot of approaches for this goal, which fall into two categories.
One category is supervised classification, such as support vector machine (SVM) (Chaplot, Patnaik, & Jagannathan, 2006) and k-nearest neighbors (k-NN) (Fletcher-Heath, Hall, Goldgof, et al., 2001). The other category is unsupervised classification, such as self-organization feature map (SOFM) (Chaplot et al., 2006) and fuzzy c-means (Maitra & Chatterjee, 2008). While all these methods achieved good results, and the supervised classification performs better than unsupervised classification in terms of classification accuracy (success classification rate).
The goal of this study was to design a more efficient and accurate classifier to distinguish normal and abnormal brain MRIs. Moreover, we will present a detailed description of the classifier so that other researchers test and validate our method.
Our proposed method first employs wavelet transform to extract features from MRIs. Second, principle component analysis (PCA) technique was applied to reduce the dimensions of features. Third, the reduced features are sent to a back propagation neural network (BPNN), where scaled conjugate gradient (SCG) is adopted to find the optimal weights of the BPNN.
The structure of this paper is organized as follows. A short description of our method is presented in Section 2. Wavelet transform based methods for feature extraction is presented in Section 3. Principle component analysis technique for feature reduction is introduced in Section 4. Section 5 briefs the structure and training approach of the back propagation neural network. Experiments in Section 6 demonstrate the effectiveness and rapidness of our proposed algorithm. We conclude this paper in Section 7.
Section snippets
Methodology
Our method consists of three stages as shown in Fig. 1: feature extraction, feature reduction, and NN-based classification. This is a canonical and standard classification method which has already been proven as the best classification method (Ghosh, Shankar, & Meher, 2009).
Advantages of wavelet transform
The most conventional tool of signal analysis is Fourier transform (FT), which breaks down a time domain signal into constituent sinusoids of different frequencies, thus, transforming the signal from time domain to frequency domain. However, FT has a serious drawback. It lost the time information of the signal. For example, analyst can not tell when a particular event took place from a Fourier Spectrum. Thus, the classification will decrease as the time information is lost.
Gabor adapted the FT
Feature reduction
Excessive features increase computation times and storage memory. Furthermore, they sometimes make classification more complicated, which is called the curse of dimensionality. It is required to reduce the number of features.
Principal component analysis (PCA) is an efficient tool to reduce the dimension of a data set consisting of a large number of interrelated variables while retaining most of the variations. It is achieved by transforming the data set to a new set of ordered variables
Structure
Neural networks are widely used in pattern classification since they do not need any information about the probability distribution and the a priori probabilities of different classes. A single-hidden-layer backpropagation neural network is adopted with sigmoid neurons in the hidden layer and linear neuron in the output layer.
The training vectors were presented to the NN, which is trained in batch mode. The network configuration is NI × NH × 1, i.e., a two-layer network with NI input neurons, NH
Experiments and discussions
The experiments were carried out on the platform of P4 IBM with 3 GHz main frequency and 2G memory, running under Windows XP operating system. The algorithm was developed via the wavelet toolbox, the neural network toolbox, and the statistical toolbox of Matlab 2009b (The Mathworks ©). The programs can be run or tested on any computer platforms where Matlab is available.
Conclusions
In this paper we have developed a novel hybrid classifier to distinguish normal and abnormal brain MRIs. The method obtained 100% classification accuracy on both training and test images of the selected datasets, and the computation time for each image is only 0.0451 s.
Future work should focus on the following aspects. First, the proposed method should be employed for MRI images with other contrast mechanisms such as T1-weighted, proton-density-weighted, and diffusion-weighted MRIs. Second, the
Acknowledgments
The research was conducted with following supports: (1) National Natural Science Foundation of China (#60872075); (2) National Technical Innovation Project Essential Project Cultivate Project (#706928) and (3) Nature Science Fund in Jiangsu Province (#BK2007103). We gratefully appreciate to Dr. Zhenyu Zhou who reviewed the whole paper.
References (15)
- et al.
Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network
Biomedical Signal Processing and Control
(2006) Shift-invariance of short-time Fourier transform in fractional Fourier domains
Journal of the Franklin Institute
(2009)- et al.
Automatic segmentation of non-enhancing brain tumors in magnetic resonance images
Artificial Intelligence in Medicine
(2001) - et al.
A novel approach to neuro-fuzzy classification
Neural Networks
(2009) - et al.
Natural conjugate gradient training of multilayer perceptrons
Neurocomputing
(2008) - et al.
Medical ultrasound image compression using joint optimization of thresholding quantization and best-basis selection of wavelet packets
Digital Signal Processing
(2007) - et al.
Self-scaled conjugate gradient training algorithms
Neurocomputing
(2009)
Cited by (301)
Development of hybrid models based on deep learning and optimized machine learning algorithms for brain tumor Multi-Classification
2024, Expert Systems with ApplicationsDeep second generation wavelet autoencoders based on curvelet pooling for brain pathology classification
2023, Biomedical Signal Processing and ControlEffect of situational and instrumental distortions on the classification of brain MR images
2023, Biomedical Signal Processing and ControlMultimodal brain tumor detection using multimodal deep transfer learning[Formula presented]
2022, Applied Soft ComputingAn energy-efficient and secure framework for IoMT: An application of smart cities
2022, Sustainable Energy Technologies and Assessments