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Classification of MRI brain images using combined wavelet entropy based spider web plots and probabilistic neural network |
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| Authors: | M. Saritha K. Paul Joseph Abraham T. Mathew |
| Affiliation: | 1. School of Mechanical and Electrical Engineering, Central South University, Changsha, China;2. Department of SEEM, City University of Hong Kong, Hong Kong, China;1. School of Software, Shanghai Jiao Tong University, Shanghai, China;2. Shanghai Key Laboratory of Scalable Computing and Systems, Department of Computer Science, Shanghai Jiao Tong University, Shanghai, China;3. Information Technology Department, Faculty of Computers and Information, Cairo University, Orman, Giza, Egypt;1. Artificial Intelligence Lab, Department of Computer Applications, Cochin University of Science and Technology, Kochi, India;2. Institute of Radiology and Imaging Sciences, Indira Gandhi Co-operative Hospital, Kochi, Kerala, India;1. National University of Computer & Emerging Sciences, Islamabad, Pakistan;2. College of Computer and Information Sciences, Al Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia;3. Signal and Image Processing Lab, Gwangju Institute of Science and Technology, Gwangju, South Korea;1. Kocaeli University, Mechatronics Engineering, 41380, Turkey;2. Siirt University, Computer Engineering, 56100, Turkey;3. Siirt University, Electrical and Electronics Engineering, 56100, Turkey |
| Abstract: | Magnetic resonance imaging (MRI) is a non-invasive diagnostic tool very frequently used for brain imaging. The classification of MRI images of normal and pathological brain conditions pose a challenge from technological and clinical point of view, since MR imaging focuses on soft tissue anatomy and generates a large information set and these can act as a mirror reflecting the conditions of the brain. A new approach by integrating wavelet entropy based spider web plots and probabilistic neural network is proposed for the classification of MRI brain images. The two step method for classification uses (1) wavelet entropy based spider web plots for the feature extraction and (2) probabilistic neural network for the classification. The spider web plot is a geometric construction drawn using the entropy of the wavelet approximation components and the areas calculated are used as feature set for classification. Probabilistic neural network provides a general solution to the pattern classification problems and the classification accuracy is found to be 100%. |
| Keywords: | Magnetic resonance imaging (MRI) Wavelet transformation Entropy Spider web plots Probabilistic neural network |
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