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Analysis of dual tree M‐band wavelet transform based features for brain image classification
Author(s) -
Ayalapogu Ratna Raju,
Pabboju Suresh,
Ramisetty Rajeswara Rao
Publication year - 2018
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.27210
Subject(s) - support vector machine , artificial intelligence , complex wavelet transform , pattern recognition (psychology) , computer science , brain tumor , wavelet , classifier (uml) , cad , margin (machine learning) , wavelet transform , discrete wavelet transform , medicine , pathology , machine learning , engineering drawing , engineering
Purpose The most complex organ in the human body is the brain. The unrestrained growth of cells in the brain is called a brain tumor. The cause of a brain tumor is still unknown and the survival rate is lower than other types of cancers. Hence, early detection is very important for proper treatment. Methods In this study, an efficient computer‐aided diagnosis (CAD) system is presented for brain image classification by analyzing MRI of the brain. At first, the MRI brain images of normal and abnormal categories are modeled by using the statistical features of dual tree m‐band wavelet transform (DTMBWT). A maximum margin classifier, support vector machine (SVM) is then used for the classification and validated with k ‐fold approach. Results Results show that the system provides promising results on a repository of molecular brain neoplasia data (REMBRANDT) with 97.5% accuracy using 4 th level statistical features of DTMBWT. Conclusion Viewing the experimental results, we conclude that the system gives a satisfactory performance for the brain image classification.