Brain disease diagnosis using local binary pattern and steerable pyramid

• Published in: International journal of multimedia information retrieval 2019-09, Vol.8 (3), p.155-165
• Main Authors: Kale, Vandana V., Hamde, Satish T., Holambe, Raghunath S.
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Back propagation; Back propagation networks; Brain; Brain cancer; Brain diseases; Brain research; Classification; Computer Science; Data Mining and Knowledge Discovery; Database Management; Datasets; Decomposition; Diagnosis; Disorders; Encephalitis; Entropy; Feature extraction; Image Processing and Computer Vision; Infectious diseases; Information Storage and Retrieval; Information Systems Applications (incl.Internet); Magnetic resonance imaging; Medical imaging; Metastasis; Methods; Multimedia Information Systems; Multiple sclerosis; Neural networks; Neuroimaging; Optimization; Regular Paper; Sarcoma; Signs and symptoms; Support vector machines; Wavelet transforms
• ISSN: 2192-6611; 2192-662X
• DOI: 10.1007/s13735-019-00174-x

Brain diseases can cause invisible disorders, cognitive and behavioral changes. Their symptoms vary widely. In some cases, treatment can improve the symptoms while in other cases injuries become permanent. Many disorders are progressive. Therefore, early and accurate diagnosis of disorder is essential for improving disorder condition and patient’s quality of life. This paper presents the brain disease diagnosis system in which two feature extraction methods are compared. One of the feature extraction methods uses local binary pattern and steerable pyramid (SP) to decompose magnetic resonance (MR) brain images into subbands which are termed as LBPSP subbands. Another feature extraction method uses SP solely to decompose MR images into SP subbands. Energies over LBPSP and SP subbands are calculated. The features are subjected to backpropagation neural network classifier. To prove the effectiveness of the proposed system, multi-class disease classification is carried out on four MR image datasets. Also, ‘one-vs-all’ binary classification is performed on one of the datasets. Energy features of LBPSP subbands achieve multi-class classification accuracies of 97.67%, 97.27%, 94.67% and 85.01% on datasets DS-200, DS-310, DS-255 and DS-612, respectively. The performance measures of ‘one-vs-all’ binary class classification prove the competency and efficiency of LBPSP subband features over the existing methods.The comparative results of two feature extraction methods indicate that the energy features of LBPSP subbands have more discriminating potential than energy features of SP subbands. Experimental results reveal that energy features of LBPSP subbands lead to the existing classification methods.