Detection of brain tumour in multi-modality images using hybrid features

Brain is recognized as a focal part of nervous system and it is inflated by tumour. Therefore the lifespan among humans gets diminished. The anatomy of brain can be reflected by Magnetic resonance image (MRI) or Computed tomography (CT) image. The accuracy segmentation of brain tumour detection in a multimodality images with inadequate computing resource is a challenging task in a medical field. To take over the provisioned difficulties we proposed a new novel innovative approach. The segmentation process comprises the subsequent footsteps. To wipe out the noise and smoothen image there is a need for a pre-processing. Cross guided bilateral filter (CGBF) technique had introduced for the eradication of noises in multimodality images. In this paper, hybrid dual tree complex wavelet transform with Walsh hadamard transform (Hybrid DTCWT-WHT) and Gabor filter is proposed in order to extract the indispensable hybrid set of features from the respective wavelet transforms. The hybrid DTCWT-WHT approach is used for an accurate identification brain tumour in Multi-Modality brain images. Features are important one for differentiating and deciding the exact class of brain tumour. The proposed hybrid features are used for predicting the presence of brain tumours and helps to segment the brain region correctly. Secondly in this framework, Adaptive mayfly Optimization (AMO) is proposed for the selection of crucial features from the feature vectors and destroys the non-required features. Then the classification purpose is emphasized to categorize tumour and un-tumour images. To efficiently strengthen the segmentation resolution, Fuzzy group teaching (FGT) algorithm is proposed. Proposed scheme is consolidated in Brain Tumour segmentation (BraTS) 2020 dataset to brand a nominal segmentation process. The substantial outcome had been appraised in terms of 98.18%. Accuracy, 95.50% precision, 97.14% Recall, 97.14% F1-Score, 98.66% Specificity, 95.52% Structural similarity index metric (SSIM), 95.12% Universal quality index (UQI), 0.94% Jaccard and 0.97% dice coefficients correspondingly.