International Journal Of Engineering Technology And Management Sciences

AUTHOR(S)

SAHIK FAREEDA, K PRASAD BABU

DOI: https://doi.org/10.46647/ijetms.2023.v07i01.021

Page No: 131 - 137

ABSTRACT
In this work, a fully automated system for brain region segmentation by using Human intelligence based deep learning technique is proposed. Deep learning technique is most popular state of the art method in recent applications. There are two stages involved the pre-processing and segmentation via Convolutional Neural Network (CNN).The MRI image with noise is used as an input image. MRI images are collected from publicly available database Open Access Series of Image Studies (OASIS). Three layers are used in this network, which is used to segment the brain region. The MR images are first given to pre-processing step to enhance the quality of image for segmentation. In this work, Non Local Mean Filter is used for image denoising which calculates weighted average of pixels and finding similarity with the target pixel. The denoised image is given as an input of CNN. Brain region segmentation by deep learning involves feature extraction. CNN learns features directly from an image and no handcrafted features are needed. The method consists of three steps such as input data generation, construction of model and learning the parameter. So, a compact representation from the image as image patches are given as input data to the multilayer convolutional neural network. The supervised deep network consists of three layers. Input image is given to the input layer, it predict the label from input layer. In every hidden layer one convolutional layer and one pooling layer is Present. Convolutional layer compute a dot product of the weights, input, and add a bias term. In this work 4 training images and 1 testing images in ages from the database are used. CNN is trained iteratively with representative input patterns along with target label. The execution of the CNN gives high exactness in the scope of 94% to 96%.

References:

[1] Venkatesh1, M.Judith Leo2, “MRI Brain Image Segmentation and Detection Using KNN Classification”, International Conference on Physics and Photonics Processes in Nano Sciences, 1362 (2019) 012073 IOP Publishing doi:10.1088/1742-6596/1362/1/012073.
[2] Bin Liua b, Xinhua Sang, ShujunXinga, Bo Wangaa., “Noise suppression in brain magnetic resonance imaging based on non-local means filter and fuzzy cluster”, Elsevier, pp.2955-2959, July 2015
[3] Sudipto Dolui, Alan Kuurstra, Iván C. Salgado Patarroyo, Oleg V. Michailovich., “A new similarity measure for non-local means filtering of MRI images “, Elsevier, pp.1040-1054, 2013.
[4] Jens Kleesiek, Gregor Urban, Alexander Hubert, Daniel Schwarz, Klaus Maier-Hein, Martin Bendszus, Armin Biller, “Deep MRI brain extraction: A 3D convolutional neural network for skull stripping”,Elsevier, Neuro image, pp.460-469, January 2016.
[5] Jonathan Long, Evan Shelhamer, Trevor Darrell, “Fully convolutional neural networks for semantic segmentation” in proc. IEEE Conf. Comput. Vis. Pattern Recongn. pp. 3431-3440, Jun, 2015.
[6] Mohammad Havaei, Axel Davy, David Warde-Farley, Antoine Biard, Aaron Courville, YoshuaBengio, Chris Pal, Pierre-Marc Jodoin, Hugo Larochelle, “Brain tumor segmentation with deep neural network”, Elsevier, Medical Image Analysis, pp. 18-31, 2017.
[7] Ali Isin, cemDirekonglu, MelikeSah, “Review of MRI-based brain tumor image segmentation using deep learning methods”, Elsevier, ICAFS, pp.317-324, August 2016.
[8] Urban G,,”Multi-modal brain tumor segmentation using deep convolutional neural network”, MICCAI Multimodal Brain Tumor segmentation challenge, pp.31-35, 2014.
[9] KonstantinosKamnitsas, Christian Ledig, Virgina F.J. Newcomb, Joanna P.Simpson, Andrew D. Kane, David K. Menon, Daniel Rueckert, Ben Glocker, “Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation”, Medical Image Analysis, pp. 61-78, October 2016.
[10] W. Zhang, R. Li, H. Deng, L. Wang, W. Lin, S. Ji, and D. Shen, “Deep convolutional neural networks for multi-modality is intense infant brain image segmentation,” Neuro Image, vol. 108, pp. 214–224, 2015.
[11] Olaf Ronneberger, P.Fischer, and T.Brox, “U-net: convolutional networks for biomedical image segmentation” in Proc. 18th Int. Conf. Med. Image Comput, pp.234-241, 2015.
[12] Sergio Pereira, Adriano Pinto, Victor Alves, Carlos A. Silva , “Brain tumor segmentation using convolutional neural network in MRI images”, IEEE Trans.Med. Imag, Vol. 35, No.5, May 2016.

How to Cite This Article:
SAHIK FAREEDA, K PRASAD BABU. HUMAN INTELLIGENCE BASED DEEP LEARNING TECHNIQUE FOR IMAGE SEGMENATION OF BRAIN MRI. ijetms;7(1):131-137. DOI: 10.46647/ijetms.2023.v07i01.021