Abstract
Brain tumor categorization and segmentation play a crucial in the existing healthcare domain. In this research study, an effective technique for brain magnetic resonance imaging (MRI) image segmentation and classification is demonstrated in order to improve accurate brain tumor segmentation. The anticipated brain tumor segmentation technique includes five phases, namely preprocessing, filtering, feature extraction, and classification along with segmentation. In the preprocessing stage, from the MRI database, the input MRI image is primarily fetched, and additionally, it is exposed to the skull stripping in order to remove the unwanted zone from the image. Skull stripped image is refined by utilizing a filter recognized as the adaptive median filter. However, the constituents of discrete wavelet transform (DWT), probability, skewness, and kurtosis are detached from the filtered images. Cuckoo search optimized feedforward neural network (FFNN) classifier categorizes the brain images like normal and also abnormal concerning the extracted features. Finally, the gray matter (GM), white matter (WM) along with cerebrospinal fluid (CSF) are apportioned as of the normal images through k-means clustering, and later, the cancer is segmented by utilizing EM algorithm, and the edema region is apportioned by utilizing watershed algorithm from the anomalous images, respectively. Henceforth, the outcomes will be investigated for exhibiting the accomplishment of the recommended classification and also segmentation techniques.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Vilas, H.R., Kulkarni, S.N., Chiranth, H., Bhille., M.: Segmentation and compression of 2D brain MRI images for efficient teleradiological applications. In: Electrical, Electronics, and Optimization Techniques (ICEEOT), pp. 1426–1431 (2016)
Sharma, K., Kaur, A., Gujral, S.: Brain tumor detection based on machine learning algorithms. Int. J. Comput. Appl. 103(1) (2014)
Goswami, S., Bhaiya, L.K.P.: Brain tumour detection using unsupervised learning based neural network. In: Communication Systems and Network Technologies (CSNT), pp. 573–577 (2013)
Murthy, Deepthi, T.S., Sadashivappa, G.: Brain tumor segmentation using thresholding, morphological operations and extraction of features of tumor. In: Advances in Electronics, Computers and Communications (ICAECC), pp. 1–6 (2014)
Gupta, S., Kumar, P.: Automated region growing approach for brain tumor segmentation in 3D MR images. Pattern Recogn. 43(10), 3548–3559 (2010)
Sachin, N., Shah, D., Khairnar, V., Kadu, S.: Brain tumor detection based on bilateral symmetry information. J. Eng. Res. Appl. 4(6), 98–103 (2014)
Bauer, S., Porz, N., Meier, R., Pica, A., Slotboom, J., Wiest, R., Reyes, M.: Interactive segmentation of MR images from brain tumor patients. In: Biomedical Imaging (ISBI), pp. 862–865. IEEE (2014)
Ilunga-Mbuyamba, E., Avina-Cervantes, J.G., Garcia-Perez, A., deJesus Romero-Troncoso, R., Aguirre-Ramos, H., Cruz-Aceves, I., Chalopin, C.: Localized active contour model with background intensity compensation applied on automatic MR brain tumor segmentation. Neurocomputing 220, 84–97 (2017)
Nabizadeh, N., John, N., Wright, C.: Histogram-based gravitational optimization algorithm on single MR modality for automatic brain lesion detection and segmentation. Expert Syst. Appl. 41(17), 7820–7836 (2014)
Njeh, I., Sallemi, L., Ayed, I.B., Chtourou, K., Lehericy, S., Galanaud, D., Hamida, A.B.: 3D multimodal MRI brain glioma tumor and edema segmentation: a graph cut distribution matching approach Comput. Med. Imaging Graph. 40, 108–119 (2015)
Kanas, V.G., Zacharaki, E.I., Davatzikos, C., Sgarbas, K.N., Megalooikonomou, V.: A low-cost approach for brain tumor segmentation based on intensity modeling and 3D random walker. Biomed. Signal Process. Control 22, 19–30 (2015)
Cabria, I., Gondra, I.: MRI segmentation fusion for brain tumor detection. Inf. Fusion 36, 1–9 (2017)
Chander, P.S., Soundarya, J., Priyadharsini, R.: Brain tumor detection and classification using K-means clustering and SVM classifier. In: RITA, pp. 49–63. Springer, Singapore (2020)
Murali, E., Meena, K.: A hybrid approach to the classification of brain tumors from MRI images using fast bounding box algorithm. Int. J. Simul. Syst. Sci.Technol. 19(4) (2018)
Ali, H.A.M., Ahmed, M.A.A., Hussein, E.M.: MRI brain tumour segmentation based on multimodal clustering and level-set method. In: International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE), pp. 1–5. IEEE (2018)
Karuppathal, R., Palanisamy, V.: Fuzzy based automatic detection and classification approach for MRI-brain tumor. ARPN J. Eng. Appl. Sci. 9(12) (2014)
Mohsen, H., El-Dahshan, E.-S., El-Horbaty, E.-S., Salem, A.-B.: Classification using deep learning neural networks for brain tumors Future Comput. Inform. J. 3(1), 68–71 (2018)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Kalam, R., Rahiman, M.A. (2022). Efficient Segmentation of Tumor and Edema MR Images Using Optimized FFNN Algorithm. In: Smys, S., Tavares, J.M.R.S., Balas, V.E. (eds) Computational Vision and Bio-Inspired Computing. Advances in Intelligent Systems and Computing, vol 1420. Springer, Singapore. https://doi.org/10.1007/978-981-16-9573-5_56
Download citation
DOI: https://doi.org/10.1007/978-981-16-9573-5_56
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-9572-8
Online ISBN: 978-981-16-9573-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)