Active Contour Model for Brain MR Tumor Segmentation and Volume Estimation
G. Anand Kumar1, P. V. Sridevi2
1G. Anand Kumar, Assistant Professor, Department of ECE, Gayatri Vidya Parishad College of Engineering(Autonomous), Visakhapatnam, Andhra Pradesh, India.
2Dr. P. V. Sridevi, Professor, Department of ECE, Andhra University College of Engineering (Autonomous), Visakhapatnam , Andhra Pradesh, India
Manuscript received on September 23, 2019. | Revised Manuscript received on October 15, 2019. | Manuscript published on October 30, 2019. | PP: 7226-7231 | Volume-9 Issue-1, October 2019 | Retrieval Number: E7484068519/2019©BEIESP | DOI: 10.35940/ijeat.E7484.109119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Brain MR tumor segmentation and estimation of volume is a critical task in medical applications. Brain tumors are analyzed by the common test method known as magnetic resonance imaging (MRI) which provides a detail image of brain. The proposed work involves detection of tumor in brain using deep learning based active contour model. Segmentation is the main objective of the proposed work for achieving detailed information about the tumor and accurate volume estimation to detect the size of the tumor. The Euclidean similarity factor (ESF) is used for considering the spatial distances and intensity differences of the region there by preserving all the fine details of the image. 3D convolutional neural network (3DCNN) is used for extracting the features and segmentation to identify the tumor location in the brain. Finally, shoelace method is used to estimate the volume of the tumor, and it provides treatment planning, surgical methods, estimation of dose, etc. The simulation results in this suggested approach could attain effective performance as compared with the existing approaches.
Keywords: Brain tumor, Magnetic resonance imaging, Euclidean similarity factor, Convolutional neural network.