Among brain tumors, gliomas are the most common and aggressive, leading to a very short life expectancy in their highest grade. Thus, treatment planning is a key stage to improve the quality of life of oncological patients. Magnetic resonance imaging (MRI) is a widely used imaging technique to assess these tumors, but the large amount of data produced by MRI prevents manual segmentation in a reasonable time, limiting the use of precise quantitative measurements in the clinical practice. So, automatic and reliable segmentation methods are required; however, the large spatial and structural variability among brain tumors make automatic segmentation a challenging problem. Here, we propose an automatic segmentation method based on Convolutional Neural Networks (CNN), exploring small 3*3 kernels. The use of small kernels allows designing a deeper architecture, besides having a positive effect against over fitting, given the fewer number of weights in the network.
Dr. D Kishore Babu
Dr. D Kishore Babu is a graduate in Computer science and Information Technology from JNTUH Hyderabad and M.E in CSE from Sathyabama University, Chennai. He has received Ph.D Degree in Computer Science and Engineering from JNTU, Kakinada AP.He is working in the department of Computer Science and Engineering at Institute of Aeronautical Engineering.
Dr. K Suresh Babu
Number of Pages:
LAP LAMBERT Academic Publishing
Convolutional neural networks, magnetic resonance imaging, segmentation, Digital Image Processing
COMPUTERS / Networking / General