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Least Centre Distance Based MAXNET Architecture to Obtain Threshold for Brain Tumor Edema Segmentation From FLAIR MRI
Sarkar K., Mandal R.K., Mandal A. and Sarkar S.

Published in: International Journal of Computer Sciences and Engineering
Volume- 5, Issue-2, pp.112-120, Feb 2017
DPI :-> 16.10022.IJCSE.2017.V5I2.112120.1259



Abstract
In recent years, Brain Tumor has become one of the most common deadly diseases and MRI is commonly used to diagnose it. Automated recognition of brain tumors from MRI is a difficult task because of the variability of size, shape, and contrast of the tumor. On the other hand, it has a huge impact in helping the physicians by assessing the type, size, exact topological location and other related parameters of the tumor. Image segmentation techniques are often applied in identifying the tumor from the MRI images in addition to other techniques. There are numerous segmentation techniques available for this purpose such as: (i) Region based (ii) Edge based (iii) Threshold based. Here a threshold based approach has been designed and proposed to do the segmentation of edema, where the threshold is determined by MAXNET, a Self Organization Map (SOM) based artificial neural network.

Key-Words / Index Term
Artificial Neural Network (ANN), Brain Tumor, Least centre distance method, Magnetic resonance imaging, MAXNET, segmentation, Self Organizing Map (SOM)

How to cite this article
Sarkar K., Mandal R.K., Mandal A. and Sarkar S. , “Least Centre Distance Based MAXNET Architecture to Obtain Threshold for Brain Tumor Edema Segmentation From FLAIR MRI ”, International Journal of Computer Sciences and Engineering, 5, Issue-2, pp.112-120, Feb 2017. DPI:16.10022.IJCSE.V5.I2.1259