Least Centre Distance Based MAXNET Architecture to Obtain Threshold for Brain Tumor Edema Segmentation From FLAIR MRI

Authors

  • Sarkar K Department of Computer Science, Ananda Chanda College (University of North Bengal), Jalpaiguri, India
  • Mandal RK Department of Computer Science and Application, University of North Bengal, West Bengal, India
  • A Mandal Department of Computer Science and Application, University of North Bengal, West Bengal, India
  • Sarkar S Department of Computer Science and Application, University of North Bengal, West Bengal, India

Keywords:

Artificial Neural Network (ANN), Brain Tumor, Least centre distance method, Magnetic resonance imaging, MAXNET, segmentation, Self Organizing Map (SOM)

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.

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Published

2025-11-11

How to Cite

[1]
K. Sarkar, R. Mandal, A. Mandal, and S. Sarkar, “Least Centre Distance Based MAXNET Architecture to Obtain Threshold for Brain Tumor Edema Segmentation From FLAIR MRI”, Int. J. Comp. Sci. Eng., vol. 5, no. 2, pp. 112–120, Nov. 2025.

Issue

Vol. 5 No. 2 (2017): IJCSE February Edition

Section

Research Article

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