Call For Papers
Contact Us

  Brain Tumor Detection Using Standard Deviation and Area  
  Authors : Philipsy E; Dr. Radhakrishnan B
  Cite as:


Brain tumor is a perilous disease which causes brain damage. So detection and classification of brain tumor in early stage is necessary. Brain tumors can be basically categorized into normal, malignant or benign categories. There are two types of tumors- primary or secondary. Primary brain tumors originate in human brain and develop from growth of brain cells, membranes, nerve cells and glands. Secondary brain tumor originates in one part of the body and spreads into the brain or other part of the body. In the proposed work MRI brain images is converted into grayscale. Then the image is preprocessed using median filter for removing noises. Compute the standard deviation and by using canny edge detection, boundaries can be detected. Morphological operations such as dilation and erosion are applied for removing the unwanted disturbances after that tumor area is identified.


Published In : IJCSN Journal Volume 8, Issue 3

Date of Publication : June 2019

Pages : 239-242

Figures :02

Tables : --


Philipsy E : is continuing her M.tech on Computer Science and Engineering at APJ Abdul Kalam Technological University, Thiruvananthapuram.

Dr. Radhakrishnan B : is working as the Head of CSE department. He has more than 14 years experience in teaching and has published papers on data mining and image processing. His research interests include image processing, data mining, image mining.


MRI of Brain Scans, Brain Tumor, standard deviation, Binarization, brain abnormalities

The proposed algorithm shows an effective method for segmentation of the brain tumors from the 2D MRI images. Detect presence of brain tumor based on thresholding method. Experimental results on data sets show that the proposed method performed automatic detection of brain tumor from mri scans. It also find the area of the tumor portion, minimizing the computational procedure, minimize the manual interaction.


[1] Ming -ni wu1 chia- chin lin2 chin-cheng-chan3, "Brain Tumor Detection Using Color-Based K-Means Clustering Segmentation" December 2007,PP 1-3. [2] N. Dalal and B. Triggs, "Histograms of Oriented Gradients for Human Detection", In IEEE Computer Society Conference on Computer Vision and Pattern Recognition, volume 1, pages 886-893, June 2005. [3] P. Mohanaiah1, P. Sathyanarayana2, L. GuruKumar3, "Image Texture Feature Extraction using GLCM Approach" International Journal of Scientific and Research Publications, Volume 3, Issue 5, May 2013, PP 1-2. [4] Dr. S. Vijayarani 1 Mrs. M. Vinupriya2, "Performance Analysis of Canny and Sobel Edge Detection Algorithms in Image Mining", International Journal of Innovative Research in Computer and Communication Engineering, Vol. 1, Issue 8, October 2013. [5] An Algorithm for Detecting Brain Tumors in MRI Images Ehab F. Badran, EsraaGalal Mahmoud, and Nadder Hamdy Department of Electronics and Communications Engineering Arab Academy for Science and Technology & Maritime Transport Alexandria ,Egypt.