SLGP Header

Tumor Detection In Brain Using Morphological Image Processing

JASEM Front Page

In developing countries like India the causes of disease like cancer will degrade the economy of the country. Image processing is the field to detect these kind of unwanted cells and reviles the amount it spreads. In this paper the detection of tumor in brain, either malignant tumor or non- malignant tumor is done. The morphological image processing is to be used in order to locate and identify the size of tumor. The image from MRI scan will tell the presence of tumor in the brain, but we have to find the size of that tumor. The recent technology came for finding the size, shape, type and other important specification regarding the tumor like CT scan. This paper will show how the image from MRI scan is adjusted to suitable contrast and tumor is separated from the original image.
Keywords: malignant, tumor, MRI scan, CT scan, morphological image processing, separate
Image processing is the field were the information from images can be retrieved using suitable algorithm. In this paper the morphological image processing is used to detect the tumors from the brain either malignant or non-malignant tumors. The brain tumors some times change to malignant will leads to cancer. There are several techniques to capture image of brain like MRI, CT scan etc… A tumor is a mass of tissue that grows out of control of the normal forces that regulates growth. The multifaceted brain tumors can be split into two common categories depending on the tumors beginning, their enlargement prototype and malignancy. Primary brain tumors are tumors that take place commencing cells in the brain or commencing the wrapper of the brain. In this paper, the morphological operations like dilation, erosion etc… was done to remove the tumor from the MRI Image. Recent techniques achieved in researches for detection of brain tumor can be broadly classified as
1. Histogram based method.
2. Morphological operation is applied to MRI images of Brain.
3. Edge base segmentation and color base segmentation.
4. Cohesion self-merging based partition K-mean Algorithm. We are going to use only the morphological operation.


  1. Kimmi Verma1, Aru Mehrotra2, Vijayeta Pandey3, Shardendu Singh4 “Image Processing Techniques For The Enhancement Of Brain Tumor Patterns” International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering Vol. 2, Issue 4, April 2013.
  2. Roshan G. Selkar, Prof. M. N. Thakare, Prof. B. J. Chilke, “Segmentation and Detection of Brain Tumor using watershed and thresholding Algorithm” IJRAE in April 2014.
  3. Roshan G. Selkar, Prof. M. N. Thakare “Brain Tumor Detection And Segmentation By Using Thresholding And Watershed Algorithm” IJAICT Volume 1, Issue 3, July 2014 Doi:01.0401/ijaict.2014.03.08 Published Online 05 (08) 2014.
  4. M. Usman Akram', Anam Usman2 ‘Computer Aided System for Brain Tumor Detection and Segmentation’ 978-1-61284-941-6/11/$26.00 ©2011 IEEE.
  5. Gauri P. Anandgaonkar1, Ganesh.S.Sable2 “Detection and Identification of Brain Tumor in Brain MR Images Using Fuzzy C-Means Segmentation” International Journal of Advanced Research in Computer and Communication Engineering Vol. 2, Issue 10, October 2013.
  6. Aslı Kale, Selim Aksoy “Segmentation of Cervical Cell Images”.
  7. H. S. Wu, J. Barba, and J. Gil, “Iterative thresholding for segmentation of cells from noisy images,” Journal of Microscopy, vol. 197, no. 3, p. 296, 2000.
  8. Sura Ramzi Shareef “Breast Cancer Detection Based on Watershed Transformation” IJCSI International Journal of Computer Science Issues, Vol. 11, Issue 1, No 1, January 2014 ISSN (Print): 1694-0814 | ISSN (Online): 1694-0784.
  9. H. I. Ali, "Digital Images Edge Detection Using Mathematical Morphology Operations", Iraq Journal of Science, Vol. 51, No.1, 2010, PP. 177-184.
  10. E. Bengtsson, C. Wählby, and J. Lindblad “Robust Cell Image Segmentation Methods1” pattern recognition and image analysis Vol. 14 No. 2 2004.