Image processing is an active research area in which medical image processing is a highly challenging field. Medical imaging techniques are used to image the inner portions of the human body for medical diagnosis. Brain tumor is a serious life altering disease condition. Image segmentation plays a significant role in image processing as it helps in the extraction of suspicious regions from the medical images. In this paper we have proposed segmentation of brain MRI image using K-means clustering algorithm followed by morphological filtering which avoids the misclustered regions that can inevitably be formed after segmentation of the brain MRI image for detection of tumor location.

1. Miss.Dharshika Shreeganesh
Reg No : 2012/SP/040
Index No : S 8288

2. Its me..! I am
Sick..!

3. Techniques
Performing
Biopsy
Performing
Imaging
X-Rays
Ultra
sounds
CT MRI

5. Image Segmentation
Techniques
Edge-based
Edge
Detection
Active
contours
Region-based
Merge/split
Graph cut
Pixel-based
Clustering
Fuzzy C-
means
K-means
Thresholding
Global
Adaptive

6. Original MRI spin-density brain images;
(a) gray-level image, (b) Color image

7. Start
Input Data Objects
select c1,…..,ck cluster centers
Calculate distance between each
pixel and each clustering center
is found
Distribute the data points x
among the k clusters
Update clustering
centers
Stop
Changed
Not changed
Flow Chart of the K-means Clustering

8. Color-based segmentation with K-means clustering process for spin-density
brain images ; (a)image labeled by cluster index ,(b)objects in cluster 1, (c)
objects in cluster 2 ,(d) final segmentation

9. Morphological
Operation
Erosion Dilation

10. Clustering of brain tumor MR images
Tumor detected

11. [1] R. P. Joseph, C. S. Singh, and M. Manikandan, “Brain Tumor
Mri Image Segmentation and Detection in Image Processing,” pp. 1–
5, 2014.
[2] M. Rakesh and T. Ravi, “Image Segmentation and Detection of
Tumor Objects in MR Brain Images Using FUZZY C-MEANS ( FCM )
Algorithm,” vol. 2, no. 3, pp. 2088–2094, 2012.
[3] H. P. S. P, G. K. Sundararaj, and A. Jayachandran, “Brain Tumor
Segmentation of Contras Material Applied MRI Using Enhanced Fuzzy
C-Means Clustering,” vol. 1, no. 2, pp. 161–166, 2012.
[4] B. Basavaprasad and M. Ravi, “A COMPARATIVE STUDY ON
CLASSIFICATION OF IMAGE SEGMENTATION METHODS WITH A FOCUS
ON GRAPH BASED TECHNIQUES,” pp. 310–315, 2014.
[5] K. I. Rahmani, “Clustering of Image Data Using K-Means and
Fuzzy,” vol. 5, no. 7, pp. 160–163, 2014.