tumor cancer image Segmentation and detection

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.