BRAIN TUMOR MRI IMAGE SEGMENTATION AND DETECTION IN IMAGE PROCESSING

Miss.Dharshika ShreeganeshReg No : 2012/SP/040

Index No : S 8288

Brain Tumor MRI Image Segmentation And

Detection In Image ProcessingDigital Image Processing

CSC304MC3

Introduction

Its me..! I am Sick..!

Diagnostic methods

Techniques

Performing Biopsy

Performing Imaging

X-Rays Ultra sounds CT MRI

Magnetic Resonance Imaging

Image SegmentationImage 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

PROPOSED METHODOLOGY

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

K-Means ClusteringStart

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 changedFlow Chart of the K-means Clustering

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

Morphological Filtering

Morphological Operation

Erosion Dilation

Clustering of brain tumor MR images

Tumor detected

Reference[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.

Thank You…!