Detection of Brain Tumor Using Segmentation based Encryption for

MRI images

Guide: Dr.S.Nirmala Devi

T.Arumugasamy

Reg. No: 2010278001

06th Semester, ME - Medical Electronics (SS PT)

Objective

To detect the Brain tumor in MRI image. Tumor area consider as ROI and rest of the area consider as RONI.

To secure the transmitted data (Medical image) among the medical centers for telemedicine in the health care.

Literature Survey

Year Author Paper Title

2012 Ajala Funmilola A, Oke O.A, Et al

Fuzzy k-c-means Clustering Algorithm for Medical Image Segmentation

2012 M.C.Jobin Christ, Dr.R.M.S.Parvathi

Medical Image Segmentation Using Fuzzy C-means Clustering And Marker Controlled Watershed

Algorithm

2010 Vasant Manohar, Yuhua GuMRI Segmentation Using Fuzzy C-Means and Finite

Gaussian Mixture Model

2010 Kuo-Lung Wu Analysis of parameter selections for Fuzzy C-Means

1994Chaur-Heh Hsieh, Chung-Ming Kuo, Chung-Woei

Chao, Po-Chiang Lu

Image Segmentation Based On Fuzzy Clustering Algorithm

Proposed Idea

Introduction

Clustering is one of the most fundamental issues in pattern recognition.

It plays a key role in searching for structures in data.

Fuzzy C-Means Clustering

Fuzzy C-Means (FCM) is a method of clustering which allows one piece of data to belong to two or more clusters.

This method (developed by Dunn in 1973 and improved by Bezdek in 1981) is frequently used in pattern recognition.

Fuzzy C-Means Clustering

Fuzzy C-Means Clustering(FCM), is also known as Fuzzy

ISODATA.

The FCM employs fuzzy partitioning such that a data

point can belong to all groups with different membership

grades between 0 and 1.

FCM is an iterative algorithm. The aim of FCM is to find

cluster centers (centroids) that minimize a dissimilarity

function.

Fuzzy C-Means Clustering

Fuzzy C-Means Clustering

This algorithm is based upon iterative optimization of the objective function, with update of membership and cluster centers.

This is based upon initial membership matrix for each item in a cluster.

Center of clusters are calculated based upon the membership function.

Once the centers are determined the membership matrix is updated

When the difference between two sequential membership matrix is less than the initial termination criterion the algorithm is stopped. Otherwise step 2 and 3 are repeated.

Fuzzy C - Means : Flow chart

Input Image

No of Clusters

Initialize Partition Matrix

Find Membership matrix

Find the center Values

If Obj fun diff b/w two iteration is min End

No

Yes

Distance b/w data and centers

Find objective function

Update Membership

values

Image encryption & decryption

Encryption and decryption are both methods used to ensure the secure passing of messages and other sensitive documents and information.

Encryption basically means to convert the message into code or scrambled form, so that anybody who does not have the 'key' to unscramble the code, cannot view it.

A key is the actual 'described method' that was used to scramble the data, and hence the key can also unscramble the data.

Image decryption

When the data is unscrambled by the use of

a key, that is what is known as 'decryption'.

It is the opposite of encryption and the

'described method' of scrambling is basically

applied in reverse, so as to unscramble it.

Hence, the jumbled and unreadable text

becomes readable once again.

Segmented image

Encrypted image