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1. ABSTRACT
Multimodal medical image fusion, as a powerful tool for the clinical
applications, has developed with the advent of various imaging modalities in
medical imaging Two different fusion rules based on phase congruency and
directive contrast are proposed and used to fuse low- and high frequency
coefficients way to enable more accurate analysis of multimodality images.
a novel fusion framework is proposed for multimodal medical images based on
Finally, the fused image is constructed by the inverse DWT with all composite
coefficients. Experimental results and comparative study show that the
proposed fusion framework provides an effective

2. CONTENTS
 Aim
 Introduction
 Advantages and Applications of Image fusion
 Existing method
 Disadvantages
 Proposed method
 Applications
 System requirements
 Bibilography

3. INTRODUCTION
Medical images are very important now a days ,medical
images are provide the information of organs.
Medical images are two types
 CT & MRI
 CT & PET
 MRI & SPECT

4. INTRODUCTION
Medical Image is very important now days ,present we are many problems so
it can resolved by using methods
Image enhancement: Image enhancement is the process of adjusting digital images
so that the results are more suitable for display or further image analysis
Adaptive histogram equalization : As an alternative to using histgram you can
perform contrast limited adaptive histogram equalization
 Image segmentation: image segmentation is the process of dividing an image into
multiple parts Wavelet transform decomposes a signal into a set of basis functions
IMAGE FUSION: Image fusion is the process of combining multiple images into a
single image without distortion or loss of information

5. PROBLEM STATEMENT :
• Different enhancement techniques are used for general image
processing when captured , medical images with different
modalities may reflect very different categories of organ/tissue
information.
• Having different effects like Absorption of light, Reflection of
light, Scattering of light camera projection etc.
• Existing systems mainly use two methods
• wavelet segmentation based fusion

6. EXISTING SYSTEM
 HISTOGRAM EQUALIZATION METHOD:
This method usually increases the global contrast of
many images especially when the usable data of the
image is represented by close contrast values through
this adjustment the intensities can be better distributed
on the histogram. Histogram equalization accomplishes
this by effectively spreading out the most frequent
intensity values .

7. Histogram Equalization:
Image
Size Adjustment
RGB to Grey Image
conversion
Histogram
Equalization Technique
Processed output
image

8. INPUT IMAGE: it is used to capturing of the data
Syntax: i= imread(‘input.jpg’);
MEDIUM FILTER :We have seen that smoothing (low pass) filters
reduce noise. However, the underlying assumption is that the
neighbouring pixels represent additional samples of the same value
as the reference pixel, i.e. they represent the same feature. At edges,
this is clearly not true, and blurring of features results.
HISTOGRAM EQUALISATION: This method usually increases the global
contrast of many images especially when the usable data of the
image is represented by close contrast values through this
adjustment the intensities can be better distributed on the histogram.
Syntax: i= imhist(‘input.jpg’);

9. 1) Object clarity is low.
2) Time complexity is high.
3) Time varient property.
DRAWBACKS

10. Proposed system
Image fusion :-
Image fusion is the process of combining information from multiple images of
different modality, focus, view, sensors and time into a single image with
complementary information and without redundant information.
The fused image gives a better description than the source images and also it has
better quality in the aspects of contrast, edge, texture and information to provide
better quality

11. Block Diagram

12. Existing system
Flow chart
MRI /CT
Image
Normalization
DWT
FUSION
OUTPUT
IMAGE

13. INPUT IMAGE: it is used to capturing of the data
Syntax: i= imread (‘input.jpg’);
MEDIUM FILTER :We have seen that smoothing (low pass) filters reduce
noise. At edges, this is clearly not true, and blurring of features results.
DWT(DISCRETE WAVE TRANSFORM) :
In numerical analysis and functional analysis, a discrete wavelet
transform (DWT) is any wavelet transform for which the wavelets are discretely
sampled. As with other wavelet transforms, a key advantage it has over fourier
transforms is temporal resolution: it captures both frequency and location
information (location in time).

14. Discrete Wavelet Transform
• 2-D DWT for Image

15. Wavelet Transform
• Discrete wavelet transform (DWT), which transforms a discrete time
signal to a discrete wavelet representation.
• it converts an input series x0, x1, ..xm, into one high-pass wavelet
coefficient series and one low-pass wavelet coefficient series (of length
n/2 each) given by:
Output image: To Improve by using filter values.

16. SYSTEM REQUIREMENTS
Hardware Requirements:
Processor - Intel core i3
RAM - 2GB
Hard Disk - 20 GBs
Software Requirements:
 Tool - MATLAB R2016
Operating System - Windows 7,8

17. results

18. Applications:-
 Medical Image Processing
 Remote Sensing
 Computer vision
Advantages:-
 Geometric correction
 Replace the defective area

19. conclusion