This document presents a novel technique for medical image compression using multi-wavelet transformation with modified fast Haar wavelet transform (MFHWT) in the Set Partitioning in Hierarchical Trees (SPIHT) algorithm. The technique aims to minimize information loss and increase image quality during compression. It involves applying multi-wavelet decomposition using MFHWT on the image, then using SPIHT to find significant and insignificant pixels. A region of interest is selected before compression to avoid loss of important image details. The technique is evaluated on medical images and shows improved peak signal-to-noise ratio and compression ratio compared to standard SPIHT.

1. International Journal of Graphics and Multimedia (IJGM), ISSN 0976 – 6448(Print),
ISSN 0976 – 6456(Online) Volume 4, Issue 1, January - April 2013, © IAEME
1
A NOVEL TECHNIQUE IN SPIHT FOR MEDICAL IMAGE
COMPRESSION
Shipra Gupta1
, Chirag Sharma2
1
(Computer Science and Engg , Lovely Professional University, Hoshiarpur, India)
2
(Computer Science and Engg, Lovely Professional University, Jalandhar, India)
ABSTRACT
Medical science grows very fast and each hospital needs to store high volume of data
about patients and in this field the images produce by the modality is in the form of large file, in
order to get the opinion from other doctors images are send to other place using electronic
media. Compression of images needs to be apply as the size of image is very large to send, but
with compression there is loss of information in the image. In order to minimize the loss and to
increase the quality of image requires compression is to be done, multi wavelet transformation
technology plays a vital role. So, in this paper we consider that multi wavelet with Region of
Interest (ROI) on the selecting portion will not only give the quality but also reduce the loss of
information from image. And we are going to implement the multi wavelet transformation with
Modified Fast Haar Wavelet Transform (MFHWT) in Set Partitioning in Hierarchical Trees
algorithm (SPIHT).
Keywords: Medical Image, MFHWT, Multi wavelet, ROI, SPIHT.
I. INTRODUCTION
Image compression is the process of encoding information using fewer bits.
Compression is useful because it helps to reduce the consumption of expensive resources, such
as hard disk space or transmission bandwidth. It also reduces the time required for images to be
sent over the Internet or download from web pages. It also helps in accelerating transmission
speed [1]. Data compression methods are usually classified as either lossless or lossy methods
[1] [8].
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2. International Journal of Graphics and Multimedia (IJGM), ISSN 0976 – 6448(Print),
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1. Wavelet Transform
When the signal in time for its frequency content is analyzed then in that wavelet
functions are used. Multi resolution hierarchical characteristics are provided by wavelet based
compression. Hence image can be compressed at different levels of resolution. It can be
sequentially processed from low resolution to high resolution [1] [8]. It has excellent energy
compaction property which suitable for exploiting redundancy in an image to achieve
compression [2] [8]. Wavelets are localized in the both time and frequency domains. Hence it is
easy to capture local features in a signal [1] [8]. A newer alternative to the wavelet transform is
the multi wavelet transform. Multi wavelets are similar to wavelets but have some important
differences. In particular, whereas wavelets have an associated scaling function and wavelet
function, multi wavelets have two or more scaling and wavelet functions [3].
Fig. 1 (a) Wavelet (b) multi wavelets [3] [8]
2. Haar Transform
The Haar wavelet transformation is a simple form of compression involved in averaging
and differencing term, sorting detail coefficients; eliminate data and reconstructing the matrix
such that the resulting matrix is similar to initial matrix [4].
3. Modified Fast Haar Wavelet Transform (MFHWT)
MFHWT can be done by just taking (w+x+y+z)/4 instead of (x+y)/2 for approximation
and (w+x-y-z)/4 instead of (x-y)/2 for differencing process. 4 nodes are considered at a time
[1]. Also, it is used to reduce the memory requirements and the amount of inefficient movement
of Haar coefficients [5]. Thus MFHWT reduce the calculation work of Haar transform.
4. SPIHT
SPIHT algorithm is one of the powerful algorithm for the compression. After wavelet
transform SPIHT algorithm is used to encode the coefficients of wavelet. In SPIHT sorting is
done by comparing two elements at a time and results in yes/no states. In this sorting pass
coefficients are categorizes into 3 lists [8]:

3. International Journal of Graphics and Multimedia (IJGM), ISSN 0976 – 6448(Print),
ISSN 0976 – 6456(Online) Volume 4, Issue 1, January - April 2013, © IAEME
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LIS List of Insignificant sets are the set of coefficients having magnitude smaller than the
threshold.
LIP List of Insignificant Pixels are the coefficients having magnitude smaller than the
threshold.
LSP List of significant pixels are the pixels those magnitude is larger than that of threshold.
In this pass, only bits related to the LSP entries and binary outcomes of the magnitude tests are
transmitted to the decoder. In implementation, we grouped together the entries in the LIP and
LIS which have the same parent into an entry element. For each entry element in LIP, we
estimated a pattern in both encoder and decoder to describe the significance status of each entry
in the current sorting pass. If the result of the significance test of the entry item is the same as
the specified pattern, we can use one bit to represent the status of the whole entry atom which
otherwise had two entries and representation of significance by two bits. If the significance test
result does not match the pattern, we transmitted the result of the significance test for each entry
in the atom. In Refinement pass for each entry in the LSP, except those included in the last
sorting pass , output nth bit of the entry [6]. There are two passes in SPIHT one is sorting pass
which is initial step and other is refinement pass. In sorting pass sorting is done by comparing
two elements at a time, and each comparison results in yes/no. it checks the significance of
coefficients present in LIS. If the coefficients are significant then it results in yes and move to
LSP. If they are not significant it results in no. In refinement pass it is performed after sorting
pass the significant coefficients which we get from sorting pass are send to decoder[8].
5. Region of Interest (ROI)
Region of interest is the selected portion of the image which contains the information that
is required. ROI is a feature introduced to overcome the loss of information in parts of an image
which are more important than others [7]. ROI can be defined by a user and they are encoded
with better quality than the rest of the image [8].
6. Medical Images
Medical science grows very fast and hence each hospital needs to store high volume of
data about the patients. And medical images are one of the most important data about patients.
Medical images are important as they are used by doctors in order to keep record of patients for
long term. In order to keep the record of patients for long terms they are compressed using
compression techniques so that large amount of data can be store. There are many types of
medical images that are used to detect disease of patients. MRI is magnetic resonance image
which is used to get information about tissues, organs in human body. Other types are X-ray,
CT(computer tomography) , ECG(electrocardiogram) [8].
II. PROPOSED SCHEME
Purposed algorithm modifies the existing SPIHT algorithm with multi wavelet
transformation and multi wavelet decomposition will be performed with MFHWT. To perform
the operation of compression using improved SPIHT, following algorithm is used:

4. International Journal of Graphics and Multimedia (IJGM), ISSN 0976 – 6448(Print),
ISSN 0976 – 6456(Online) Volume 4, Issue 1, January - April 2013, © IAEME
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Step1: Read the image as matrix.
Step2: Select the region of interest (ROI) that provides the information which is required.
Step3: Apply SPHIT algorithm to find the list of significant and insignificant pixels or
frequency bands.
Step4: To find the LSP we use the multi wavelet decomposition which will perform with the
help of MFHWT.
Step5: After applying MFHWT we get a transformed image of input image.
Step6: for reconstruction process applies the inverse.
Step7: Calculate Compression ration and PSNR for reconstructed image.
In objective measures of image quality metrics, some statistical indices are calculated to
indicate the quality of reconstructed image. The image quality provides some measure of
closeness between two digital images by exploiting the differences in the statistical distribution
of pixel values. The most commonly used error metrics used for comparing compression are
Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR).
PSNR computes Peak Signal to Noise Ratio, in decibels, between two images. This ratio is
used to provide the quality measurement between the original and a compressed image. Higher
the PSNR more will be the quality.
MSE computes Mean Square Error between the compressed image and original image. Lower
the value of MSE lowers the error.
III. EXPERIMENTAL RESULTS
Fig. 2 GUI for image compression

5. International Journal of Graphics and Multimedia (IJGM), ISSN 0976 – 6448(Print),
ISSN 0976 – 6456(Online) Volume 4, Issue 1, January - April 2013, © IAEME
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Fig. 3 Compression of medical image using SPIHT
Fig. 4 Compression of medical image using ISPIHT

6. International Journal of Graphics and Multimedia (IJGM), ISSN 0976 – 6448(Print),
ISSN 0976 – 6456(Online) Volume 4, Issue 1, January - April 2013, © IAEME
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Fig. 5 Compression of Teeth using SPIHT
Fig. 6 Compression of Teeth using ISPIHT

7. International Journal of Graphics and Multimedia (IJGM), ISSN 0976 – 6448(Print),
ISSN 0976 – 6456(Online) Volume 4, Issue 1, January - April 2013, © IAEME
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IV. RESULTS AND CALCULATIONS
After the experiments performed on Images in MATLAB, we have realized that above
factors determine the quality of reconstructed image. Our technique is better than those of other
techniques of compression, because this technique provides better quality, avoid loss of
information. The quality of image is measured by the Peak Signal to Noise Ratio (PSNR).
Following table provides the PSNR values on the image.
Table1. Calculation of PSNR values by applying SPIHT and Improved SPIHT.
V. CONCLUSION
A number of techniques have been proposed on compression; however our proposed
technique is better than other techniques as this technique provide more quality and less loss of
information. The proposed compression scheme is evaluated on the medical images to
compress them with better quality so that there is no loss of information. And can be send to
doctors without any loss of information with better quality. Our proposed compression scheme
is based on ROI that provide the part of image that contains the information which is required.
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SR.
NO.
Techniques PSNR
value
Bpp Compression
Ratio
Compressed
Image
1. SPIHT 60.95 0.8108 3.3787
2. ISPIHT 77.0031 2.5759 10.7328

8. International Journal of Graphics and Multimedia (IJGM), ISSN 0976 – 6448(Print),
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