Medical imaging, as we all know, is the process of taking images of various parts of the human body for diagnostic and surgical purposes. Some of the popular medical imaging modalities are X-ray radiography, Magnetic resonance imaging, Medical ultrasound, Computed tomography etc. Since, these images contain clinical data of extreme importance for treatment follow-ups and are acquired at cost of radiation exposure, infrastructure, money and time involved. Thus, once acquired, the medical imaging data should not be disposed off casually, instead it should be retained so that it can be utilized for various medical applications and the chances of repeated testing can be minimized. Also, maintaining electronic health records of patients serves as database for medical research and experimentation. However, growing use of digital imaging techniques has exponentially increased the demands for storage space, handling and transfer of these medical images. These digital image files have large size and thus, require a huge amount of memory space. Moreover, transmitting these files over networks is a time consuming job.
Let us consider the example of a low resolution color image of dimensions 512 x 512 which is to be transmitted over telephone lines. Now, using a 48000 bits/sec modem, the transmission of this single image itself would take approximately 22 minutes which is unacceptable for most applications especially for Telemedicine applications. Hence, the development of techniques for efficient storage and transmission of images has become quite necessary .
1.1 Image compression- an overview
A common characteristic of most of the images is that the neighboring pixels are highly correlated and therefore contain superfluous information. Image compression techniques make use of this fact to determine a representation in which picture elements are less correlated. Compression removes redundancy from the picture elements and irrelevant information by omitting pixel values that cannot be noticed by human eye.
Image compression is an efficient tool for transmission and storage of medical images provided used appropriately. In other words an optimal compression ratio should be chosen so as to suit the needs of medical examination, without compromising with its diagnostic value .
1.2 Types of Compression
Image compression can be classified into two types viz. lossless and lossy compression.
Lossless compression is the technique of reducing the size of an image without any virtual loss of information. It is also known as reversible form of image compression since the image obtained after compression and then decompression resembles the original one. Typical compression ratios that can be achieved ranges from 1.5 to 3.6 .
Conversely, lossy or irreversible form of compression techniques are those in which some or the other information is always lost. Though, lossy compression algorithms are capable of compressing images at ratios much higher than that...