Hospitals handle their medical image data on computers. Through computers and network it is possible to distribute the image data across the computers within the hospital for processing . X-Ray and computerized tomography (CT) scan for example produce sequence of images thus generating enormous data and transmitting such high volume data over the network can be a challenge. To overcome this problem image compression is introduced in the field of medical imaging. There are numerous compression research studies being done to figure out the compression techniques that can be suitably applied to medical images.

There are several types of image compression techniques available but in case of biomedical images the loss of diagnosability of the image due to compression is unacceptable and hence to achieve higher degree of compression without any significant loss in the diagonasability of the images hybrid schemes of DWT (Discrete wavelet Transform), DCT (Discrete Cosine Transform) and Huffman encoding compression techniques are employed.

Image Compression using hybrid schemes of DWT, DCT and Huffman encoding

An effective DWT algorithm has been performed on the RGB (Red, Green, Blue) parts of the extracted input image separately. Once the DWT is performed on the image then next is to apply DCT by dividing the image into 60X60 blocks to make the components of frequency of the image which are greater than 60 as 0. After this histogram probability reduction function for all RGB components are calculated using Mean intensities. Then Image quantization is performed using ,,q'' factor which calculates probability index for each unique quantity. After applying quantization, Huffman code for each unique symbol is calculated so as to compress the image using Huffman compression. At the end the Compression ratio and Peak-signal-to noise ratio is calculated reducing the amount of data required to represent a given quantity of information

Original Brain image (256X256)

Compressed Brain image at q=2 and q=5

Steps to Perform Medical Image Compression

Image loading & resizing: In order to compress the image, the foremost step is to load the image and then the loaded sized into 256x256 formats so to reduce the compression time.

Discrete wavelet Transform (DWT): For the compression, first DWT is applied on the image using Threshold value. Threshold values neglects the certain wavelet coefficient for doing this one has to decide the value of threshold. Value of threshold affects the quality of compressed image.

Discrete Cosine Transform (DCT): DCT is applied separately on R, G and B components of the image. Discrete cosine transform is applied on the compressed image to further compress the image by selecting the DCT threshold value.

Huffman compression for R, G and B:In the proposed compression method before applying Huffman compression, quantization is applied as it will give better results. In quantization, compression is achieved by compressing a range of values to a single quantum value. When the given number of discrete symbols in a given stream is reduced, the stream becomes more compressible. After the image is quantized, Huffman compression is applied. The Huffman has used a variable length code table for the encoding of each character of an image where the variable length code table is derived from the estimated probability of occurrence for each possible value of the source symbol. Huffman has used a particular method for choosing the representation for each symbol which has resulted in a prefix codes. These prefix codes expresses the most common source symbols using shorter strings of bits than are used for less common source symbols. In this way, we have achieved a compressed image.

By: Ms. Sandhya Sharma, Asst. Professor, ECE, Chitkara University, Himachal Pradesh