In recent years, telemedical need arose, as a result of the increasing numbers of patients significantly, where using image compression is one of the most important techniques in the field processing research field, as well as in Multiple applications such as medicine, reconnaissance, aerial surveillance, and space. Image compression necessary to reduce transport costs due to rising transport prices and distances

In this paper, take the original image and then divide it into two separate sub-sections (ROI and non-ROI), an area of ??importance in medical imagery, which is very important. This area should be diagnosed with extreme precision because it refers to the disease and depends on the diagnosis. Here in our technology, the spectrum propagation algorithm, which contains binary signs used in DCT conversion, was used for non-ROI.

Compressing a digital image can facilitate its transmission, storage, and processing. As medical imaging becomes increasingly digital, the quantities of image data are forcing consideration of compression in picture archiving and communication systems (PACS) and evolving telemedicine systems. HIS (Hospital Information System) and PACS based on DICOM standards pave the way to store medical images and ease remote medical treatments

Telemedicine

The request of this growing today on telemedicine farsightedness geographical distances, as a result of the development of information technology and telecommunications has been the use of such technology to provide health care and clinical trials and the exchange of information from one location to another through electronic messages, which use a range of applications using video with directions, smart devices, e-mail, and to facilitate the process of telemedicine. Sometimes you need an interview directly between patient and health experts is very essential, so be patient at the site and another site expert Where the use of flat-two directions to the interview. The number of patients with various diseases of the spine a large segment of society (break, sliding, lumbar spine trauma. Etc.) so telemedicine contributed to solving part of these problems.

Telemedicine the benefits of several could be envisaged through the following:

a)   Improving access to information as a result of information technology.

b)   The provision of health care through the delivery of information

c)   Healthcare services.

d)   Vocational education to facilitate the services.

e)   The development of techniques for the examination and reducing the costs of social care.

In telemedicine, medical images are very important in the field of medicine, with the passage of time and the data generated a very large number of medical images by storing the disease data generated by imaging techniques, computed tomography (CT), digital subtraction angiography (DSA), positron emission tomography (PET), and magnetic resonance imaging (MRI), Etcetera medical imaging techniques.

As a result of the foregoing, it would be useful if the pressure of medical images by storing information the basic picture of the restructuring of the Picture.

The process of the pressure of the pictures consist of two stages:

1.   Research on the characteristics of the image data, histogram gray-level, Photos of entropy, links, and functions.

2.   Finding suitable compression techniques to get a picture of those characteristics .

Fractal Image Compression

Fractal encoding: Mathematical technique to encode certain medical image into a set of mathematical data which show the typical encoding image repeated geometric characteristics (fractal image). The truth is that reliable Fractal encodes that all creatures consisting of information in the form of the relevant resolutions, and the process of replication of patterns called an attractor, Fractal encodes is used to convert the image into a symbol often fractional. The encoding process extreme account is either IFS (Iterated Function Systems) or by PIFS (Partitioned Iterated Function Systems) are used to achieve FIC. the reason goes back to The large number of operations required to determine a fractal pattern of lines in the Picture.

Iterated Function Systems Represents briefly mathematical steps the process of fractal image according to the theory of IFS.  An IFS is a series of transitions that have the following properties

a.  

b.   , where w maps a metric space, { : point, , also to choose an initial image  

Depending on the characteristics of this theory, it attractor map of W to the original image to compress which can obtained through any initial image form in accordance apply map W.

Listless Speck

Listless Speck refers to partitioning embedded block. Note that the same rules of split block used by LSK are also used by Speck, through the tagging of all initial pixels each sub band Ranges. In order to illustrate the work Listless Speck by tacking the following: -

a.   Storage

b.   Initialization

c.   Encoder Algorithm

In this method, the main encoder algorithm is performed every bit plane, b, a beginner with B, and decreased to 0. Here in the algorithm, there are three tracks are as follows:

1.   Pass the little pixels

2.   Pass a small set

3.   Pass Refinement. Where each bit plane, Importance level is given by

Moreover, we can start by passing the little pixels, and identify important new elements and mark as MNP. When you move to the next item, this can be described as a mark [i]=MIP When the mark is MNP on the elements in the state array

Note here the increased memory requirements and the main objective of this lies in the use of signs of table markers

Decompression Process

After the completion of the process of the compression of medical images and fulfilling, the goal of compression requires us to reverse process to remove the compression from medical images a process very easy. Therefore, we must get rid of fractal parameter and get the image, through this process the original image is obtained, By LSK decoding we get decompressed through transmission of fractal the approximation and Different image.

The application of medical images, coding in the area of telemedicine in this research with compression system. When the user begins the selection the ROI, start the ROI coding, by using fractal encoding the select similar groups in the input medical image, Well, when we use LSK encoder. We get ROIs different are encoded, In the end, all encoded data gathered through the integration of all ROI medical image data that is encoded, as well as similar blocks different blocks. The focus should be on that medical images used in pressure is not deformed after compression. The methods proposed in this paper have a high proportion of pressure when compared with the techniques used in the past, In addition to technology Fractal, LSK and Context tree faster and more accurate than the previous techniques.