Research And Implementation Of Batch Images Compression Algorithm Based On The Common Characteristic

Batch images compression algorithm based on the common characteristic plays a very important role in the fields of transmission,storage of medical images.Along with the advanceof image processing and computer technology,development of digital imaging technology has been used increasingly in the medical fields,and the image information is becoming doctors'diagnosis basic;so the processing of medical images is vital important.Medical images has a great amount of data and it will take up more channel width,which make transport more expensive,transmission rate slower.This is very adverse to image storage,transfer and use,but it also hindered the doctor effective access and use of the images.As the foundation of the development of medical imaging,image compression method has became one of the fastest growing areas in medical technology,so as to make the clinical doctor observation more intuitive and more clear,which also make diagnostic more effective.This topic put forward an efficient image compression method,it significantly improveimages compression efficiency while it keeps the integrity of the original images as much as possible providing more images information for the doctors' diagnosis.On the other hand,the innovation proposed by this compression method is that it only transfers intermediate data while at the same time it greatly reduces the amount of data transmission.This topic adopts compressed sensing,particle swarm optimization and some other methods,and the simulationwas based on matlab.The general idea is using the common characteristic of plenty of medical images to calculate a final picture which is the most similar to the all images and it takes the role of basic image.And the difference between basic image and the original images can be compressed,the data we got takes up much fewer space,which means it can be easily store and transform.When we need the original images again,we can just refactor the difference fromthe compressed data and load the difference to the basic image,finally reconstruct the original images.