Abstract
Medical data grows very fast and hence medical institutions need to store high volume of data about their patients. Medical images are one of the most important data types about patients. As a result, hospitals have a high volume of images that require a huge storage space and transmission bandwidth to store these images. Most of the time transmission bandwidth is not sufficient to storing and transmit all the image data with the required efficiency. Image compression is the process of encoding information using fewer bits than an un-encoded representation using specific encoding schemes. Compression is useful because it helps to reduce the consumption of expensive resources, such as storage space or transmission bandwidth (computing). In this paper, a medical image compression technique based on combining region growing and wavelets algorithms was introduced. A region growing algorithm is used to simply partitioning the image into two parts foreground and background depending on the intensity values. Then, wavelets methods applied on foreground regions including important regions. These regions are compressed lossless to keep the appearance of the image as intact while making the simplifications and the other region is lossy compressed to reduce the file size, leading to that the overall compression ratio gets better and the reconstructed image seems like the original one. To prove the capability of the proposed algorithm, different four image structures from X-Ray, Computed tomography CT and Magnetic resonance imaging MRI types are tested.
Highlights
The need for compressing medical image is increasing daily cause every hospital have to keep archive to store the patient history including their different types of medical images
Chen had designed & implemented a new medical image compression technique based on subband decomposition called Discrete Cosine Transform (DCT) and modified SPIHT data organization
Experimental results showed that the quality of the reconstructed medical image has been increased by the peak signal-to-noise ratio (PSNR) value [4]
Summary
The need for compressing medical image is increasing daily cause every hospital have to keep archive to store the patient history including their different types of medical images. Julius introduced a way of compressing medical images by using Discrete Haar wavelet transform (DWT) for improving compression efficiencies for monochrome and color images [1]. Ramakrishnan and Sriraam has developed a new image compression technique in the field on medical images called internet transmission of DICOM (Digital Imaging and Communications in Medicine) with effective bandwidth utilization. For the progressive transmission of DICOM images a wavelet based encoder called SPIHT (set partitioning in hierarchical trees) has been used [3]. Chen had designed & implemented a new medical image compression technique based on subband decomposition called DCT and modified SPIHT (set partitioning in hierarchical trees) data organization. The detailed features of an image were stored in the translation function In this method, high-frequency sub bands are used in good number for reduction of the redundancy by using the algorithm with modified SPIHT.
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