HIERARCHAL CODING STRATEGY FOR EFFICIENT COMPRESSION FOR MEDICAL IMAGES: INTRODUCTION (2)

Image compression is common in the transmission of images. JPEG (Joint Photographic Experts Group) compression, the most common image format used on the Internet, is also used for medical imaging, although Wavelet image compression has also been investigated. To achieve an appreciable reduction in image size (i.e., more than 1:4), some loss of information and consequently some degradation of image quality must be expected.

JPEG image compression breaks the image into blocks of 8 by 8 pixels and converts these blocks subsequently into spatial frequency components. Sampling make use of this frequency domain information (in a step called quantization) by closely preserving the low-frequency components and approximating the high-frequency components. The amount of information that is discarded determines the amount of compression. A coding process compresses the remaining frequency coefficients. The decompressing process reverses these steps. The effects of compression can be seen at high compression levels when “blocking artifacts” become evident.

There are a number of different Wavelet algorithms, their differences lying in the type of filter used. In general, band- and low-pass filters are applied to the pixel rows of an image and then reapplied to the columns of pixels. Which produces information on the low-frequency components of the image and the horizontal, vertical, and diagonal detail in the image, As with the Wavelet Based Compression algorithm, there are also quantizing and coding steps. A number of studies have been conducted on medical image compression. Bittorf et al. assessed compressed images of skin lesions, concluding that images needed to be at least 768 3512 pixels with 24-bit color resolution (i.e., . 1 MB images) to be suitable for diagnosis. Persons et al. found that low-contrast objects in the images still remain visible after Wavelet Based Compression and Wavelet image compression of magnetic resonance imaging and computed tomography images, although fine and irregular details are easily degraded. Martin et al. compressed 2-, 3-, and 4-MB fundus images and found that a compression ratio of 1:24 still produced images of diagnostic quality. A number of approaches are considered to find the optimum level of image compression. Some studies determine the compression level at which changes from the original image are first noticed (“just-noticeable-difference”). In one study, the compression ratio limit for chest radiograph images is shown to be 1:6.12.Erickson et al.rated the appearance of structures on chest radiographs, finding that a Wavelet image compression ratio of 1:40 produced images indistinguishable from the original images. Another approach is to determine at what compression level the abnormalities become indistinguishable or the highest compression level that is clinically acceptable. In a previous study we found that a Wavelet Based Compression compression ratio of approximately 1:30 (approximately 20-30 KB images) can be achieved without affecting the ability of the ophthalmologist to identify these abnormalities.13 Kim14 used Wavelet Based Compression compression for 900-KB gastrointestinal color images and showed that compressed images approximately 20 KB in size were still acceptable.

The rest of the paper organized as follows Wavelet based image compression is briefed in section II, In section III contain the proposed system architecture, results and observations were shown in section IV, The conclusion is briefed in section V.

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