JOURNAL ARTICLE

Region Of Interest Based Compression Of Medical Image Using Discrete Wavelet Transform

Sudeepti Dayal and Neelesh Gupta

Year: 2018 Journal:   Zenodo (CERN European Organization for Nuclear Research)   Publisher: European Organization for Nuclear Research

Abstract

Image abbreviation is utilized for reducing the size of a file without demeaning the quality of the image to
an objectionable level. The depletion in file size permits more images to be deposited in a given number of
spaces. It also minimizes the time necessary for images to be transferred. There are different ways of
abbreviating image files. For the use of Internet, the two most common abbreviated graphic image formats
are the JPEG formulation and the GIF formulation. The JPEG procedure is more often utilized or
photographs, while the GIF method is commonly used for logos, symbols and icons but at the same time
they are not preferred as they use only 256 colors. Other procedures for image compression include the
utilization of fractals and wavelets. These procedures have not profited widespread acceptance for the
utilization on the Internet. Abbreviating an image is remarkably not similar than the compressing raw
binary data. General-purpose abbreviation techniques can be utilized to compress images, the obtained
result is less than the optimal. This is because of the images have certain analytical properties, which can
be exploited by encoders specifically designed only for them. Also, some of the finer details of the image
can be renounced for the sake of storing a little more bandwidth or deposition space. In the paper,
compression is done on medical image and the compression technique that is used to perform compression
is discrete wavelet transform and discrete cosine transform which compresses the data efficiently without
reducing the quality of an image.

Keywords:
Discrete wavelet transform Image compression Compression (physics) Wavelet transform Artificial intelligence Computer science Wavelet Computer vision Image (mathematics) Mathematics Materials science Image processing Composite material

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Topics

Radiomics and Machine Learning in Medical Imaging
Health Sciences →  Medicine →  Radiology, Nuclear Medicine and Imaging
Brain Tumor Detection and Classification
Life Sciences →  Neuroscience →  Neurology

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