Abstract
Over recent times, medical imaging plays a significant role in clinical practices. Storing and transferring the huge volume of images becomes complicated without an efficient image compression technique. This paper proposes a compression algorithm that uses a Haar based wavelet transform called Tetrolet transform, which reduces the noise on the input images and decomposes with a 4 x 4 blocks of equal squares called tetrominoes. It opts for a decomposing using optimal scheme for achieving the input image into a sparse representation which gives a much-detailed performance for texture and edge information better than wavelet transform. Set Partitioning in Hierarchical Trees (SPIHT) is used for encoding the significant coefficients to achieve efficient image compression. It has been investigated with various metaheuristic algorithms. Experimental results prove that the proposed method outperforms the other transform-based compression in terms of PSNR, CR, and Complexity. Also, the proposed method shows an improved result with another state of work.
Highlights
Over recent years, there has been a huge series of images that are getting generated in hospitals to diagnose various diseases
The performances are evaluated for the proposed method with a set of six medical images of size (256 X 256, 8 bits per pixel) with DICOM format and the quality of the compressed images has been measured in terms of Peak signal noise ratio, Mean square error (MSE), Compression ratio and computational time
The effectiveness of the proposed method is evaluated on comparison with existing algorithms such as Curvelet (Saravanan et al, 2013), Contourlet (Uma Vetri Selvi & Nadarajan, 2017) and Ripplet transform (Juliet et al, 2016), all combined with Set partitioning in hierarchical trees (SPIHT) encoder
Summary
There has been a huge series of images that are getting generated in hospitals to diagnose various diseases. Subjective and Objective quality measures are considered over compression Popular transforms such as Wavelet and JPEG based algorithm achieves a high compression ratio but failed to maintain the quality of the image. As the main drawback states that algorithms are irreversible, this paper proposes a compression method for medical images using a Haar wavelet-based transform called Tetrolet (Krommweh, 2010). This method decomposes the input medical images into blocks to find a sparsest tetrolet representation over the image and encodes with (SPIHT) Set partitioning hierarchical tree method (Dragotti et al, 2000).
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