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Top1. Introduction
Over recent years, there has been a huge series of images that are getting generated in hospitals to diagnose various diseases. Doctors / Clinicians prefer to judge the illness of the patients through the images generated of internal organs. These Medical images are often generated using acquisition devices such as CT scan, MRI, X-Ray, Etc., Commonly these medical images are volumetric in size which requires high storage (Gonzalez et al., 2009). Speed and Bandwidth are the major setbacks as considered while transmitting the medical images (Smith-Bindman et al., 2008) for telemedicine. This problem can be overcome by compressing a medical image effectively. Digital image compression achieves the redundancy in an image that can be represented using a smaller number of bits to acquire an acceptable quality image. Compression deals with two variety of methods such as Lossy compression and Lossless compression. Generally, images used in a medical domain need to be compressed without losing the data (Khalaf, Abdulsahib, Kasmaei, et al., 2020) for better diagnosis, which directs an efficient lossless medical image compression algorithm.
Even a vast number of algorithms were already proposed for finding an efficient compression algorithm, still finding a lossless medical image compression algorithm is a challenging task. The performance of the lossless algorithm is measured using the ratio bit rate required for the original input image to its compressed image called compression ratio (CR). Bit rate is defined by an average number of bits essential to represent each pixel of the compressed image. Subjective and Objective quality measures are also 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).
The proposed method is analyzed by comparing its performances through different metrics with metaheuristic algorithms and also with transform-based compression methods (Saravanan et al., 2013),(Juliet et al., 2016) and (Uma Vetri Selvi & Nadarajan, 2017) with a dataset of medical images. Experimental results prove that the proposed method achieves a higher value with various performance metrics such as peak signal-noise ratio (PSNR), Compression Ratio (CR), and Computational time (CT) over other algorithms. This paper is arranged as follows: Section 2 deals with related works over the different compression transforms followed by Section 3 that describes the proposed method with Tetrolet transform and SPIHT encoder. Section 4 details the performance analysis of the proposed algorithm over other methods with various metrics. Section 5 deals with the Results and discussion of the compression algorithms and finally conclusions are given in section 6.