Open Access

ARTICLE

An Optimal Lempel Ziv Markov Based Microarray Image Compression Algorithm

R. Sowmyalakshmi^1,*, Mohamed Ibrahim Waly², Mohamed Yacin Sikkandar², T. Jayasankar¹, Sayed Sayeed Ahmad³, Rashmi Rani³, Suresh Chavhan^4,5

1 Department of Electronics and Communication Engineering, University College of Engineering, BIT Campus, Anna University, Tiruchirappalli, 620024, India
2 Department of Medical Equipment Technology, College of Applied Medical Sciences, Majmaah University, Al Majmaah, 11952, Saudi Arabia
3 Department of Computer Engineering, College of Engineering and Computing, Al Ghurair University, Dubai, 37374, United Arab Emirates
4 Automotive Research Center, Vellore Institute of Technology, Vellore, 632014, India
5 Federal University of Piauí, Teresina, 64049-550, PI, Brazil

* Corresponding Author: R. Sowmyalakshmi. Email:

Computers, Materials & Continua 2021, 69(2), 2245-2260. https://doi.org/10.32604/cmc.2021.018636

Received 15 March 2021; Accepted 20 April 2021; Issue published 21 July 2021

Abstract

In the recent years, microarray technology gained attention for concurrent monitoring of numerous microarray images. It remains a major challenge to process, store and transmit such huge volumes of microarray images. So, image compression techniques are used in the reduction of number of bits so that it can be stored and the images can be shared easily. Various techniques have been proposed in the past with applications in different domains. The current research paper presents a novel image compression technique i.e., optimized Linde–Buzo–Gray (OLBG) with Lempel Ziv Markov Algorithm (LZMA) coding technique called OLBG-LZMA for compressing microarray images without any loss of quality. LBG model is generally used in designing a local optimal codebook for image compression. Codebook construction is treated as an optimization issue and can be resolved with the help of Grey Wolf Optimization (GWO) algorithm. Once the codebook is constructed by LBG-GWO algorithm, LZMA is employed for the compression of index table and raise its compression efficiency additionally. Experiments were performed on high resolution Tissue Microarray (TMA) image dataset of 50 prostate tissue samples collected from prostate cancer patients. The compression performance of the proposed coding esd compared with recently proposed techniques. The simulation results infer that OLBG-LZMA coding achieved a significant compression performance compared to other techniques.

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Citations