An Optimal Lempel Ziv Markov Based Microarray Image Compression Algorithm

• Published in: Computers, materials & continua Vol. 69; no. 2; pp. 2245 - 2260
• Main Authors: Sowmyalakshmi, R., Ibrahim Waly, Mohamed, Yacin Sikkandar, Mohamed, Jayasankar, T., Sayeed Ahmad, Sayed, Rani, Rashmi, Chavhan, Suresh
• Format: Journal Article
• Language: English
• Published: Henderson Tech Science Press 2021
• Subjects: Algorithms; Coding; Image compression; Image quality; Image resolution; Image transmission; Medical imaging; Optimization; Prostate; Scientific papers
• ISSN: 1546-2226; 1546-2218; 1546-2226
• DOI: 10.32604/cmc.2021.018636

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.