Performance Comparison of Wavelet Transforms based Medical Image Compression

V. Anusuya¹, Stency V. S.², G. Srividhya³, M. K. Mohammed Faizel⁴, G. Arul Kumaran⁵,

R. Santhosh⁶, P. Sherubha⁷

¹Associate Professor, Department of IT, Ramco Institute of Technology, Rajapalayam, Tamil Nadu, India

²Assistant Professor, Department of Computer Applications, Mercy College, Palakkad, Kerala, India

³Assistant Professor (Grade I), Department of Electronics and Communication Engineering, Panimalar Engineering College, Poonamalle, Chennai, Tamil Nadu, India

⁴Assistant Professor, Department of Artificial Intelligence and Data Science, K. Ramakrishna College of Engineering, Samayapuram, Tamil Nadu, India

⁵Associate Professor, School of Computing and Information Technology, REVA University, Bangalore, Karnataka, India

⁶Professor, Department of Computer Science and Engineering, Karpagam Academy of Higher Education, Tamil Nadu, India

⁷Associate Professor, Department of Information Technology, Karpagam College of Engineering, Coimbatore, India

Mail: pgkrishanu@gmail.com; stencyvs@mercycollege.edu.in; srividhyakrishiv@gmail.com; moh5zal@gmail.com; erarulkumaran@gmail.com; santhoshrd@gmail.com; sherubha.p@kce.ac.in

Abstract

Medical image analysis plays a vital role in diagnosis of diseases and the need of the day is to arrive at a simple and efficient compression technique. This paper proposes a comparative analysis of three different wavelet based medical image compression techniques. First algorithm is based on Bi-orthogonal wavelet with Parallel coding  (BiWT-PC) , second is based on Haar wavelet with block coding  (HWT-BC) and third algorithm is based on stationary wavelet transform with Parallel coding (SWT-PC). In this work, 3D medical image is converted into 2D slices and preprocessed using lifting scheme. Wavelet transform is applied to this preprocessed image, which divides the image into multilevel sub-bands. Then, the suitable encoding method is applied to get the compressed image. At the receiver side, the original image is recovered back by applying inverse wavelet transform and proper decoding over the compressed image. Experimentations are carried out over MRI and CT images with four quantitative metrics such as PSNR, CR, DcT and EcT. From the experimental analysis, it is observed that SWT-PC method is quite efficient since it has high PSNR and low CR.

Keywords: Stationary Wavelet Transform; Haar Wavelet Transform; Biorthogonal Wavelet Transform; MRI; CT