Implementation and Performance Assessment of Biomedical Image Compression and Reconstruction Algorithms for Telemedicine Applications

Abstract

Compression serves as a significant feature for efficient storage and transmission of medical, satellite, and natural images. Transmission speed is a key challenge in transmitting a large amount of data especially for magnetic resonance imaging and computed tomography scan images. Compressive sensing is an optimization-based option to acquire sparse signal using sub-Nyquist criteria exploiting only the signal of interest. This chapter explores compressive sensing for correct sensing, acquisition, and reconstruction of clinical images. In this chapter, distinctive overall performance metrics like peak signal to noise ratio, root mean square error, structural similarity index, compression ratio, etc. are assessed for medical image evaluation by utilizing best three reconstruction algorithms: basic pursuit, least square, and orthogonal matching pursuit. Basic pursuit establishes a well-renowned reconstruction method among the examined recovery techniques. At distinct measurement samples, on increasing the number of measurement samples, PSNR increases significantly and RMSE decreases.

Adaptive block size selection in a hybrid image compression algorithm employing the DCT and SVD

“…The utilization of image compression techniques is of great significance in the process of lowering the file size of digital photos, with the primary objective of preserving the visual quality of the image. Images play a crucial role in a multitude of applications by facilitating efficient storage, transport, and sharing processes [1][2][3][4]. Some prominent image compression techniques, as depicted in Figure 1, are explained as follows:…”

“…Vector Quantization: In vector quantization, identical image elements are grouped and represented by a single codebook entry. This approach is incorporated into image compression standards such as Video Coding Experts Group (VCEG) and Moving Picture Experts Group (MPEG) [2][3][4]. iii.…”

“…This standard is also a coding system that is based on the DCT combined with the wavelet-based method. This standard further supports spatial random access while capturing varying degrees of the region of interest [1][2][3][4].…”

“…It predicts pixel values based on neighboring values and only encodes prediction errors. Examples of predictive coding include differential pulse code modulation (DPCM) and adaptive differential pulse code modulation (ADPCM) [1][2][3][4]. v. Hybrid Techniques: Hybrid compression techniques integrate numerous compression techniques to maximize their benefits.…”

“…v. Hybrid Techniques: Hybrid compression techniques integrate numerous compression techniques to maximize their benefits. For example, the hybrid algorithm using the DCT and singular value decomposition (SVD) with adaptive block size selection incorporates the benefits of frequency-based and decomposition-based techniques [1][2][3][4]. vi.…”