A Lossless Compression Method for Medical Image Sequences Using JPEG-LS and Interframe Coding

Abstract

Hospitals and medical centers produce an enormous amount of digital medical images every day, especially in the form of image sequences, which requires considerable storage space. One solution could be the application of lossless compression. Among available methods, JPEG-LS has excellent coding performance. However, it only compresses a single picture with intracoding and does not utilize the interframe correlation among pictures. Therefore, this paper proposes a method that combines the JPEG-LS and an interframe coding with motion vectors to enhance the compression performance of using JPEG-LS alone. Since the interframe correlation between two adjacent images in a medical image sequence is usually not as high as that in a general video image sequence, the interframe coding is activated only when the interframe correlation is high enough. With six capsule endoscope image sequences under test, the proposed method achieves average compression gains of 13.3% and 26.3% over the methods of using JPEG-LS and JPEG2000 alone, respectively. Similarly, for an MRI image sequence, coding gains of 77.5% and 86.5% are correspondingly obtained.

Huffman Image Compression Incorporating DPCM and DWT

“…The entropy (H) of the source can be determined from the following Equation (2). The entropy value describes how much compression is possible for that image in the form of bit per pixel.…”

A Review of the Effective Techniques of Compression in Medical Image Processing

“…Miaou et al [23] have conferred a way that mixes the JPEG-LS and an inter-frame encoding with motion vectors to boost the compression performance of deploying JPEG-LS alone.…”

Hybrid Algorithm for Medical Image Sequences using Super-Spatial Structure Prediction with LZ8

“…These medical image sequences require considerable storage space [1].The solution to this problem could be the application of compression. Medical image compression is very important in the present world for efficient archiving and transmission of images.…”

“…There are several lossless image compression algorithms like Lossless JPEG,JPEG 2000,PNG,CALIC and JPEG-LS.JPEG-LS has excellent coding and best possible compression efficiency [1].But the Super-Spatial Structure Prediction algorithm proposed in [5] has outperformed the JPEG-LS algorithm. This algorithm divides the image into two regions, structure regions (SRs) and non-structure regions (NSRs).The structure regions are encoded with Super-Spatial Structure Prediction technique and non-structure regions are encoded using JPEG-LS.…”

“…No codebook is required in this compression scheme because the structure components are searched within the encoded image regions [6]. JPEG-LS has excellent coding and best possible compression efficiency [1].This is a simple and baseline algorithm which consists of two independent and distinct stages called modeling and encoding. This is developed with the aim of providing a low complexity lossless and nearlossless image compression.…”