Lossless Medical Image Compression Research Articles

Low power high speed FPGA design of lossless medical image compression using optimal deep neural network

Low power high speed FPGA design of lossless medical image compression using optimal deep neural network

Novel Integer Shmaliy Transform and New Multiparametric Piecewise Linear Chaotic Map for Joint Lossless Compression and Encryption of Medical Images in IoMTs

The discrete Shmaliy moment transform (DST) is a type of discrete orthogonal moment transform that is widely used in signal and image processing. However, DST is not suitable for lossless image applications due to its non-integer reversible nature. To overcome this limitation, we introduce the integer discrete Shmaliy transform (IDST) that performs integer-to-integer encoding, leading to a perfect and unique reconstruction of the input image. Next, a new 1D chaotic system model, the 1D multiparametric piecewise linear chaotic map (M-PWLCM), is presented as an extension of the existing 1D PWLCM. The M-PWLCM includes eight control parameters defined over an unlimited interval. To demonstrate the relevance of IDST and M-PWLCM in reversible image processing applications, they are used in a new scheme for lossless compression and encryption of medical images in the internet of medical things (IoMTs). On the one hand, the simulation results show that our scheme offers a good compression ratio and a higher level of security to resist differential attacks, brute force attacks and statistical attacks. On the other hand, the comparative analysis carried out shows the overall superiority of our scheme over similar state-of-the-art ones, both in achieving a higher compression ratio and better security when communicating medical images over unsecured IoMTs.

Combining Image Space and q-Space PDEs for Lossless Compression of Diffusion MR Images

Diffusion MRI is a modern neuroimaging modality with a unique ability to acquire microstructural information by measuring water self-diffusion at the voxel level. However, it generates huge amounts of data, resulting from a large number of repeated 3D scans. Each volume samples a location in q-space, indicating the direction and strength of a diffusion sensitizing gradient during the measurement. This captures detailed information about the self-diffusion and the tissue microstructure that restricts it. Lossless compression with GZIP is widely used to reduce the memory requirements. We introduce a novel lossless codec for diffusion MRI data. It reduces file sizes by more than 30% compared to GZIP and also beats lossless codecs from the JPEG family. Our codec builds on recent work on lossless PDE-based compression of 3D medical images, but additionally exploits smoothness in q-space. We demonstrate that, compared to using only image space PDEs, q-space PDEs further improve compression rates. Moreover, implementing them with finite element methods and a custom acceleration significantly reduces computational expense. Finally, we show that our codec clearly benefits from integrating subject motion correction and slightly from optimizing the order in which the 3D volumes are coded.

Multiplicative Multiresolution Decomposition for Lossless Volumetric Medical Images Compression

With the emergence of medical imaging, the compression of volumetric medical images is essential. For this purpose, we propose a novel Multiplicative Multiresolution Decomposition (MMD) wavelet coding scheme for lossless compression of volumetric medical images. The MMD is used in speckle reduction technique but offers some proprieties which can be exploited in compression. Thus, as the wavelet transform the MMD provides a hierarchical representation and offers a possibility to realize lossless compression. We integrate in proposed scheme an inter slice filter based on wavelet transform and motion compensation to reduce data energy efficiently. We compare lossless results of classical wavelet coders such as 3D SPIHT and JP3D to the proposed scheme. This scheme incorporates MMD in lossless compression technique by applying MMD/wavelet or MMD transform to each slice, after inter slice filter is employed and the resulting sub-bands are coded by the 3D zero-tree algorithm SPIHT. Lossless experimental results show that the proposed scheme with the MMD can achieve lowest bit rates compared to 3D SPIHT and JP3D.

An IoT Method for Telemedicine: Lossless Medical Image Compression Using Local Adaptive Blocks

Image compression gains prominence in picture archiving and communication systems for storage and transmission of data. The advancements in technology enable the usage of computer-assisted algorithms for disease diagnosis and therapeutic planning. In this research work, a prediction-based compression algorithm was proposed to compress DICOM images. The local adaptive block-based predictor compression technique utilizes neighborhood pixel gray values for prediction and the Huffman coder was employed for the encoding of predicted coefficients. The compression results were considered effective, in contrast with the JPEG lossless, Kernel Least Mean Square, Context Adaptive Block-Based Prediction, and Least Mean square Prediction approaches. The performance validation by metrics reveals the effectiveness of the proposed local adaptive block prediction-based approach. The proposed compression algorithm was implemented in Raspberry Pi B+ embedded processor as IoT application. The compressed images are transferred through the cloud to other nodes in the network, thus facilitating the teleradiology for disease diagnosis by physicians.

Lossless Medical Image Compression by Using Difference Transform

This paper introduces a new method of compressing digital images by using the Difference Transform applied in medical imaging. The Difference Transform algorithm performs the decorrelation process of image data, and in this way improves the encoding process, achieving a file with a smaller size than the original. The proposed method proves to be competitive and in many cases better than the standards used for medical images such as TIFF or PNG. In addition, the Difference Transform can replace other transforms like Cosine or Wavelet.

Lossless Compression Algorithm for Medical Images With High Precision Based on Discrete Wavelet Transform

In image distortion and low adaptive recognition after medical image compression, a high precision medical image lossless compression algorithm based on discrete wavelet transform is proposed. A 3D imaging model of multi-dimensional medical images is constructed, and adaptive information enhancement and image restoration processing are performed on the collected medical images. According to the results of high-dimensional segmentation and segmentation, discrete wavelet transform is used to achieve high-precision lossless compression of medical images. The results show that the medical image compression is better non-destructive and the image fidelity is higher, which improves the detection and adaptive recognition ability of medical images

Retraction Note to: Lossless medical image compression algorithm using tetrolet transformation

Retraction Note to: Lossless medical image compression algorithm using tetrolet transformation

Lossless medical image compression based on anatomical information and deep neural networks

Modern imaging modalities generate large volumes of medical data that place a heavy burden on both storage and transmission. Consequently, image data compression is a key research topic in the field of medical imaging. This paper proposes a compression technique that combines anatomical information and a custom deep neural network model to facilitate the transmission and storage of medical image data. This technique first divides the medical image data into specific regions based on anatomical features, namely, image density, relative position, and organ size. A deep neural network is then trained to generate a series of optimal predictors for each region. These predictors can be adaptively switched based on the characteristics of the region being compressed. The residuals are finally compressed with an entropy coding scheme. An evaluation of this compression technique shows that the proposed “divide and conquer” method indeed achieves high prediction accuracy and obtains a compression performance that is better than that of JPEG2000 by 38%.

Efficiently compressing 3D medical images for teleinterventions via CNNs and anisotropic diffusion.

Efficient compression of images while preserving image quality has the potential to be a major enabler of effective remote clinical diagnosis and treatment, since poor Internet connection conditions are often the primary constraint in such services. This paper presents a framework for organ-specific image compression for teleinterventions based on a deep learning approach and anisotropic diffusion filter. The proposed method, deep learning and anisotropic diffusion (DLAD), uses a convolutional neural network architecture to extract a probability map for the organ of interest; this probability map guides an anisotropic diffusion filter that smooths the image except at the location of the organ of interest. Subsequently, a compression method, such as BZ2 and HEVC-visually lossless, is applied to compress the image. We demonstrate the proposed method on three-dimensional (3D) CT images acquired for radio frequency ablation (RFA) of liver lesions. We quantitatively evaluate the proposed method on 151 CT images using peak-signal-to-noise ratio ( ), structural similarity ( ), and compression ratio ( ) metrics. Finally, we compare the assessments of two radiologists on the liver lesion detection and the liver lesion center annotation using 33 sets of the original images and the compressed images. The results show that the method can significantly improve of most well-known compression methods. DLAD combined with HEVC-visually lossless achieves the highest average of 6.45, which is 36% higher than that of the original HEVC and outperforms other state-of-the-art lossless medical image compression methods. The means of and are 70dB and 0.95, respectively. In addition, the compression effects do not statistically significantly affect the assessments of the radiologists on the liver lesion detection and the lesion center annotation. We thus conclude that the method has a high potential to be applied in teleintervention applications.

A near lossless three-dimensional medical image compression technique using 3D-discrete wavelet transform

In the field of medical image processing, there are several imaging modalities that emerged and produced high quality of images that imposes to greater amount of storage space as well as bandwidth for transmission. Therefore, there is always a great need to develop compression techniques on such images. Moreover, they produce three-dimensional images by sequence of 2D slices and process each as like 2D image. Therefore, it is essential that every medical image processing operation needs to converge on 3D space (three-dimensional image). In this paper, we provide the wavelet-based image compression technique that can be directly applied on the 3D images itself. We apply 3D DWT on a 3D volume and the resultant coefficients are taken to further compression process (like thresholding, entropy encoding). The inverse processes are performed for decompression to reconstruct the images. The effects of the results based on different wavelets are assessed and the results of lossy mode are compared in terms to lossless mode.

An optimized JPEG-XT-based algorithm for the lossy and lossless compression of 16-bit depth medical image

JPEG-based compression is the most widely used image compression algorithm. Previously, JPEG-XT-based work focused on 16-bit depth high-dynamic satellite infrared images, but not on medical images. In this study, we represent an optimized JPEG-XT method (OPT_JPEG-XT) that better compresses 16-bit depth medical images by amplifying (N times) discrete cosine transform (DCT) coefficients. The results show that the small integers and the first two decimal portions of DCT coefficients play important roles in the compression of medical images. By using the appropriate N and number of decimal portions (NDP), OPT_JPEG-XT could realize lossless compression of medical images. Regarding upper and lower 8-bit subimages, the upper subimages have more important roles in the improvement of OPT_JPEG-XT performance than the lower subimages; lower subimages could occupy over 90% sizes of the entire encoding files. Thus, OPT_JPEG-XT could save about 60% of storage space with high PSNR (peak signal-noise-ratio, over 100) and low MSE (mean-square-error, less than 0.08) by decreasing the compression efficiency of lower subimages. Compared to the conventional JPEG-XT and JPEG 2000, OPT_JPEG-XT can acquire a similar compression performance (PSNR > 100, MSE < 0.9, SSR (saving space rate, >60%) to JPEG 2000 when using N = 20 for lower subimages and lossless compression of upper subimages. OPT_JPEG-XT could obtain high SSR (about 90%, similar to traditional JPEG-XT) with with much smaller MSE (25 times lower than conventional JPEG-XT). Therefore, OPT_JPEG-XT could be developed a novel compression method that could realize the lossless and lossy compression of medical images.

Gaussian Hermite polynomial based lossless medical image compression

The role of compression is inevitable in the storage and transmission of medical images. The polynomial based image compression is proposed in this work for the compression of abdomen CT medical images. The input images are preprocessed by min–max normalization; the pixels are scanned and subjected to polynomial approximation. The polynomial approximated coefficients are subjected to llyods quantization and encoded by arithmetic coder. The medical image compression using Gaussian Hermite polynomial gives superior results when compared with the legendre polynomial based image compression and JPEG lossless compression techniques in terms of Peak to signal noise ratio (PSNR), Mean square Error (MSE) and other picture quality metrics. The algorithms are tested on real-time DICOM abdomen CT image and can be used for data transfer in teleradiology application.

Lossless Compression of Medical Images based on the Differential Probability of Images

Lossless compression is crucial in the remote transmission of large-scale medical image and the retainment of complete medical diagnostic information. The lossless compression method of medical image based on differential probability of image is proposed in this study. The medical image with DICOM format was decorrelated by the differential method, and the difference matrix was optimally coded by the Huffman coding method to obtain the optimal compression effect. Experimental results obtained using the new method were compared with those using Lempel–Ziv–Welch, modified run–length encoding, and block–bit allocation methods to verify its effectiveness. For 2-D medical images, the lossless compression effect of the proposed method is the best when the object region is more than 20% of the image. For 3-D medical images, the proposed method has the highest compression ratio among the control methods. The proposed method can be directly used for lossless compression of DICOM images.

Lossless Medical Image Compression by Multi Oriented Prediction Technique

Lossless Medical Image Compression by Multi Oriented Prediction Technique

RETRACTED ARTICLE: Lossless medical image compression algorithm using tetrolet transformation

In many analytical applications the reuse of medical images plays an important role. In order to store the images, image compression is the technique used to save the storage space and the storage time. The image compression is used in image transmission for the fast transmission. Also in health care sector the compressed image helps to make it complete computerized. The noise interference is the major problem in the image compression. Thus some compression techniques are used to remove the noise added in medical images. This will improve the quality of the compressed image. In image compression the medical image can be processed in deep degree by de-noising, edge preservation and high compression rate. The aim of the paper is develop an efficient computational tetrolet transform, which can be used in lossless compression. For analysis purpose various wavelet techniques are used and result is compared with this techniques. From the analysis it is inferred that by proposed technique is able to achieve a high quality images with low noise.

Bit Plane Slicing, Wavelet and Polynomials Mixing for Image Compression

This paper introduced a hybrid technique for lossless image compression of natural and medical images; it is based on integrating the bit plane slicing and Wavelet transform along with a mixed polynomial of linear and non linear base. The experiments showed high compression performance with fully grunted reconstruction.

Software System for Efficient Lossless Compression of Medical Images

Software System for Efficient Lossless Compression of Medical Images

A novel resolution independent gradient edge predictor for lossless compression of medical image sequences

ABSTRACT Digital visualization of human body in terms of medical images with high resolution and bit depth generates tremendous amount of data. In the field of medical diagnosis, lossless compression technique is preferred that facilitates efficient archiving and transmission of medical images avoiding false diagnosis. Among various approaches to lossless compression of medical images, predictive coding techniques have high coding efficiency and low complexity. Gradient Edge Detector (GED) used in predictive coding is based on threshold value for prediction and choice of threshold is very important for efficient prediction. However, no specific method is adopted in the literature for threshold value selection. This paper presents an efficient prediction solution targeted at lossless compression of 8 bits and higher bit depth volumetric medical images up to 16 bits. Novelty of the proposed technique is developing Resolution Independent Gradient Edge Predictor (RIGED) algorithm to support 8- and 16-bit depth medical images. Percentage improvement of the proposed model is 30.39% over state-of-the-art Median Edge Detector (MED) and 0.92% over Gradient Adaptive Predictor (GAP) in terms of entropy for medical image dataset of different modalities having different resolutions and bit depths.

An Efficient Lossless ROI Image Compression Using Wavelet-Based Modified Region Growing Algorithm

Abstract Nowadays, medical imaging and telemedicine are increasingly being utilized on a huge scale. The expanding interest in storing and sending medical images brings a lack of adequate memory spaces and transmission bandwidth. To resolve these issues, compression was introduced. The main aim of lossless image compression is to improve accuracy, reduce the bit rate, and improve the compression efficiency for the storage and transmission of medical images while maintaining an acceptable image quality for diagnosis purposes. In this paper, we propose lossless medical image compression using wavelet transform and encoding method. Basically, the proposed image compression system comprises three modules: (i) segmentation, (ii) image compression, and (iii) image decompression. First, the input medical image is segmented into region of interest (ROI) and non-ROI using a modified region growing algorithm. Subsequently, the ROI is compressed by discrete cosine transform and set partitioning in hierarchical tree encoding method, and the non-ROI is compressed by discrete wavelet transform and merging-based Huffman encoding method. Finally, the compressed image combination of the compressed ROI and non-ROI is obtained. Then, in the decompression stage, the original medical image is extracted using the reverse procedure. The experimentation was carried out using different medical images, and the proposed method obtained better results compared to different other methods.