Volumetric medical image compression using inter-slice correlation switched prediction approach

With the advancement in medical data acquisition and telemedicine systems, image compression has become an important tool for image handling, as the tremendous amount of data generated in medical field needs to be stored and transmitted effectively. Volumetric MRI and CT images comprise a set of image slices that are correlated to each other. The prediction of the pixels in a slice depends not only upon the spatial information of the slice, but also the inter-slice information to achieve compression. This article proposes an inter-slice correlation switched predictor (ICSP) with block adaptive arithmetic encoding (BAAE) technique for 3D medical image data. The proposed ICSP exploits both inter-slice and intra-slice redundancies from the volumetric images efficiently. Novelty of the proposed technique is in selecting the correlation coefficient threshold (T_ϒ) for switching of ICSP. Resolution independent gradient edge detector (RIGED) at optimal prediction threshold value is proposed for intra-slice prediction. Use of RIGED, which is modality and resolution independent, brings the novelty and improved performance for 3D prediction of volumetric images. BAAE is employed for encoding of prediction error image to resulting in higher compression efficiency. The proposed technique is also extended for higher bit depth volumetric medical images (16-bit depth) presenting significant compression gain of 3D images. The performance of the proposed technique is compared with the state-of-the art techniques in terms of bits per pixel (BPP) for 8-bit depth and was found to be 31.21%, 27.55%, 21.89%, and 2.39% better than the JPEG-2000, CALIC, JPEG-LS, M-CALIC, and 3D-CALIC respectively. The proposed technique is 11.86%, 8.56%, 7.97%, 6.80%, and 4.86% better than the M-CALIC, 3D CALIC, JPEG-2000, JPEG-LS and CALIC respectively for 16-bit depth image datasets. The average value of compression ratio for 8-bit and 16-bit image dataset is obtained as 3.70 and 3.11 respectively by the proposed technique.     

REPro.JPEG: a new image compression approach based on reduction/expansion image and JPEG compression for dermatological medical images

Medical images are known for their huge volume which becomes a real problem for their archiving or transmission notably for telemedicine applications. In this context, we present a new method for medical image compression which combines image definition resizing and JPEG compression. We baptise this new protocol REPro.JPEG (reduction/expansion protocol combined with JPEG compression). At first, the image is reduced then compressed before its archiving or transmission. At last, the user or the receiver decompresses the image then enlarges it before its display. The obtain results prove that, at the same number of bits per pixel lower than 0.42, that REPRo.JPEG guarantees a better preservation of image quality compared to the JPEG compression for dermatological medical images. Besides, applying the REPRo.JPEG on these colour medical images is more efficient while using the HSV colour space compared to the use of RGB or YCbCr colour spaces.     

Efficient image compression techniques for compressing multimodal medical images using neural network radial basis function approach

Image compression technique is used to reduce the number of bits required in representing image, which helps to reduce the storage space and transmission cost. Image compression techniques are widely used in many applications especially, medical field. Large amount of medical image sequences are available in various hospitals and medical organizations. Large images can be compressed into smaller size images, so that the memory occupation of the image is considerably reduced. Image compression techniques are used to reduce the number of pixels in the input image, which is also used to reduce the broadcast and transmission cost in efficient form. This is capable by compressing different types of medical images giving better compression ratio (CR), low mean square error (MSE), bits per pixel (BPP), high peak signal to noise ratio (PSNR), input image memory size and size of the compressed image, minimum memory requirement and computational time. The pixels and the other contents of the images are less variant during the compression process. This work outlines the different compression methods such as Huffman, fractal, neural network back propagation (NNBP) and neural network radial basis function (NNRBF) applied to medical images such as MR and CT images. Experimental results show that the NNRBF technique achieves a higher CR, BPP and PSNR, with less MSE on CT and MR images when compared with Huffman, fractal and NNBP techniques.     

Objective analysis and bit rate analysis of HEVC compressed 4D-fMRI images

Medical imaging and clinical diagnostics are complementary to one another since their analysis is typical and contains critical information. The growing volume of data has become one of the biggest challenges, as the acquisition of medical modalities is currently having high resolution from the improved and efficient machines (3 to 7 T or more). Moreover, image and video compression is a need with the consideration that there should not be any gap for losing the important information. Less bitrate requirement with high compression ratio without sacrificing important detail is a challenge these days. The current study, is dealing with the compression of 4D-functional medical resonance images (fMRI) with a codec, that is, high-efficient video coding (HEVC/H.265) and its objective analysis along with its predecessor that is advanced video coding (AVC/H.264) and with VP8 (WebM Project of Google) reported here. Further, the bit rate analysis that has been conducted, also accounts in conjunction with the bitrate investigation, which is an imperative perspective vital for the telemedicine field. The simulation results reported here represents the compression ratio (CR = 118.23:1) with HEVC/H.265 codec over the compression ratio (CR = 20.52:1) provided by AVC/H.264 and VP8 (CR = 78.29:1). There has been significant improvement observed in alignment of the peak signal-to-noise ratio (APSNR), structural similarity (SSIM), and mean squared error (MSE) metrics. Overall, the performance of the anticipated technique is satisfactory for the forthcoming telemedicine or clinical use.     

Enhanced brain image retrieval using carrier frequency offset compensated orthogonal frequency division multiplexing for telemedicine applications

Wireless technology plays a vital role in the Electronic health (e‐health) applications such as telemedicine and remote patient monitoring. One of the major challenges of telemedicine applications is ensuring adequate quality of service and realizing precise diagnosis. Telemedicine employs image fusion to enhance the quality of medical image for better diagnosis. However, the transmission of medical image needs high data rate and bandwidth. Due to the robustness to multipath fading, high data rate transmission capability, high flexibility, high bandwidth efficiency, and easy implementation using fast Fourier transform, an orthogonal frequency division multiplexing (OFDM) is the suitable technique for medical image transmission in telemedicine applications This paper is focused on improving the quality of the fused brain image transmission over OFDM based telemedicine service using comb type pilot based carrier frequency offset compensation. The simulation results show that the proposed method offers better performance in terms of mean squared error, Euclidean distance, peak signal to noise ratio, bit error rate, carrier to interference ratio, and computational complexity.     

Lossless color medical image compression using adaptive block‐based encoding for human computed tomographic images

The advancement in medical imaging systems such as computed tomography (CT), magnetic resonance imaging (MRI), positron emitted tomography (PET), and computed radiography (CR) produces huge amount of volumetric images about various anatomical structure of human body. There exists a need for lossless compression of these images for storage and communication purposes. The major issue in medical image is the sequence of operations to be performed for compression and decompression should not degrade the original quality of the image, it should be compressed loss lessly. In this article, we proposed a lossless method of volumetric medical image compression and decompression using adaptive block‐based encoding technique. The algorithm is tested for different sets of CT color images using Matlab. The Digital Imaging and Communications in Medicine (DICOM) images are compressed using the proposed algorithm and stored as DICOM formatted images. The inverse process of adaptive block‐based algorithm is used to reconstruct the original image information loss lessly from the compressed DICOM files. We present the simulation results for large set of human color CT images to produce a comparative analysis of the proposed methodology with block‐based compression, and JPEG2000 lossless image compression technique. This article finally proves the proposed methodology gives better compression ratio than block‐based coding and computationally better than JPEG 2000 coding. © 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 227–234, 2013     

Lossless compression-based progressive image transmission scheme

Abstract

The lossless compression of images is widely used in medical imaging and remote sensing applications. Also, progressive transmission of images is often desirable because it can reduce the transmission bits of an image. Therefore, combining the features of lossless compression and progressive transmission of images has been intensely researched. The bitplane method (BPM) is the simplest way to implement a lossless progressive image transmission system. In the present paper, a new block-based scheme for lossless progressive image transmission is proposed. This scheme will reduce the transmission load and improve the image quality of BPM. This method first performs a quantization operation upon the blocks of an image. Next, these blocks are encoded with fewer bits, and the bits are then transmitted phase by phase. The experimental results show that the image quality of this method is better than those in the BPM and improved BPM in related traditional works under the same transmission load. Moreover, during the first phase, the difference in peak signal-to-noise ratio between the present method and BPM is exactly equal up to 8.85 dB. This method is therefore effective for lossless progressive image transmission.     

Fast image vector quantization using a modified competitive learning neural network approach

The basic goal of image compression through vector quantization (VQ) is to reduce the bit rate for transmission or data storage while maintaining an acceptable fidelity or image quality. The advantage of VQ image compression is its fast decompression by table lookup technique. However, the codebook supplied in advance may not handle the changing image statistics very well. The need for online codebook generation became apparent. The competitive learning neural network design has been used for vector quantization. However, its training time can be very long, and the number of output nodes is somewhat arbitrarily decided before the training starts. Our modified approach presents a fast codebook generation procedure by searching for an optimal number of output nodes evolutively. The results on two medical images show that this new approach reduces the training time considerably and still maintains good quality for recovered images. © 1997 John Wiley & Sons, Inc. Int J Imaging Syst Technol, 8, 413–418, 1997     

A Robust Watermarking Scheme Based on ROI and IWT for Remote Consultation of COVID-19

In the current dire situation of the corona virus COVID-19, remote consultations were proposed to avoid cross-infection and regional differences in medical resources. However, the safety of digital medical imaging in remote consultations has also attracted more and more attention from the medical industry. To ensure the integrity and security of medical images, this paper proposes a robust watermarking algorithm to authenticate and recover from the distorted medical images based on regions of interest (ROI) and integer wavelet transform (IWT). First, the medical image is divided into two different parts, regions of interest and non-interest regions. Then the integrity of ROI is verified using the hash algorithm, and the recovery data of the ROI region is calculated at the same time. Also, binary images with the basic information of patients are processed by logistic chaotic map encryption, and then the synthetic watermark is embedded in the medical carrier image using IWT transform. The performance of the proposed algorithm is tested by the simulation experiments based on the MATLAB program in CT images of the lungs. Experimental results show that the algorithm can precisely locate the distorted areas of an image and recover the original ROI on the basis of verifying image reliability. The maximum peak signal to noise ratio (PSNR) value of 51.24 has been achieved, which proves that the watermark is invisible and has strong robustness against noise, compression, and filtering attacks.     

基于四叉树小波分形的医学图像压缩

文章提出了一种基于四叉树小波分形的医学图像压缩新算法。利用小波子带图像间的相似性和块内存在的自相似性进行分形编码。克服了传统分形编码复杂和编码时间过长的缺点，有效提高了编码效率。实验表明，此方法在无诊断失真的情况下可获得较好的压缩效果，在远程医疗和PACS系统等领域的医学图像压缩中有重要的潜在应用价值。     

Secure Watermarking Scheme for Color DICOM Images in Telemedicine Applications

Teleradiology plays a vital role in the medical field, which permits transmitting medical and imaging data over a communication network. It ensures data reliability and provides convenient communication for clinical interpretation and diagnostic purposes. The transmission of this medical data over a network raises the problems of legal, ethical issues, privacy, and copyright authenticity. The copyright protection of medical images is a significant issue in the medical field. Watermarking schemes are used to address these issues. A gray-level or binary image is used as a watermark frequently in color image watermarking schemes. In this paper, the authors propose a novel non-blind medical image watermarking scheme based on 2-D Lifting Wavelet Transform (LWT), Multiresolution Singular Value Decomposition (MSVD), and LU factorization to improve the robustness and authenticity of medical images. In this scheme, multiple color watermarks are embedded into the colored DICOM (Digital Imaging and Communications in Medicine) images obtained from Color Doppler images (DICOM format), and the average results achieved by our proposed scheme is 46.84 db for Peak Signal-to-Noise Ratio (PSNR), 37.46 db for Signal-to-Noise Ratio (SNR), 0.99 for Quality of Image and 0.998 for Normalized Correlation for various image processing attacks. These results make our watermarking technique an ideal candidate for medical image watermarking.     

An Optimal Lempel Ziv Markov Based Microarray Image Compression Algorithm

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.     

Progressive edge detection compression for fingerprint images

In this article, a progressive fingerprint image compression (for storage or transmission) that uses an edge detection scheme is developed. First, the image is decomposed into two components: the primary component, which contains the edges, and the secondary component, which contains the textures and the features. Then, a general grasp for the image is reconstructed in the first stage at bit rates of 0.0223 and 0.0245 bpp for the tested fingerprints images (samples 1 and 2), respectively. The quality of the reconstructed images is competitive with the 0.75‐bpp target bit set by the FBI standard. Also, the compression ratio and the image quality of this algorithm are competitive with other methods reported in the literature. The compression ratio for our algorithm is about 45:1 (0.180 bpp). © 2003 Wiley Periodicals, Inc. Int J Imaging Syst Technol 12, 211–216, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ima.10025     

Enhancing security of DICOM images during storage and transmission in distributed environment

Digital watermarking is proposed as a method to enhance medical data security. Medical image watermarking requires extreme care when embedding additional information within medical images, because the additional information should not degrade medical image quality. Doctors diagnose medical images by seeing Region of Interest (ROI). A ROI of a medical image is an area including important information and must be stored without any distortion. If a medical image is illegally obtained or if its content is changed, it may lead to wrong diagnosis. In our scheme a well-known technique least significant bit substitution (LSB) is adapted to fulfill the requirements of data hiding and authentication in medical images. The scheme divide Digital Imaging and Communications in Medicine (DICOM) image into two parts ROI and non-ROI (non-region of interest). The DS (Digital Signature) which include patient data and SOP Instance UID are embedded randomly into the LSB border of the image. The DWT (Discrete Wavelet Transform) of ROI-MSB (most significant bit) embedded into the LSB middle of the image. The Result shows the ability to hide and retrieve DS in non-ROI, while ROI, the most important area for diagnosis, is retrieved exactly at the receiver side.     

A hybrid contextual compression technique using wavelet and contourlet transforms with PSO optimized prediction

Contextual compression is an essential part of any medical image compression since it facilitates no loss of diagnostic information. Although there are many techniques available for contextual image compression still there is a need for developing an efficient and optimized technique which would produce good quality images at lower bit rates. This article presents an efficient contextual compression algorithm using wavelet and contourlet transforms to capture the fine details of the image, along with directional information to produce good quality at high Compression Ratio (CR). The 2D discrete wavelet transform, which uses the simplest Daubechies wavelets, db1, or haar wavelet, is chosen and used to get the subband coefficients. The approximate coefficients of the higher subbands undergo contourlet transform employing length N ladder filters for capturing the directional information of the subbands at different scale and orientations. An optimized approach is used for predicting the quantized and the normalized subband coefficients resulting in improved compression performance. The proposed contextual compression approach was evaluated for its performance in terms of CR, Peak Signal to Noise Ratio, Feature SIMilarity index, Structure SIMilarity Index, and Universal quality (Q) after reconstruction. The results clarify the efficiency of the proposed method over other compression techniques.     

Application of Image Compression to Multiple-Shot Pictures Using Similarity Norms With Three Level Blurring

Image compression is a process based on reducing the redundancy of the image to be stored or transmitted in an efficient form. In this work, a new idea is proposed, where we take advantage of the redundancy that appears in a group of images to be all compressed together, instead of compressing each image by itself. In our proposed technique, a classification process is applied, where the set of the input images are classified into groups based on existing technique like L₁ and L₂ norms, color histograms. All images that belong to the same group are compressed based on dividing the images of the same group into sub-images of equal sizes and saving the references into a codebook. In the process of extracting the different sub-images, we used the mean squared error for comparison and three blurring methods (simple, middle and majority blurring) to increase the compression ratio. Experiments show that varying blurring values, as well as MSE thresholds, enhanced the compression results in a group of images compared to JPEG and PNG compressors.     

Efficient colour image compression using fusion approach

The aim of image compression endeavour is to reduce the total data required to represent the image, which, in turn, decreases the demand of transmission bandwidth and storage space. In this work, we propose an image fusion based idea that can be exploited extensively to reduce the file size of JPEG compressed image further. Before performing the JPEG compression, we compute both intensity and a subsampled colour representation of the image undergoing compression. Then, similar to the JPEG compression, discrete cosine transformation, quantisation and entropy coding processes are applied on these images and stored in a single image file container. In the decoder, these two images are reconstructed and fused to obtain the resultant decoded image. Our experiments show that the proposed method does meet the lower storage and bandwidth requirement by reducing the average bits per pixel of the encoded image than that of the JPEG compressed image.     

Denoising Medical Images Using Deep Learning in IoT Environment

Medical Resonance Imaging (MRI) is a noninvasive, nonradioactive, and meticulous diagnostic modality capability in the field of medical imaging. However, the efficiency of MR image reconstruction is affected by its bulky image sets and slow process implementation. Therefore, to obtain a high-quality reconstructed image we presented a sparse aware noise removal technique that uses convolution neural network (SANR_CNN) for eliminating noise and improving the MR image reconstruction quality. The proposed noise removal or denoising technique adopts a fast CNN architecture that aids in training larger datasets with improved quality, and SARN algorithm is used for building a dictionary learning technique for denoising large image datasets. The proposed SANR_CNN model also preserves the details and edges in the image during reconstruction. An experiment was conducted to analyze the performance of SANR_CNN in a few existing models in regard with peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and mean squared error (MSE). The proposed SANR_CNN model achieved higher PSNR, SSIM, and MSE efficiency than the other noise removal techniques. The proposed architecture also provides transmission of these denoised medical images through secured IoT architecture.     

三维医学图像优化压缩算法

针对 CT 三维医学图像特点,将三维医学数据分块,通过平滑处理和边界交迭减小分块失真,将 SPIHT 算法与 SPECK 算法结合,帧内采用 SPIHT 算法,帧间利用 SPECK 编码算法,组合算法提高了压缩效率,优于单一的算法。     

Optimal multi-scale geometric fusion based on non-subsampled contourlet transform and modified central force optimization

In the current era of technological development, medical imaging plays an important part in several applications of medical diagnosis and therapy. This requires more precise images with much more details and information for correct medical diagnosis and therapy. Medical image fusion is one of the solutions for obtaining much spatial and spectral information in a single image. This article presents an optimization-based contourlet image fusion approach in addition to a comparative study for the performance of both multi-resolution and multi-scale geometric effects on fusion quality. An optimized multi-scale fusion technique based on the Non-Subsampled Contourlet Transform (NSCT) using the Modified Central Force Optimization (MCFO) and local contrast enhancement techniques is presented. The first step in the proposed fusion approach is the histogram matching of one of the images to the other to allow the same dynamic range for both images. The NSCT is used after that to decompose the images to be fused into their coefficients. The MCFO technique is used to determine the optimum decomposition level and the optimum gain parameters for the best fusion of coefficients based on certain constraints. Finally, an additional contrast enhancement process is applied on the fused image to enhance its visual quality and reinforce details. The proposed fusion framework is subjectively and objectively evaluated with different fusion quality metrics including average gradient, local contrast, standard deviation (STD), edge intensity, entropy, peak signal-to-noise ratio, Q ^ab/f, and processing time. Experimental results demonstrate that the proposed optimized NSCT medical image fusion approach based on the MCFO and histogram matching achieves a superior performance with higher image quality, average gradient, edge intensity, STD, better local contrast and entropy, a good quality factor, and much more details in images. These characteristics help for more accurate medical diagnosis in different medical applications.