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。     

Binary medical image compression using the volumetric run-length approach

Image compression has become an inevitable tool along with the advancing medical data acquisition and telemedicine systems. The run-length encoding (RLE), one of the most effective and practical lossless compression techniques, is widely used in two-dimensional space with common scanning forms such as zigzag and linear. In this study, an algorithm which takes advantage of the potential simplicity of the run-length algorithm is devised in a volumetric approach for three-dimensional (3D) binary medical data. The proposed algorithm, namely 3D-RLE, being different from the two-dimensional approach that utilizes only intra-slice correlations, is designed to compress binary volumetric data by employing also the inter-slice correlation between the voxels. Furthermore, it is extended to several scanning forms such as Hilbert and perimeter to determine an optimal scanning procedure coherent with the morphology of the segmented organ in data. The algorithm is employed on four datasets for a comprehensive assessment. Numerical simulation results demonstrated that the performance of the algorithm is 1:30 better than those of the state-of-the-art techniques, on average.     

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     

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.     

3D multiwavelet based block coding algorithm for compression of volumetric medical images

Three dimensional (3D) medical images possess some specific characteristics that should be utilized by an efficient compression scheme. In this article, one such compression scheme for volumetric 3D medical image data is presented. Two processes involved in this scheme are decorrelation and encoding. Decorrelation of the 3D data is realized through 3D multiwavelet transform with apt prefiltering so as to give good representation of the image which could be exploited by the encoder. Encoding is done through proposed Block Coding Algorithm, which is embedded, block based, and wavelet transform coding algorithm without maintaining any list structures. The idea behind this algorithm is to sort the 3D transform coefficients in to a 1D array with respect to declining thresholds and to use state table to keep track of the blocks and coefficients that has been coded. In the experiment conducted on various 3D magnetic resonance and computed tomography images of human brain with multiwavelets such as Geronimo–Hardin–Massopust, Chui‐Lian, and orthogonal symmetric/antisymmetric (SA4), efficiency of the proposed scheme was weighed against the state of art encoders such as 3D Set Partitioning in Hierarchical Trees, 2D Set Partitioned Embedded BloCK Coder, and No List SPIHT. Attributes used for performance measurements are peak signal to noise ratio, bit rate, and structural similarity index of reconstructed image with respect to original image. © 2014 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 24, 182–192, 2014     

Three-dimensional range data compression using computer graphics rendering pipeline

This paper presents the idea of naturally encoding three-dimensional (3D) range data into regular two-dimensional (2D) images utilizing computer graphics rendering pipeline. The computer graphics pipeline provides a means to sample 3D geometry data into regular 2D images, and also to retrieve the depth information for each sampled pixel. The depth information for each pixel is further encoded into red, green, and blue color channels of regular 2D images. The 2D images can further be compressed with existing 2D image compression techniques. By this novel means, 3D geometry data obtained by 3D range scanners can be instantaneously compressed into 2D images, providing a novel way of storing 3D range data into its 2D counterparts. We will present experimental results to verify the performance of this proposed technique.     

A Triple-Channel Encrypted Hybrid Fusion Technique to Improve Security of Medical Images

Assuring medical images protection and robustness is a compulsory necessity nowadays. In this paper, a novel technique is proposed that fuses the wavelet-induced multi-resolution decomposition of the Discrete Wavelet Transform (DWT) with the energy compaction of the Discrete Wavelet Transform (DCT). The multi-level Encryption-based Hybrid Fusion Technique (EbhFT) aims to achieve great advances in terms of imperceptibility and security of medical images. A DWT disintegrated sub-band of a cover image is reformed simultaneously using the DCT transform. Afterwards, a 64-bit hex key is employed to encrypt the host image as well as participate in the second key creation process to encode the watermark. Lastly, a PN-sequence key is formed along with a supplementary key in the third layer of the EbHFT. Thus, the watermarked image is generated by enclosing both keys into DWT and DCT coefficients. The fusions ability of the proposed EbHFT technique makes the best use of the distinct privileges of using both DWT and DCT methods. In order to validate the proposed technique, a standard dataset of medical images is used. Simulation results show higher performance of the visual quality (i.e., 57.65) for the watermarked forms of all types of medical images. In addition, EbHFT robustness outperforms an existing scheme tested for the same dataset in terms of Normalized Correlation (NC). Finally, extra protection for digital images from against illegal replicating and unapproved tampering using the proposed technique.     

Optimal Mode Decision Method for Interframe Prediction in H.264/AVC

Studies show that encoding technologies in H.264/AVC, including prediction and conversion, are essential technologies. However, these technologies are more complicated than the MPEG-4, which is a standard method and widely adopted worldwide. Therefore, the amount of calculation in H.264/AVC is significantly up-regulated compared to that of the MPEG-4. In the present study, it is intended to simplify the computational expenses in the international standard compression coding system H.264/AVC for moving images. Inter prediction refers to the most feasible compression technology, taking up to 60% of the entire encoding. In this regard, prediction error and motion vector information are proposed to simplify the computation of inter predictive coding technology. In the initial frame, motion compensation is performed in all target modes and then basic information is collected and analyzed. After the initial frame, motion compensation is performed only in the middle 8×8 modes, and the basic information amount shifts. In order to evaluate the effectiveness of the proposed method and assess the motion image compression coding, four types of motion images, defined by the international telecommunication union (ITU), are employed. Based on the obtained results, it is concluded that the developed method is capable of simplifying the calculation, while it is slightly affected by the inferior image quality and the amount of information.     

Ray-space数据片内和片间相关性压缩分析

Ray-space数据特征分析表明,其片内和片间均存在很大相关性,并且其数据有明显的方向性特征。通过最优宏块分割、不同精度的块匹配和多参考片预测结果分析,讨论了适合Ray-space数据特点的片内和片间预测技术。与传统的只利用片内相关性的压缩技术相比,利用上述两类相关性,并结合Ray-space数据方向性特征,能进一步提高编码压缩效率。在获得相同重建图像质量下,可使基于H.264的Ray-space压缩方法比基于H.263的方法在码率上节省50%,比JPEG的方法节省75%左右。     

Compression of interference patterns with application to phase-shifting digital holography

A compression method of phase-shifting digital holographic data is presented. Three interference patterns are recorded, and holographic information is extracted from them by phase-shifting interferometry. The scheme uses standard baseline Joint Photographic Experts Group (JPEG) or standard JPEG-2000 image compression techniques on the recorded interference patterns to reduce the amount of data to be stored. High compression rates are achieved for good reconstructed object image quality. The utility of the proposed method is experimentally verified with real holographic data. Results for compression rates using JPEG-2000 and JPEG of approximately 27 and 20, respectively, for a normalized root-mean-square error of approximately 0.7 are demonstrated.     

Channelized Hotelling observers for the assessment of volumetric imaging data sets

Current clinical practice is rapidly moving in the direction of volumetric imaging. For two-dimensional (2D) images, task-based medical image quality is often assessed using numerical model observers. For three-dimensional (3D) images, however, these models have been little explored so far. In this work, first, two novel designs of a multislice channelized Hotelling observer (CHO) are proposed for the task of detecting 3D signals in 3D images. The novel designs are then compared and evaluated in a simulation study with five different CHO designs: a single-slice model, three multislice models, and a volumetric model. Four different random background statistics are considered, both gaussian (noncorrelated and correlated gaussian noise) and non-gaussian (lumpy and clustered lumpy backgrounds). Overall, the results show that the volumetric model outperforms the others, while the disparity between the models decreases for greater complexity of the detection task. Among the multislice models, the second proposed CHO could most closely approach the volumetric model, whereas the first new CHO seems to be least affected by the number of training samples.     

Depth extraction of three-dimensional objects in space by the computational integral imaging reconstruction technique

A novel approach to extract the depth data of 3D objects in space by using the computational integral imaging reconstruction (CIIR) technique is proposed. With elemental images of 3D objects captured by the CCD camera through a pinhole array, depth-dependent object images can be reconstructed on the output plane by the CIIR technique. Only the images reconstructed on the output planes where 3D objects were located are clearly focused; so the depth data of 3D objects in space can be extracted by discriminating these focused output images from the others by using an image separation technique. A feasibility test of the proposed CIIR-based depth extraction method is carried out, and its results are discussed as well.     

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.     

Lossless and lossy image compression using boolean function minimization

A novel approach for lossless as well as lossy compression of monochrome images using Boolean minimization is proposed. The image is split into bit planes. Each bit plane is divided into windows or blocks of variable size. Each block is transformed into a Boolean switching function in cubical form, treating the pixel values as output of the function. Compression is performed by minimizing these switching functions using ESPRESSO, a cube based two level function minimizer. The minimized cubes are encoded using a code set which satisfies the prefix property. Our technique of lossless compression involves linear prediction as a preprocessing step and has compression ratio comparable to that of JPEG lossless compression technique. Our lossy compression technique involves reducing the number of bit planes as a preprocessing step which incurs minimal loss in the information of the image. The bit planes that remain after preprocessing are compressed using our lossless compression technique based on Boolean minimization. Qualitatively one cannot visually distinguish between the original image and the lossy image and the value of mean square error is kept low. For mean square error value close to that of JPEG lossy compression technique, our method gives better compression ratio. The compression scheme is relatively slower while the decompression time is comparable to that of JPEG.     

Radiometric order preserving method to display wide-dynamic images for imagery photointerpretation

Wide-dynamic numerical images are increasingly frequent in professional environments, military photointerpretation, and x-ray or magnetic resonance medical imagery. However, a dynamic compression process is necessary to exploit such images without incessant image manipulation. A wealth of efficient methods has been developed to tackle this problem on aesthetic grounds. We argue that professional imagery interpretation needs preservation of the original radiometric order. We develop a measure of image efficiency use of the 8-bit radiometric channel and find that it correlates nicely with subjective appraisal. The image underscores a radiometric order-preserving process to reach a standard radiometric efficiency. Lost information is then reintroduced by addition of an edge image devoid of artifacts, with an automatic weighting ensuring a natural-looking image.     

Efficient storage and transmission of ladar imagery

We develop novel methods for compressing volumetric imagery that has been generated by single-platform (mobile) range sensors. We exploit the correlation structure inherent in multiple views in order to improve compression efficiency. We show that, for lossless compression, three-dimensional volumes compress more efficiently than two-dimensional (2D) images by a factor of 60%. Furthermore, our error metric for lossy compression suggests that accumulating more than nine range images in one volume before compression yields as much as a 99% improvement in compression performance over 2D compression.     

Digitized holography: modern holography for 3D imaging of virtual and real objects

Recent developments in computer algorithms, image sensors, and microfabrication technologies make it possible to digitize the whole process of classical holography. This technique, referred to as digitized holography, allows us to create fine spatial three-dimensional (3D) images composed of virtual and real objects. In the technique, the wave field of real objects is captured in a wide area and at very high resolution using the technique of synthetic aperture digital holography. The captured field is incorporated in virtual 3D scenes including two-dimensional digital images and 3D polygon mesh objects. The synthetic field is optically reconstructed using the technique of computer-generated holograms. The reconstructed 3D images present all depth cues like classical holograms but are digitally editable, archivable, and transmittable unlike classical holograms. The synthetic hologram printed by a laser lithography system has a wide viewing zone in full-parallax and give viewers a strong sensation of depth, which has never been achieved by conventional 3D systems. A real hologram as well as the details of the technique is presented to verify the proposed technique.     

Colour image coding scheme using absolute moment block truncation coding and block prediction technique

Abstract

A predictive colour image compression scheme based on absolute moment block truncation coding is proposed. In this scheme, the high correlations among neighbouring image blocks are exploited by using the similar block prediction technique. In addition, the bit plane omission technique and the coding of quantisation levels are used to cut down the storage cost of smooth blocks and complex blocks respectively. According to the experimental results, the proposed scheme provides better performance than the comparative schemes based on block truncation coding. It provides better image qualities of compressed images at low bit rates. Meanwhile, it consumes very little computational cost. In other words, the proposed scheme is quite suitable for real time multimedia applications.     

Automatic threshold technique for holographic particle field characterization

The 3D distribution of a particle field by digital holography is obtained by 3D numerical reconstruction of a 2D hologram. The proper identification of particles from the background during numerical reconstruction influences the overall effectiveness of the technique. The selection of a suitable threshold value to segment particles from the background of reconstructed images during 3D holographic reconstruction process is a critical issue, which influences the accuracy of particle size and number density of reconstructed particles. The object particle field parameters, such as depth of sample volume and density of object particles, influence the optimal threshold value. The present study proposes a novel technique for the determination of the optimal threshold value of a reconstructed image. The effectiveness of the proposed technique is demonstrated using both simulated and experimental data. The proposed technique is robust to variation in optical properties of particle and background, depth of sample volume, and number density of object particle field. The particle diameter obtained from the proposed threshold technique is within 5% of that obtained from the particle size analyzer. There is a maximum ten times increase in reconstruction effectiveness by using the proposed automatic threshold technique in comparison with the fixed manual threshold technique.     

Reversible Data Hiding in Encrypted Images Based on Prediction and Adaptive Classification Scrambling

Reversible data hiding in encrypted images (RDH-EI) technology is widely used in cloud storage for image privacy protection. In order to improve the embedding capacity of the RDH-EI algorithm and the security of the encrypted images, we proposed a reversible data hiding algorithm for encrypted images based on prediction and adaptive classification scrambling. First, the prediction error image is obtained by a novel prediction method before encryption. Then, the image pixel values are divided into two categories by the threshold range, which is selected adaptively according to the image content. Multiple high-significant bits of pixels within the threshold range are used for embedding data and pixel values outside the threshold range remain unchanged. The optimal threshold selected adaptively ensures the maximum embedding capacity of the algorithm. Moreover, the security of encrypted images can be improved by the combination of XOR encryption and classification scrambling encryption since the embedded data is independent of the pixel position. Experiment results demonstrate that the proposed method has higher embedding capacity compared with the current state-ofthe-art methods for images with different texture complexity.