Lossless compression methods for magnetic resonance imaging using wavelet transform a systematic review

En medicina la información de las imágenes diagnósticas es vital e imprescindible, por este motivo es necesario procesarlas sin que existan márgenes de error que interfieran con su lectura y análisis. En términos generales: las imágenes presentan redundancia entre píxeles lo cual hace que ocupen un tamaño considerable que va desde los Megabytes (MB) hasta los Gigabytes (GB); el proceso de transmitirlas a través de la red se dificulta en términos de almacenamiento y coste por ende se deben aplicar procesos de compresión sin pérdidas útiles para reducir el ancho de banda, mejorar la capacidad de almacenamiento e incrementar la velocidad de transmisión sin afectar la calidad de la imagen diagnóstica.In medicine, the information from diagnostic images is vital and essential, for this reason, it’s necessary to process them without error margins that could interfere with their reading and analysis. In general terms: images present redundancy between pixels causing them occupy a considerable size ranging from Megabytes (MB) to Gigabytes (GB); the process of transmit them through the network is difficult in terms of storage and computational cost, therefore lossless compression processes must be applied to reduce bandwidth, improve storage capacity and increase transmission speed without affecting the quality of the diagnostic image

Efficient Lossless Coding Of Medical Image Volumes Using Reversible Integer Wavelet Transforms

A novel lossless medical image compression algorithm based on three-dimensional integer wavelet transforms and zerotree coding is presented. The EZW algorithm is extended to three dimensions and context-based adaptive arithmetic coding is used to improve its performance. The algorithm (3-D CB-EZW) efficiently encodes image volumes by exploitingthe dependencies in all three dimensions, while enabling lossy and lossless compression from the same bitstream. Results on lossless compression of CT and MR images are presented, and compared to other lossless compression algorithms. The progressive performance of the 3-D CB-EZW algorithm is also compared to other lossy progressive coding algorithms. For representative images, the 3-D CB-EZW algorithm produced an average of 14% and 20% decrease in compressed file sizes for CT and MR images, respectively, compared to the best available 2-D lossless compression techniques. 1. INTRODUCTION An increasing number of medical radiology images are crea..

Lossless and low-cost integer-based lifting wavelet transform

Discrete wavelet transform (DWT) is a powerful tool for analyzing real-time signals, including aperiodic, irregular, noisy, and transient data, because of its capability to explore signals in both the frequency- and time-domain in different resolutions. For this reason, they are used extensively in a wide number of applications in image and signal processing. Despite the wide usage, the implementation of the wavelet transform is usually lossy or computationally complex, and it requires expensive hardware. However, in many applications, such as medical diagnosis, reversible data-hiding, and critical satellite data, lossless implementation of the wavelet transform is desirable. It is also important to have more hardware-friendly implementations due to its recent inclusion in signal processing modules in system-on-chips (SoCs). To address the need, this research work provides a generalized implementation of a wavelet transform using an integer-based lifting method to produce lossless and low-cost architecture while maintaining the performance close to the original wavelets. In order to achieve a general implementation method for all orthogonal and biorthogonal wavelets, the Daubechies wavelet family has been utilized at first since it is one of the most widely used wavelets and based on a systematic method of construction of compact support orthogonal wavelets. Though the first two phases of this work are for Daubechies wavelets, they can be generalized in order to apply to other wavelets as well. Subsequently, some techniques used in the primary works have been adopted and the critical issues for achieving general lossless implementation have solved to propose a general lossless method. The research work presented here can be divided into several phases. In the first phase, low-cost architectures of the Daubechies-4 (D4) and Daubechies-6 (D6) wavelets have been derived by applying the integer-polynomial mapping. A lifting architecture has been used which reduces the cost by a half compared to the conventional convolution-based approach. The application of integer-polynomial mapping (IPM) of the polynomial filter coefficient with a floating-point value further decreases the complexity and reduces the loss in signal reconstruction. Also, the “resource sharing” between lifting steps results in a further reduction in implementation costs and near-lossless data reconstruction. In the second phase, a completely lossless or error-free architecture has been proposed for the Daubechies-8 (D8) wavelet. Several lifting variants have been derived for the same wavelet, the integer mapping has been applied, and the best variant is determined in terms of performance, using entropy and transform coding gain. Then a theory has been derived regarding the impact of scaling steps on the transform coding gain (GT). The approach results in the lowest cost lossless architecture of the D8 in the literature, to the best of our knowledge. The proposed approach may be applied to other orthogonal wavelets, including biorthogonal ones to achieve higher performance. In the final phase, a general algorithm has been proposed to implement the original filter coefficients expressed by a polyphase matrix into a more efficient lifting structure. This is done by using modified factorization, so that the factorized polyphase matrix does not include the lossy scaling step like the conventional lifting method. This general technique has been applied on some widely used orthogonal and biorthogonal wavelets and its advantages have been discussed. Since the discrete wavelet transform is used in a vast number of applications, the proposed algorithms can be utilized in those cases to achieve lossless, low-cost, and hardware-friendly architectures

Compressão sem perdas de projeções de tomografia computadorizada usando a transformada Wavelet

Orientador: Eduardo Parente RibeiroDissertação (mestrado) - Universidade Federal do Paran

Compression and protection of multidimensional data

2013 - 2014The main objective of this thesis is to explore and discuss novel techniques related to the compression and protection of multidimensional data (i.e., 3-D medical images, hyperspectral images, 3-D microscopy images and 5-D functional Magnetic Resonance Images). First, we outline a lossless compression scheme based on the predictive model, denoted as Medical Images Lossless Compression algorithm (MILC). MILC is characterized to provide a good trade-off between the compression performances and reduced usage of the hardware resources. Since in the medical and medical-related fields, the execution speed of an algorithm, could be a “critical” parameter, we investigate the parallelization of the compression strategy of the MILC algorithm, which is denoted as Parallel MILC. Parallel MILC can be executed on heterogeneous devices (i.e., CPUs, GPUs, etc.) and provides significant results in terms of speedup with respect to the MILC. This is followed by the important aspects related to the protection of two sensitive typologies of multidimensional data: 3-D medical images and 3-D microscopy images. Regarding the protection of 3-D medical images, we outline a novel hybrid approach, which allows for the efficient compression of 3-D medical images as well as the embedding of a digital watermark, at the same time. In relation to the protection of 3-D microscopy images, the simultaneous embedding of two watermarks is explained. It should be noted that 3-D microscopy images are often used in delicate tasks (i.e., forensic analysis, etc.). Subsequently, we review a novel predictive structure that is appropriate for the lossless compression of different typologies of multidimensional data... [edited by Author]XIII n.s

Distortion-constraint compression of three-dimensional CLSM images using image pyramid and vector quantization

The confocal microscopy imaging techniques, which allow optical sectioning, have been successfully exploited in biomedical studies. Biomedical scientists can benefit from more realistic visualization and much more accurate diagnosis by processing and analysing on a three-dimensional image data. The lack of efficient image compression standards makes such large volumetric image data slow to transfer over limited bandwidth networks. It also imposes large storage space requirements and high cost in archiving and maintenance. Conventional two-dimensional image coders do not take into account inter-frame correlations in three-dimensional image data. The standard multi-frame coders, like video coders, although they have good performance in capturing motion information, are not efficiently designed for coding multiple frames representing a stack of optical planes of a real object. Therefore a real three-dimensional image compression approach should be investigated. Moreover the reconstructed image quality is a very important concern in compressing medical images, because it could be directly related to the diagnosis accuracy. Most of the state-of-the-arts methods are based on transform coding, for instance JPEG is based on discrete-cosine-transform CDCT) and JPEG2000 is based on discrete- wavelet-transform (DWT). However in DCT and DWT methods, the control of the reconstructed image quality is inconvenient, involving considerable costs in computation, since they are fundamentally rate-parameterized methods rather than distortion-parameterized methods. Therefore it is very desirable to develop a transform-based distortion-parameterized compression method, which is expected to have high coding performance and also able to conveniently and accurately control the final distortion according to the user specified quality requirement. This thesis describes our work in developing a distortion-constraint three-dimensional image compression approach, using vector quantization techniques combined with image pyramid structures. We are expecting our method to have: 1. High coding performance in compressing three-dimensional microscopic image data, compared to the state-of-the-art three-dimensional image coders and other standardized two-dimensional image coders and video coders. 2. Distortion-control capability, which is a very desirable feature in medical 2. Distortion-control capability, which is a very desirable feature in medical image compression applications, is superior to the rate-parameterized methods in achieving a user specified quality requirement. The result is a three-dimensional image compression method, which has outstanding compression performance, measured objectively, for volumetric microscopic images. The distortion-constraint feature, by which users can expect to achieve a target image quality rather than the compressed file size, offers more flexible control of the reconstructed image quality than its rate-constraint counterparts in medical image applications. Additionally, it effectively reduces the artifacts presented in other approaches at low bit rates and also attenuates noise in the pre-compressed images. Furthermore, its advantages in progressive transmission and fast decoding make it suitable for bandwidth limited tele-communications and web-based image browsing applications