232 research outputs found
Hybrid Region-based Image Compression Scheme for Mamograms and Ultrasound Images
The need for transmission and archive of mammograms and ultrasound Images has
dramatically increased in tele-healthcare applications. Such images require large
amount of' storage space which affect transmission speed. Therefore an effective
compression scheme is essential. Compression of these images. in general. laces a
great challenge to compromise between the higher compression ratio and the relevant
diagnostic information. Out of the many studied compression schemes. lossless
.
IPl. (i-
LS and lossy SPII IT are found to he the most efficient ones. JPEG-LS and SI'll IT are
chosen based on a comprehensive experimental study carried on a large number of
mammograms and ultrasound images of different sizes and texture. The lossless
schemes are evaluated based on the compression ratio and compression speed. The
distortion in the image quality which is introduced by lossy methods evaluated based
on objective criteria using Mean Square Error (MSE) and Peak signal to Noise Ratio
(PSNR). It is found that lossless compression can achieve a modest compression ratio
2: 1 - 4: 1. bossy compression schemes can achieve higher compression ratios than
lossless ones but at the price of the image quality which may impede diagnostic
conclusions. In this work, a new compression approach called Ilvbrid Region-based Image
Compression Scheme (IIYRICS) has been proposed for the mammograms and
ultrasound images to achieve higher compression ratios without compromising the
diagnostic quality. In I LYRICS, a modification for JPI; G-LS is introduced to encode
the arbitrary shaped disease affected regions. Then Shape adaptive SPIT IT is applied
on the remaining non region of interest. The results clearly show that this hybrid
strategy can yield high compression ratios with perfect reconstruction of diagnostic
relevant regions, achieving high speed transmission and less storage requirement. For
the sample images considered in our experiment, the compression ratio increases
approximately ten times. However, this increase depends upon the size of the region
of interest chosen. It is also föund that the pre-processing (contrast stretching) of
region of interest improves compression ratios on mammograms but not on ultrasound
images
Hybrid Region-based Image Compression Scheme for Mamograms and Ultrasound Images
The need for transmission and archive of mammograms and ultrasound Images has
dramatically increased in tele-healthcare applications. Such images require large
amount of' storage space which affect transmission speed. Therefore an effective
compression scheme is essential. Compression of these images. in general. laces a
great challenge to compromise between the higher compression ratio and the relevant
diagnostic information. Out of the many studied compression schemes. lossless
.
IPl. (i-
LS and lossy SPII IT are found to he the most efficient ones. JPEG-LS and SI'll IT are
chosen based on a comprehensive experimental study carried on a large number of
mammograms and ultrasound images of different sizes and texture. The lossless
schemes are evaluated based on the compression ratio and compression speed. The
distortion in the image quality which is introduced by lossy methods evaluated based
on objective criteria using Mean Square Error (MSE) and Peak signal to Noise Ratio
(PSNR). It is found that lossless compression can achieve a modest compression ratio
2: 1 - 4: 1. bossy compression schemes can achieve higher compression ratios than
lossless ones but at the price of the image quality which may impede diagnostic
conclusions. In this work, a new compression approach called Ilvbrid Region-based Image
Compression Scheme (IIYRICS) has been proposed for the mammograms and
ultrasound images to achieve higher compression ratios without compromising the
diagnostic quality. In I LYRICS, a modification for JPI; G-LS is introduced to encode
the arbitrary shaped disease affected regions. Then Shape adaptive SPIT IT is applied
on the remaining non region of interest. The results clearly show that this hybrid
strategy can yield high compression ratios with perfect reconstruction of diagnostic
relevant regions, achieving high speed transmission and less storage requirement. For
the sample images considered in our experiment, the compression ratio increases
approximately ten times. However, this increase depends upon the size of the region
of interest chosen. It is also föund that the pre-processing (contrast stretching) of
region of interest improves compression ratios on mammograms but not on ultrasound
images
Image Compression Techniques: A Survey in Lossless and Lossy algorithms
The bandwidth of the communication networks has been increased continuously as results of technological advances. However, the introduction of new services and the expansion of the existing ones have resulted in even higher demand for the bandwidth. This explains the many efforts currently being invested in the area of data compression. The primary goal of these works is to develop techniques of coding information sources such as speech, image and video to reduce the number of bits required to represent a source without significantly degrading its quality. With the large increase in the generation of digital image data, there has been a correspondingly large increase in research activity in the field of image compression. The goal is to represent an image in the fewest number of bits without losing the essential information content within. Images carry three main type of information: redundant, irrelevant, and useful. Redundant information is the deterministic part of the information, which can be reproduced without loss from other information contained in the image. Irrelevant information is the part of information that has enormous details, which are beyond the limit of perceptual significance (i.e., psychovisual redundancy). Useful information, on the other hand, is the part of information, which is neither redundant nor irrelevant. Human usually observes decompressed images. Therefore, their fidelities are subject to the capabilities and limitations of the Human Visual System. This paper provides a survey on various image compression techniques, their limitations, compression rates and highlights current research in medical image compression
Proceedings of the 35th WIC Symposium on Information Theory in the Benelux and the 4th joint WIC/IEEE Symposium on Information Theory and Signal Processing in the Benelux, Eindhoven, the Netherlands May 12-13, 2014
Compressive sensing (CS) as an approach for data acquisition has recently received much attention. In CS, the signal recovery problem from the observed data requires the solution of a sparse vector from an underdetermined system of equations. The underlying sparse signal recovery problem is quite general with many applications and is the focus of this talk. The main emphasis will be on Bayesian approaches for sparse signal recovery. We will examine sparse priors such as the super-Gaussian and student-t priors and appropriate MAP estimation methods. In particular, re-weighted l2 and re-weighted l1 methods developed to solve the optimization problem will be discussed. The talk will also examine a hierarchical Bayesian framework and then study in detail an empirical Bayesian method, the Sparse Bayesian Learning (SBL) method. If time permits, we will also discuss Bayesian methods for sparse recovery problems with structure; Intra-vector correlation in the context of the block sparse model and inter-vector correlation in the context of the multiple measurement vector problem
Proceedings of the 35th WIC Symposium on Information Theory in the Benelux and the 4th joint WIC/IEEE Symposium on Information Theory and Signal Processing in the Benelux, Eindhoven, the Netherlands May 12-13, 2014
Compressive sensing (CS) as an approach for data acquisition has recently received much attention. In CS, the signal recovery problem from the observed data requires the solution of a sparse vector from an underdetermined system of equations. The underlying sparse signal recovery problem is quite general with many applications and is the focus of this talk. The main emphasis will be on Bayesian approaches for sparse signal recovery. We will examine sparse priors such as the super-Gaussian and student-t priors and appropriate MAP estimation methods. In particular, re-weighted l2 and re-weighted l1 methods developed to solve the optimization problem will be discussed. The talk will also examine a hierarchical Bayesian framework and then study in detail an empirical Bayesian method, the Sparse Bayesian Learning (SBL) method. If time permits, we will also discuss Bayesian methods for sparse recovery problems with structure; Intra-vector correlation in the context of the block sparse model and inter-vector correlation in the context of the multiple measurement vector problem
High-performance compression of visual information - A tutorial review - Part I : Still Pictures
Digital images have become an important source of information in the modern world of communication systems. In their raw form, digital images require a tremendous amount of memory. Many research efforts have been devoted to the problem of image compression in the last two decades. Two different compression categories must be distinguished: lossless and lossy. Lossless compression is achieved if no distortion is introduced in the coded image. Applications requiring this type of compression include medical imaging and satellite photography. For applications such as video telephony or multimedia applications, some loss of information is usually tolerated in exchange for a high compression ratio. In this two-part paper, the major building blocks of image coding schemes are overviewed. Part I covers still image coding, and Part II covers motion picture sequences. In this first part, still image coding schemes have been classified into predictive, block transform, and multiresolution approaches. Predictive methods are suited to lossless and low-compression applications. Transform-based coding schemes achieve higher compression ratios for lossy compression but suffer from blocking artifacts at high-compression ratios. Multiresolution approaches are suited for lossy as well for lossless compression. At lossy high-compression ratios, the typical artifact visible in the reconstructed images is the ringing effect. New applications in a multimedia environment drove the need for new functionalities of the image coding schemes. For that purpose, second-generation coding techniques segment the image into semantically meaningful parts. Therefore, parts of these methods have been adapted to work for arbitrarily shaped regions. In order to add another functionality, such as progressive transmission of the information, specific quantization algorithms must be defined. A final step in the compression scheme is achieved by the codeword assignment. Finally, coding results are presented which compare stateof- the-art techniques for lossy and lossless compression. The different artifacts of each technique are highlighted and discussed. Also, the possibility of progressive transmission is illustrated
Medical imaging analysis with artificial neural networks
Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging
Perceptually lossless coding of medical images - from abstraction to reality
This work explores a novel vision model based coding approach to encode medical images at a perceptually lossless quality, within the framework of the JPEG 2000 coding engine. Perceptually lossless encoding offers the best of both worlds, delivering images free of visual distortions and at the same time providing significantly greater compression ratio gains over its information lossless counterparts. This is achieved through a visual pruning function, embedded with an advanced model of the human visual system to accurately identify and to efficiently remove visually irrelevant/insignificant information. In addition, it maintains bit-stream compliance with the JPEG 2000 coding framework and subsequently is compliant with the Digital Communications in Medicine standard (DICOM). Equally, the pruning function is applicable to other Discrete Wavelet Transform based image coders, e.g., The Set Partitioning in Hierarchical Trees. Further significant coding gains are exploited through an artificial edge segmentatio n algorithm and a novel arithmetic pruning algorithm. The coding effectiveness and qualitative consistency of the algorithm is evaluated through a double-blind subjective assessment with 31 medical experts, performed using a novel 2-staged forced choice assessment that was devised for medical experts, offering the benefits of greater robustness and accuracy in measuring subjective responses. The assessment showed that no differences of statistical significance were perceivable between the original images and the images encoded by the proposed coder
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