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ROI Based Encoding of Medical Images: An Effective Scheme Using Lifting Wavelets and SPIHT for Telemedicine
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... The proposed work allowed decrease in the background redundancy and encoding time. Kumar et al. (2011) applied ROI coding in telemedicine by using WT with lifting. The study used SPIHT. ...
The influence and impact of digital images on modern society, science, technology
and art are remarkable. Image processing has become such a critical component in
contemporary science and technology without that many tasks would not even be
attempted. Image analysis is one of the key components that have its implications
on use of digital images. It is a truly interdisciplinary subject that draws from
synergistic developments involving many disciplines and is used in medical imaging,
microscopy, computer vision, geology and many other fields.
Gaining high-level understanding from digital images is a key requirement for
computing. One aspect of study that is assisting with this advancement is fractal
theory. This new science has gained momentum and popularity as it has become
a key topic of research in the area of image analysis. This book has put thrust on
this vital area.
This is a text for use in a first practical course in image processing and analysis,
for final-year undergraduate or first-year post graduate students with a background
in biomedical engineering, computer science, radiologic sciences or physics.
Designed for readers who will become “end users” of digital image processing in
various domains, it emphasizes the conceptual framework and the effective use of
image processing tools and uses mathematics as a tool, minimizing the advanced
mathematical development of other textbooks.
Featuring research on topics such as image compression, pattern matching,
and artificial neural networks, this book is ideally designed for system engineers,
computer engineers, professionals, academicians, researchers, and students seeking
coverage on problem-oriented processing techniques and imaging technologies. The
book is an essential reference source that discusses fractal theory applications and
analysis, including box-counting analysis, multi-fractal analysis, 3D fractal analysis,
and chaos theory, as well as recent trends in other soft computing techniques.
... Data compression tries to reduce the size of the image by removing the spatial and structural redundancies. This eventually reduces the number of bits required to represent an image [2]. ...
This paper focuses on comparison of different wavelet coders such as SPIHT, SPECK, BISK and TARP for efficient storage and better transmission. Set partition methods like SPIHT, SPECK and BISK (variant of SPECK) are based on the popular bit-plane coding paradigm and gives excellent results for lossless compression. Tarp filtering is better for predicting images with wavelet coefficients. Performance of wavelet coders are evaluated in terms of peak signal noise ratio and bit rate for objective quality assessment of reconstructed image. Experiments on test images identified the optimal wavelet encoder combination. The test results show that Cohen-Daubechies-Feaveau 9/7 along with SPIHT encoder yields comparable compression efficiency over other methods.
... Andersen et al. focused on models pertaining to propagation measurements for performance improvement. Rajkumar and Latte [10] studied the need for Region of Interest (ROI) in video transmission for better rendering of video. Aegean et al. [11] focused on advanced coding for ROI. ...
... Data compression tries to reduce the size of the image by removing the spatial and structural redundancies. This eventually reduces the number of bits required to represent an image [2]. ...
This paper focuses on comparison of different wavelet coders such as SPIHT, SPECK, BISK and TARP for efficient storage and better transmission. Set partition methods like SPIHT, SPECK and BISK (variant of SPECK) are based on the popular bit-plane coding paradigm and gives excellent results for lossless compression. Tarp filtering is better for predicting images with wavelet coefficients. Performance of wavelet coders are evaluated in terms of peak signal noise ratio and bit rate for objective quality assessment of reconstructed image. Experiments on test images identified the optimal wavelet encoder combination. The test results show that Cohen-Daubechies-Feaveau 9/7 along with SPIHT encoder yields comparable compression efficiency over other methods.
Background
The videos produced during wireless capsule endoscopy have larger data size causing difficulty in transmission with limited bandwidth. The constraint on wireless capsule endoscopy hinders the performance of compression module.
Objectives
The objectives of this paper are as follows: (i) to have an extensive review on the lossless compression techniques and (ii) to find out the limitations of the existing system and the possibilities for improvement.
Method
The literature review has been done with a focus on the compression schemes satisfying minimum computational complexity, less power dissipation and low memory requirements for hardware implementation. A thorough study on various lossless compression techniques is done under two perspectives, i.e., techniques applied on Bayer CFA and RGB images. The details of the various stages of wireless capsule endoscopy compression are looked into to have a better understanding. The suitable performance metrics for evaluating the compression techniques are listed from various literatures.
Result
In addition to the Gastrolab database that is widely, WEO clinical endoscopy atlas and Gastrointestinal atlas found to be better alternatives for experimentation. Pre-processing operations, especially new subsampling patterns need to be given more focus to exploit the redundancies in the images. Investigations shows encoder module can be modified to bring more improvement towards compression. The real-time endoscopy still exists as a promising area for exploration.
Conclusion
This review presents a research update on the details of wireless capsule endoscopy compression together with the findings as an eye-opener and guidance for further research.
In this chapter, the performance of wavelet transform-based EZW coding and SPIHT coding technique have been evaluated and compared in terms of CR, PSNR, and MSE by applying them to similar color images in two standard resolutions. The application of these techniques on entire color images such as passport size photograph in which the region containing the face of a person is more significant than other regions results in equal loss of information content and less compression ratio. So, to achieve the high CRs and distribute the quality of the image unevenly, this chapter proposes the ROI coding technique. Compressing ROI portion using discrete wavelet transform with Huffman coding and NROI compressed with Huffman, EZW coding, SPIHT coding suggested effective compression at nearly no loss of quality in the ROI portion of the photograph. Further, higher CR and PSNR with lower MSE have been found in high-resolution photographs, thereby permitting the reduction of storage space, faster transmission on low bandwidth channels, and faster processing.
High resolution satellite imaging is considered as the outstanding applicant to extract the Earth’s surface information. Extraction of a feature of an image is very difficult due to having to find the appropriate image segmentation techniques and combine different methods to detect the Region of Interest (ROI) most effectively. This paper proposes techniques to classify objects in the satellite image by using image processing methods on high-resolution satellite images. The systems to identify the ROI focus on forests, urban and agriculture areas. The proposed system is based on histograms of the image to classify objects using thresholding. The thresholding is performed by considering the behaviour of the histogram mapping to a particular region in the satellite image. The proposed model is based on histogram segmentation and morphology techniques. There are five main steps supporting each other; Histogram classification, Histogram segmentation, Morphological dilation, Morphological fill image area and holes and ROI management. The methods to detect the ROI of the satellite images based on histogram classification have been studied, implemented and tested. The algorithm is be able to detect the area of forests, urban and agriculture separately. The image segmentation methods can detect the ROI and reduce the size of the original image by discarding the unnecessary parts.
High resolution satellite imaging is considered as the outstanding applicant to extract the Earth’s surface information. Extraction of a
feature of an image is very difficult due to having to find the appropriate image segmentation techniques and combine different
methods to detect the Region of Interest (ROI) most effectively. This paper proposes techniques to classify objects in the satellite
image by using image processing methods on high-resolution satellite images. The systems to identify the ROI focus on forests,
urban and agriculture areas. The proposed system is based on histograms of the image to classify objects using thresholding. The
thresholding is performed by considering the behaviour of the histogram mapping to a particular region in the satellite image. The
proposed model is based on histogram segmentation and morphology techniques. There are five main steps supporting each other;
Histogram classification, Histogram segmentation, Morphological dilation, Morphological fill image area and holes and ROI
management. The methods to detect the ROI of the satellite images based on histogram classification have been studied,
implemented and tested. The algorithm is be able to detect the area of forests, urban and agriculture separately. The image
segmentation methods can detect the ROI and reduce the size of the original image by discarding the unnecessary parts.
Imaging applications require processing, transmission, and storage of enormous digitized data produced from large sets of images. To conserve storage, increase the processing speed and to make their transmission faster on low bandwidth channels, images are compressed prior to their storage, transmission, and processing. Different approaches to compression have been developed to achieve desired degrees of compression ratio. Techniques that achieve highest degree of compression may reduce the size of image considerably but may also reduce the quality of image substantially. In certain types of images such as passport size photographs, loss of quality in the region of interest is highly undesirable; however, some loss of information in non-regions of interest can be accepted. For such images region of interest can be left uncompressed or compressed through lossless or near lossless algorithms. To achieve good quality of reconstruction of region of interest and better overall compression ratio, this paper proposes the use of multiple compression techniques for passport size photographs. It investigates through Matlab the application of different lossless compression techniques in terms of peak signal to noise ratio, compression ratio, and mean square error on region of interest together with lossy compression techniques on non-region of interests of passport size photographs to determine best possible compression technique and compression rations for both regions.
We presented a new compression scheme by using the adaptive Space-Frequency Segmentation (SFS) decomposition method as the basis for context-based spatial entropy coders. The context-based entropy techniques were applied in the lossy coding for natural images. The proposed compression scheme for adaptive wavelet coding is combined with spatial encoding. Results showed that the proposed technique gives significantly better image quality compared with other compression rates.
Image communication systems for medical images have bandwidth and image size constraints that result in time-consuming transmission of uncompressed raw image data. Thus image compression plays vital role to reduce the bit rate for transmission or storage while maintaining an acceptable reproduction quality, but it is natural to rise the question of how much an image can be compressed and still preserve sufficient information for a given clinical application. Evaluation of the quality of medical image compression still remains an important issue. Image quality has two implications: fidelity and intelligibility. Fidelity describes how the reconstructed image differs from the original one. Intelligibility shows the ability through which the image can offer information to people with classification accuracy. Many techniques for achieving data compression have been introduced. Wavelet transform techniques currently provide the most promising approach to high-quality image compression, which is essential for teleradiology. In this study, three different Computed Tomography images which are adrenal, chest and liver have been compressed and reconstructed using wavelet transform. Subjective and objective evaluation has been done to investigate the clinical information quality of the compressed images. For the objective evaluation, the results show that the PSNR which indicates the quality of the reconstructed image is ranging from (27.6dB to 36.7dB, 23.8dB to 31.8dB, and 23.9dB to 31.0dB) for adrenal, chest, and liver respectively. For the subjective evaluation test, the results show that the compression ratio of 30:1 was acceptable for chest and liver images, whereas for adrenal image 40:1 was acceptable.
We propose an integrated, wavelet based, two-stage coding scheme for lossy, near-lossless and lossless compression of medical volumetric data. The method presented determines the bit-rate while encoding for the lossy layer and without any iteration. It is in the spirit of "lossy plus residual coding" and consists of a wavelet-based lossy layer followed by an arithmetic coding of the quantized residual to guarantee a given pixel-wise maximum error bound. We focus on the selection of the optimum bit rate for the lossy coder to achieve the minimum total (lossy plus residual) bit rate in the near-lossless and the lossless cases. We propose a simple and practical method to estimate online the optimal bit rate and provide a theoretical justification for it. Experimental results show that the proposed scheme provides improved, embedded lossy, and lossless performance competitive with the best results published so far in the literature, with an added feature of near-lossless coding.
Because of storage, transmission bandwidth, picture archiving, communication constraints and the limitations of the conventional compression methods, the medical imagery need to be compressed selectively to reduce the transmission time and storage costs while maintaining high diagnostic image quality. The contextual medical image compression (MIC) provides high spatial resolution and contrast sensitivity requirements for the diagnostic purpose. In this context, a novel approach called context modeling of medical image compression based on wavelet transform has been proposed in this work. In medical images, contextual region is an area which contains the most useful information and must be coded carefully without appreciable distortion. The proposed context based method yields significantly better compression rates with better image quality defined in terms of image quality metrics (IQM) than the general methods of compression. The experimental results have been tested on several ultrasound medical images and the obtained results have been compared with results of the standard methods namely general scaling, Maxshift, Implicit and EBCOT of selective image coding. It has been found that the proposed algorithm shows better and improved results defined in terms of IQM performance based on subjective and objective analysis as compared to the standard methods.
Two different procedures are studied by which a rrequency analysis of a time-dependenl signal can be effected, locally in lime. The lirst procedure is the short-time or windowed Fourier transform, the second is the "wavelet transform," in which high frequency components are sludied wilh sharper time resolution than low frequency components. The similarities and the differences between these two methods are discussed. For both scbemes a detailed study is made of Ibe reconslruetion method and ils stability, as a function of the chosen time-frequency density. Finally the notion of "time-frequency localization" is made precise, within this framework, by two localization theorems.
This paper presents a novel procedure to improve the performance of a Wavelet based image compression in terms of entropy. All the subbands of wavelet decomposed image are quantized based on the energy of the subband. The quantized and rounded coefficients in the LL subband are re-ordered in a pre-determined manner. A prediction algorithm is used to reduce the entropy (bits/pixel). In addition, an oblivious image watermarking algorithm is proposed using compressed LL subband. The performance of the proposed watermarking algorithm is measured in terms of Peak Signal to Noise Ratio (PSNR) and Normalized Cross Correlation (NCC). Further, this algorithm is robust for various attacks including JPEG and JPEG2000 compression on watermarked image.
This work presents volumetric color medical image compression for the Picture Archiving and Communication Systems (PACS) using AT-SPIHT algorithm. PACS has inclined the medical information system by integrating RIS, HIS and all the imaging modalities and scanning devices to PACS so that doc- tor's use the PACS efficiently for diagnosis and decision making in petite time. PACS has the potential usage in telemedicine applications which is the future in medical field. With the above technology, enormous digital images are generated and stored in PACS for the doctor's diagnostic purpose where vo- lumetric color medical image compression is imperative and to utilize the PACS competently. The volume- tric color medical images are a three-dimensional (3-D) image data set, and can be considered as a se- quence of two- dimensional (2-D) slices. AT-SPIHT applied to the 3-D color Computed Tomography (CT) medical image is an efficient progressive coding technique. AT-SPIHT algorithm utilizes a more efficient spatial orientation tree structure. This novel technique enables the PACS implementation and deployment in medical information system.