A review on region of interest-based hybrid medical image compression algorithms

Digital medical images have become a vital resource that supports decision-making and treatment procedures in healthcare facilities. The medical image consumes large sizes of memory, and the size keeps on growth due to the trend of medical image technology. The technology of telemedicine encourages the medical practitioner to share the medical image to support knowledge sharing to diagnose and analyse the image. The healthcare system needs to ensure distributes the medical image accurately with zero loss of information, fast and secure. Image compression is beneficial in ensuring that achieve the goal of sharing this data. The region of interest-based hybrid medical compression algorithm plays the parts to reduce the image size and shorten the time of medical image compression process. Various studies have enhanced by combining numerous techniques to get an ideal result. This paper reviews the previous works conducted on a region of interest-based hybrid medical image compression algorithms

Hardware Acceleration of the Embedded Zerotree Wavelet Algorithm

The goal of this project was to gain experience in designing and implementing a microelectronic system to acclerate the execution of a time-consuming software algorithm, the Embedded Zerotree Wavelet (EZW), which is used in multimedia applications. The algorithm was implemented using MATLAB to be certain it was fully understood and to serve as a validation reference. Then, the algorithm was mapped into a hardware description language, VHDL, and its resulting implementation verified with the golden reference. The hardware description was then targeted to a field-programmable gate array (FPGA). Significant acceleration was achieved since the hardware implementation in a FPGA (Xilinx Virtex-1000E using a 8.315 MHz clock) ran 10,000 times faster than the MATLAB implementation on a SUN-220 workstation. Additional speedup exploiting the parallel capabilities of the FPGA was not achieved since the EZW algorithm utilizes only sequential operations

Problem-based learning (PBL) awareness among academic staff in Universiti Tun Hussein Onn Malaysia (UTHM)

The present study was conducted to determine whether the academic staff in UTHM was aware of Problem-based Learning (PBL) as an instructional approach. It was significant to identify if the academic staff in Universiti Tun Hussein Onn Malaysia (UTHM) had the knowledge about PBL. It was also crucial to know if the academic staff was aware of PBL as a method of teaching their courses in class as this could give the feedback to the university on the use of PBL among academic staff and measures to be taken to help improve their teaching experience. A workshop could also be designed if the academic staff in UTHM was interested to know more about PBL and how it could be used in their classroom. The objective of this study was to identify the awareness of PBL among academic staff in UTHM. This study was conducted via a quantitative method using a questionnaire adapted from the Awareness Questionnaire (AQ). 100 respondents were involved in this study. The findings indicated that the awareness of PBL among UTHM academic staff was moderate. It is a hope that more exposure could be done as PBL is seen as a promising approach in the learning process. In conclusion, the academic staff in UTHM has a moderate level of knowledge about PBL as a teaching methodology

Self-similarity and wavelet forms for the compression of still image and video data

This thesis is concerned with the methods used to reduce the data volume required to represent still images and video sequences. The number of disparate still image and video coding methods increases almost daily. Recently, two new strategies have emerged and have stimulated widespread research. These are the fractal method and the wavelet transform. In this thesis, it will be argued that the two methods share a common principle: that of self-similarity. The two will be related concretely via an image coding algorithm which combines the two, normally disparate, strategies. The wavelet transform is an orientation selective transform. It will be shown that the selectivity of the conventional transform is not sufficient to allow exploitation of self-similarity while keeping computational cost low. To address this, a new wavelet transform is presented which allows for greater orientation selectivity, while maintaining the orthogonality and data volume of the conventional wavelet transform. Many designs for vector quantizers have been published recently and another is added to the gamut by this work. The tree structured vector quantizer presented here is on-line and self structuring, requiring no distinct training phase. Combining these into a still image data compression system produces results which are among the best that have been published to date. An extension of the two dimensional wavelet transform to encompass the time dimension is straightforward and this work attempts to extrapolate some of its properties into three dimensions. The vector quantizer is then applied to three dimensional image data to produce a video coding system which, while not optimal, produces very encouraging results

Survey of Hybrid Image Compression Techniques

A compression process is to reduce or compress the size of data while maintaining the quality of information contained therein. This paper presents a survey of research papers discussing improvement of various hybrid compression techniques during the last decade. A hybrid compression technique is a technique combining excellent properties of each group of methods as is performed in JPEG compression method. This technique combines lossy and lossless compression method to obtain a high-quality compression ratio while maintaining the quality of the reconstructed image. Lossy compression technique produces a relatively high compression ratio, whereas lossless compression brings about high-quality data reconstruction as the data can later be decompressed with the same results as before the compression. Discussions of the knowledge of and issues about the ongoing hybrid compression technique development indicate the possibility of conducting further researches to improve the performance of image compression method

Wavelets and Imaging Informatics: A Review of the Literature

AbstractModern medicine is a field that has been revolutionized by the emergence of computer and imaging technology. It is increasingly difficult, however, to manage the ever-growing enormous amount of medical imaging information available in digital formats. Numerous techniques have been developed to make the imaging information more easily accessible and to perform analysis automatically. Among these techniques, wavelet transforms have proven prominently useful not only for biomedical imaging but also for signal and image processing in general. Wavelet transforms decompose a signal into frequency bands, the width of which are determined by a dyadic scheme. This particular way of dividing frequency bands matches the statistical properties of most images very well. During the past decade, there has been active research in applying wavelets to various aspects of imaging informatics, including compression, enhancements, analysis, classification, and retrieval. This review represents a survey of the most significant practical and theoretical advances in the field of wavelet-based imaging informatics

Consolidating Literature for Images Compression and Its Techniques

With the proliferation of readily available image content, image compression has become a topic of considerable importance. As, rapidly increase of digital imaging demand, storage capability aspect should be considered. Therefore, image compression refers to reducing the size of image for minimizing storage without harming the image quality. Thus, an appropriate technique is needed for image compression for saving capacity as well as not losing valuable information. This paper consolidates literature whose characteristics have focused on image compression, thresholding algorithms, quantization algorithms. Later, related research on these areas are presented

Combined Industry, Space and Earth Science Data Compression Workshop

The sixth annual Space and Earth Science Data Compression Workshop and the third annual Data Compression Industry Workshop were held as a single combined workshop. The workshop was held April 4, 1996 in Snowbird, Utah in conjunction with the 1996 IEEE Data Compression Conference, which was held at the same location March 31 - April 3, 1996. The Space and Earth Science Data Compression sessions seek to explore opportunities for data compression to enhance the collection, analysis, and retrieval of space and earth science data. Of particular interest is data compression research that is integrated into, or has the potential to be integrated into, a particular space or earth science data information system. Preference is given to data compression research that takes into account the scien- tist's data requirements, and the constraints imposed by the data collection, transmission, distribution and archival systems