Medical image compression based on region of interest, with application to colon CT images

Gokturk, S.B.; Tomasi, Carlo; Girod, Bernd; Beaulieu, Christopher F.

doi:10.1109/iembs.2001.1017274

Cited by 58 publications

(34 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, the wavelet-based Hcompress software [16] used in compressing astronomy images has both a lossy and a loss-less capability; the former is used to generate pre-view images, where fine details may be irrelevant, and the latter is used in scientific analysis where such details are important. Similarly, for medical images, loss-less compression is used to preserve the information in the data, though a hybrid approach that uses lossy compression in regions that are not of interest has been proposed [15] to reduce the size.…”

Section: Related Workmentioning

confidence: 99%

A Comparison of Compressed Sensing and Sparse Recovery Algorithms Applied to Simulation Data

Fan

Kamath

2016

Stat., optim. inf. comput.

View full text Add to dashboard Cite

The move toward exascale computing for scientific simulations is placing new demands on compression techniques. It is expected that the I/O system will not be able to support the volume of data that is expected to be written out. To enable quantitative analysis and scientific discovery, we are interested in techniques that compress high-dimensional simulation data and can provide perfect or near-perfect reconstruction. In this paper, we explore the use of compressed sensing (CS) techniques to reduce the size of the data before they are written out. Using large-scale simulation data, we investigate how the sufficient sparsity condition and the contrast in the data affect the quality of reconstruction and the degree of compression. We provide suggestions for the practical implementation of CS techniques and compare them with other sparse recovery methods. Our results show that despite longer times for reconstruction, compressed sensing techniques can provide near perfect reconstruction over a range of data with varying sparsity. Background and MotivationThe analysis of data from scientific simulations is typically done by writing out the variables of interest at each time step and analyzing them at a later time. For simulations run on massively parallel machines, the volume of data written out frequently approaches terabytes and beyond. In some problems, writing out the data could be more time consuming than the computations done at each time step. As we move to exascale computing, it is expected that the I/O subsystem will not be able to provide the needs of the simulations [8]. Instead of writing out the data for analysis, it has been proposed that the analysis algorithms be moved in situ, and only the analysis results, which are hopefully much smaller, be written out. This is a viable option that works when we know the analysis algorithm we want to use and its parameters. However, in many cases, the choice of analysis algorithms and their parameters may depend on what we find as we analyze the simulation output. This is especially true when the motivation for the analysis is scientific understanding and discovery. In such cases, the analysis cannot be done in situ and alternate approaches have to be considered to address this problem of limited I/O bandwidth.One such approach is to reduce the size of the simulation output by compressing the data before they are written out. There are several ways of compressing data, ranging from the traditional compression schemes, such as JPEG, that are used for images, to others that are based on clustering techniques from data mining. For the data set and problem considered in this paper, we require loss-less compression techniques that can be applied to data that are irregularly spaced in two or three dimensions and are distributed across multiple processors (or files). We propose

show abstract

Section: Related Workmentioning

confidence: 99%

A Comparison of Compressed Sensing and Sparse Recovery Algorithms Applied to Simulation Data

Fan

Kamath

2016

Stat., optim. inf. comput.

View full text Add to dashboard Cite

show abstract

“…Figure 4 exhibits the original image (a), the DCT with 128 size quantization (b), the DCT step with 1024 size quantization (c), the PCA (d), the quantization vector with 7 x 7 blocks (e), the estimated coding (f), the ROI with 8 x 8 blocks (g), the ROI with 16 x 16 blocks (h). [7] The compression process of …”

Section: Classification Of Modalitiesmentioning

confidence: 99%

“…This JPEG standard was called. [7] The JPEG committee decided to develop another standard for image compression called JPEG2000. It was a response to the growing demands of multimedia, the internet and a great variety of digital imaging.…”

Section: Modes Of Image Compressionmentioning

confidence: 99%

Images compression process in the radiological service unit as an infrastructure component in the Colombian health model

Pico

Cardenas²,

Sevillano

2015

TECCIENCIA

View full text Add to dashboard Cite

This article depicts the process of images compression for the Discrete Cosine Transform (DCT). By being one of the most significant transformations in the area of digital image compression the DCT transforms a block of data into a new set of values.This work presents a review of images compression applied to the field of health specifically for the radiology service, where the Discrete Cosine Transform (DCT) is used as the Inverted Discrete Cosine Transform (ICDT). In this particular case, a rapid algorithm is used for DCT, which is figured out by applying the parallel arithmetic allowing the designed architecture to reach a better performance in software implementations.Firstly the different imaging modalities that are introduced into the compression system to obtain results through simulations in Matlab are included here. Subsequently, based on the results the application of radiology service is seen within the infrastructure component in the health sector and finally an analysis of production from 2011 to 2014 of hospitals is observed. Keywords:Compression process development, health informatics, Discrete Cosine Transform (DCT), image processing, image compression, infrastructure in the causal model, infrastructure component, radiology. ResumenEste artículo presenta el proceso de compresión de imágenes de la Transformada Discreta de Coseno (DCT, por sus siglas en inglés). Al ser una de las transformadas más significativas en el área de la compresión digital de imágenes, la DTC transforma un bloque de datos en un nuevo conjunto de valores fijos.Este trabajo presenta una revisión de la compresión de imágenes aplicada al campo de la salud, específicamente en los servicios de radiología, donde la Transformada Discreta de Coseno (DCT) es utilizada como la Transformada Discreta del Coseno Inverso (IDCT, por sus siglas en ingles). En este caso particular, un algoritmo rápido es usado para la DTC, que es resuelto mediante la aplicación del paralelo aritmético, permitiendo que el diseño arquitectónico alcance un mejor desempeño en la implementación del software. Inicialmente se incluyen las diferentes modalidades de imágenes, que son introducidas en el sistema de comprensión para obtener resultados a través de simulaciones en Matlab. Posteriormente, los resultados se basan en la aplicación del sistema en los servicios de radiología dentro de los componentes de infraestructura en el sector de salud y finalmente se realiza un análisis de la producción del 2011 al 2014 en los hospitales observados.Palabras clave: Desarrollo del proceso de comprensión, salud informática, Transformada Discreta de Coseno (DCT), procesamiento de imágenes, comprensión de imágenes, modelo causal de infraestructura, componentes de infraestructura, radiología.

show abstract

“…A hybrid model of lossless compression of the selected region of interest was studied by Gorturk et al [2] considering maximized data rate along with efficient motion compensation and lossy compression on the non-selected region. The experimentation were carried out using medical images and the outcome of the study shows that it is better than other techniques of compression e.g.…”

Section: Related Workmentioning

confidence: 99%

Computational Modelling of Image Coding using ROI based Medical Image Compression

Suma¹,

Sridhar²

2014

IJCA

View full text Add to dashboard Cite

In the age of digital medical imaging communication and robotic transmission of real-time image for robot guided operations in constrained bandwidth is a challenging task. The issue of compression, in medical images, is the prime focus of this study. The study has aimed to perform an investigation on the frequently adopted region-of-interest scheme called as MAXSHIFT. The design principle of this standard encoding technique allows encoding and highly prioritizing only the region of interest and then emphasis on the background (nonregion of interest area). The system allows the deployment of multiple and randomly shaped region of interest within the medical images using randomized weights for emphasizing each element of ROI. Supported by the discussion on some of the prior research work, and how this study is motivated, the present manuscript illustrates the experimental phases of implementing MAXSHIFT on two dimensional medical images. In order to check the robustness of the algorithm, the performance parameters such as bit per pixel (BPP) and Signal to Noise Ratio (SNR) is being evaluated on enormous medical images.

show abstract

Medical image compression based on region of interest, with application to colon CT images

Cited by 58 publications

References 15 publications

A Comparison of Compressed Sensing and Sparse Recovery Algorithms Applied to Simulation Data

A Comparison of Compressed Sensing and Sparse Recovery Algorithms Applied to Simulation Data

Images compression process in the radiological service unit as an infrastructure component in the Colombian health model

Computational Modelling of Image Coding using ROI based Medical Image Compression

Contact Info

Product

Resources

About