Lossy compression of volumetric medical images with 3D dead-zone lattice vector quantization

Gaudeau, Yann; Moureaux, Jean-Marie

doi:10.1007/s12243-008-0079-5

Cited by 14 publications

(10 citation statements)

References 12 publications

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“…Su principal limitación se encuentra en la gran complejidad computacional en tiempos de búsqueda (60,61). El vector de cuantificación es extremadamente eficiente en compresión de regiones uniformes de una imagen (62). Además, esta técnica se usa comúnmente en la compresión de imágenes de mamografía, puesto que permite la detección y clasificación de las anormalidades presentes en este tipo de imágenes (63,64).…”

Section: Cuantificaciónunclassified

Compresión de imágenes médicas

Noreña

Romero

2012

biomedica

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Contribución de los autores:Ambos autores participaron en la recopilación, unificación y síntesis de los antecedentes más destacados en el área de la compresión, mediante la discusión de la información publicada sobre el tema. Además, estructuraron las metodologías ya existentes para la compresión de imágenes médicas, aportando un guía base para las personas que desean introducirse en el área. La medicina moderna es una actividad cada vez más compleja, basada en la información proveniente de múltiples fuentes: historias clínicas, dictáfonos e imágenes y vídeos provenientes de múltiples dispositivos. Las imágenes médicas constituyen una de las fuentes de mayor importancia, por cuanto ofrecen un apoyo integral del acto médico: el diagnóstico y el seguimiento. Sin embargo, la cantidad de información generada por los dispositivos de adquisición de imágenes sobrepasa rápidamente la disponibilidad de almacenamiento que tienen los servicios de radiología, lo cual genera costos adicionales en equipos de cómputo con mayor capacidad de almacenamiento. Además, la tendencia actual de desarrollo de aplicaciones en la "nube de cómputo", tiene limitaciones por cuanto, aunque el almacenamiento es virtual y está disponible desde cualquier sitio, la conexión se hace a través de internet. En estos dos casos, el uso óptimo de la información requiere necesariamente de algoritmos de compresión potentes y adaptados a las necesidades de la actividad médica. En este artículo se presenta una revisión de las técnicas de compresión más utilizadas para el almacenamiento de imágenes, así como un análisis crítico de éstas desde el punto de vista de su uso en ambientes clínicos. REVISIÓN DE TEMAPalabras clave: procesamiento de imagen asistida por computador, compresión de datos, metodologías computacionales.doi: http://dx.doi.org/10.7705/biomedica.v33i1.804 Medical image compression: a reviewModern medicine is an increasingly complex activity, based on the evidence; it consists of information from multiple sources: medical record text, sound recordings, images and videos generated by a large number of devices. Medical imaging is one of the most important sources of information since they offer comprehensive support of medical procedures for diagnosis and follow-up. However, the amount of information generated by image capturing gadgets quickly exceeds storage availability in radiology services, generating additional costs in devices with greater storage capacity. Besides, the current trend of developing applications in cloud computing has limitations, even though virtual storage is available from anywhere, connections are made through internet. In these scenarios the optimal use of information necessarily requires powerful compression algorithms adapted to medical activity needs. In this paper we present a review of compression techniques used for image storage, and a critical analysis of them from the point of view of their use in clinical settings. Las imágenes médicas son hoy en día parte fundamental de la exploración diagnóstica de un paci...

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Section: Cuantificaciónunclassified

Compresión de imágenes médicas

Noreña

Romero

2012

biomedica

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“…A close examination of the existing 3-D medical image compression system [4] reveals a huge gap, particularly for the hardware implementation, since most of the existing works contribute to algorithms development and optimisation [53][54][55][56][57][58][59]. In the following, an overview of these works is described, and the first two descriptions [7], [8] will address the contributions on the hardware implementation of 3-D medical image compression, whilst the others focus on software simulation or algorithms development and optimisation [53][54][55][56][57][58][59].…”

Section: Medical Image Compressionmentioning

confidence: 99%

“…In the following, an overview of these works is described, and the first two descriptions [7], [8] will address the contributions on the hardware implementation of 3-D medical image compression, whilst the others focus on software simulation or algorithms development and optimisation [53][54][55][56][57][58][59].…”

Section: Medical Image Compressionmentioning

confidence: 99%

“…In [59], a new lossy coding scheme based on 3-D wavelet transform and lattice vector quantisation for volumetric medical images is addressed by Gaudeau and Moureaux. A new code book enclosing a multi-dimensional dead zone during the quantisation step, which enables for better correlation between neighbour voxels is also explained.…”

Section: Transform (Dct) and 3-d Discrete Fourier Transform (Dft) Exmentioning

confidence: 99%

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Efficient reconfigurable architectures for 3D medical image compression

Ahmad

Amira

2009

2009 International Conference on Field-Programmable Technology

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A AbstractRecently, the more widespread use of three-dimensional (3-D) imaging modalities, such as magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), and ultrasound (US) have generated a massive amount of volumetric data. These have provided an impetus to the development of other applications, in particular telemedicine and teleradiology. In these fields, medical image compression is important since both efficient storage and transmission of data In summary, this research is tackling the issues of massive 3-D medical volumes data that requires compression as well as hardware implementation to accelerate the slowest operations in the system. Results obtained also reveal a significant achievement in terms of the architecture efficiency and applications performance.

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“…Although many researchers have focused on lossy compression of biomedical images in [3] and [2]. In these papers, lossy compression is attained using vector quantization and the effect of quantization error is seldom studied.…”

Section: Introductionmentioning

confidence: 99%

Lossless compression based on hierarchical extrapolation for biomedical imaging applications

Vallathan¹,

Jayanthi²

2015

2015 International Conference on Microwave, Optical and Communication Engineering (ICMOCE)

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The imaging systems like CT, MRI scan exhibits huge amount of digital data and therefore compression becomes crucial for storage and communication resolves. Most of the recent compression schemes offers a very high compression ratio with significant loss of image quality and do not always perform better for all sets of similar images. This work aims at resolving this issue, which serves as the motivation. This paper presents a lossless image compression based on hierarchical extrapolation for medical images using Haar transform and through Embedded Encoding technique. The compression technique proves to be lossless and as well perform better for a variety of images including CT scan, MRI and ultrasound biomedical images than the existing schemes. The performance metrics namely Peak Signal to Noise Ratio, Compression ratio and mean square error values are computed for the compressed image for evaluation . The performance metrics attained through the proposed algorithm is bench marked with JPEG 2000. The result section of this paper brings forth the relative improvement offered by the proposed logic.

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Lossy compression of volumetric medical images with 3D dead-zone lattice vector quantization

Cited by 14 publications

References 12 publications

Compresión de imágenes médicas

Compresión de imágenes médicas

Efficient reconfigurable architectures for 3D medical image compression

Lossless compression based on hierarchical extrapolation for biomedical imaging applications

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