A review of wavelets in biomedical applications

Unser, Michaël; Aldroubi, Akram

doi:10.1109/5.488704

Cited by 639 publications

(308 citation statements)

References 104 publications

(54 reference statements)

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“…Wavelet transforms are time-frequency signal decomposition methods, their theory has been developed starting from the mid 1980's. In biomedical image processing wavelets are used for image enhancement, noise reduction and object detection (Unser and Aldroubi 1996). Although the à trous wavelet transform has excellent image enhancing and object detection characteristics on noisy images, to achieve fully automatic detection for events with variable size, parameters of the algorithm have to be properly adjusted.…”

Section: Introductionmentioning

confidence: 99%

Improved spark and ember detection using stationary wavelet transforms

Szabó¹,

Vincze²,

Csernoch³

et al. 2010

Journal of Theoretical Biology

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Calcium sparks and embers are localized intracellular events of calcium release in muscle cells studied frequently by confocal microscopy using line-scan imaging. The large quantity of images and large number of events require automatic detection procedures based on signal processing methods. In the past decades these methods were based on thresholding procedures. Although recently wavelet transforms were also introduced, they have not become widespread. We have implemented a set of algorithms based on one and two dimensional versions of the à trous wavelet transform. The algorithms were used to perform spike filtering, denoising and detection procedures. Due to the dependence of the algorithms on user adjustable parameters, their effect on the efficiency of the algorithm was studied in detail. We give methods to avoid false positive detections which are the consequence of the background noise in confocal images. In order to establish the efficiency and reliability of the algorithms, various tests were performed on artificial and experimental images. Spark parameters (amplitude, full width at half maximum) calculated using the traditional and the wavelet methods were compared. We found that the latter method is capable of identifying more events with better accuracy on experimental images. Furthermore, we extended the wavelet based transform from calcium sparks to long-lasting small-amplitude events as calcium embers. The method not only solved their automatic detection but enabled the identification of events with small amplitude that otherwise escaped the eye, rendering the determination of their characteristic parameters more accurate.To whom it may concern, Attached please find our manuscript entitled "Improved spark and ember detection using stationary wavelet transforms" by Szabo, Vincze, Csernoch and myself for consideration for publication in the Journal of Theoretical Biology.In the manuscript we give a detailed description of a method to analyze localized calcium release events based on the à trous wavelet transform. We introduce novel algorithms to handle experimental images with large intensity pixels representing shot noise as well as methods to identify long lasting small amplitude events as calcium embers. We believe that these will solve the problem of analyzing confocal images recorded in mammalian muscle fibers.We hope that you will find our manuscript interesting for the community of both theoretical and experimental biologists and thus acceptable for publication in the Journal.Sincerely yours, Peter Szentesi Cover Letter AbstractCalcium sparks and embers are localized intracellular events of calcium release in muscle cells studied frequently by confocal microscopy using line-scan imaging.The large quantity of images and large number of events require automatic detection procedures based on signal processing methods. In the past decades these methods were based on thresholding procedures. Although recently wavelet transforms were also introduced, they have not become widespread. We ...

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Section: Introductionmentioning

confidence: 99%

Improved spark and ember detection using stationary wavelet transforms

Szabó¹,

Vincze²,

Csernoch³

et al. 2010

Journal of Theoretical Biology

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“…Las técnicas de compresión sin pérdida tienen como objetivo fundamental buscar que la información original de la imagen sea preservada después de la reconstrucción, manteniendo así la relación entre la alta compresión y la calidad (29)(30)(31). Todas estas técnicas y sus variantes pueden aplicarse a la codificación de imágenes usando la redundancia estadística y la codificación entrópica.…”

Section: Compresión Sin Pérdidaunclassified

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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“…The discrete wavelet transform (DWT), and its use as a multiresolution analysis (MRA) tool, has been widely described in the literature (Jawerth and Sweldens, 1994;Hess-Nielsen and Wickerhauser, 1996;Unser and Aldroubi, 1996;Blinowska and Durka, 1997;Samar et al, 1999;Wilson, 2002;Bradley and Wilson, 2004;Wilson, 2004;Zhang et al, 2004;Bradley and Wilson, 2005;Zhang et al, 2005). In summary, the DWT is a form of digital filtering capable of deconstructing a signal into its component scales (frequency ranges), and then detailing how each scale evolves over time.…”

Section: The Over-complete Discrete Wavelet Transformmentioning

confidence: 99%

On the dual structure of the auditory brainstem response in dogs

Wilson

Bailey

Balke

et al. 2006

Clinical Neurophysiology

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Objective: To use the over-complete discrete wavelet transform (OCDWT) to further examine the dual structure of auditory brainstem response (ABR) in the dog. Methods: ABR waveforms recorded from 20 adult dogs at supra-threshold (90 and 70 dBnHL) and threshold (0-15 dBSL) levels were decomposed using a six level OCDWT and reconstructed at individual scales (frequency ranges) A6 (0-391 Hz), D6 (391-781 Hz), and D5 (781-1563 Hz). Results: At supra-threshold stimulus levels, the A6 scale (0-391 Hz) showed a large amplitude waveform with its prominent wave corresponding in latency with ABR waves II/III; the D6 scale (391-781 Hz) showed a small amplitude waveform with its first four waves corresponding in latency to ABR waves I, II/III, V, and VI; and the D5 scale (781-1563 Hz) showed a large amplitude, multiple peaked waveform with its first six waves corresponding in latency to ABR waves I, II, III, IV, V, and VI. At threshold stimulus levels (0-15 dBSL), the A6 scale (0-391 Hz) continued to show a relatively large amplitude waveform, but both the D6 and D5 scales (391-781 and 781-1563 Hz, respectively) now showed relatively small amplitude waveforms. Conclusions: A dual structure exists within the ABR of the dog, but its relative structure changes with stimulus level. Significance: The ABR in the dog differs from that in the human both in the relative contributions made by its different frequency components, and the way these components change with stimulus level.

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A review of wavelets in biomedical applications

Cited by 639 publications

References 104 publications

Improved spark and ember detection using stationary wavelet transforms

Improved spark and ember detection using stationary wavelet transforms

Compresión de imágenes médicas

On the dual structure of the auditory brainstem response in dogs

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