Real-time registration and display of confocal microscope imagery for multiple-band analysis

Budge, Scott E.; Mayampurath, Anoop; Solinsky, J.C.

doi:10.1109/acssc.2004.1399412

Cited by 3 publications

(3 citation statements)

References 6 publications

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“…Although image registration tasks are mostly performed using general purpose computers, combining central processing and graphics processing units, there are developments targeting implementation in specialised hardware, for example, using field programmable gate arrays. Examples of fast, hardware‐based image registration include registration of medical images [7–9] and real‐time registration of confocal microscopy images [10], precisely the type of images that would benefit from residual complexity similarity metric.…”

Section: Introductionmentioning

confidence: 99%

Fast computation of residual complexity image similarity metric using low‐complexity transforms

Pauchard¹,

Cintra

Madanayake³

et al. 2015

IET Image Processing

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Section: Introductionmentioning

confidence: 99%

Fast computation of residual complexity image similarity metric using low‐complexity transforms

Pauchard¹,

Cintra

Madanayake³

et al. 2015

IET Image Processing

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“…Protein (Oliver et al 2005) and DNA (Brown et al 2004) sequencing are starting to use FPGAs to reduce processing time. Real-time processing, registration and other image analyses from confocal microscopy are enabled by FPGAs (Budge et al 2004, Resat et al 2004. Most modelling applications on FPGAs have been limited to studying neural networks consisting of reduced neurons (Blake et al 1997, Botros and Abdul-Aziz 1994, Changjian and Hammerstrom 2003, Omondi and Rajapakse 2002.…”

Section: Introductionmentioning

confidence: 99%

Architectures for high-performance FPGA implementations of neural models

Weinstein¹,

Lee²

2005

J. Neural Eng.

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As the complexity of neural models continues to increase (larger populations, varied ionic conductances, more detailed morphologies, etc) traditional software-based models have difficulty scaling to reach the performance levels desired. This paper describes the use of FPGAs, or field programmable gate arrays, to easily implement a wide variety of neural models with the performance of custom analogue circuits or computer clusters, the reconfigurability of software, and at a cost rivalling personal computers. FPGAs reach this level of performance by enabling the design of neural models as parallel processed data paths. These architectures provide for a wide range of single-compartment, multi-compartment and population models to be readily converted to FPGA implementations. Generalized architectures are described for the efficient modelling of a first-order, nonlinear differential equation in throughput maximizing or latency minimizing data-path configurations. The homogeneity of population and multicompartment models is exploited to form deep pipelines for improved performance. Limitations of FPGA architectures and future research areas are explored.

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“…Some microscopes, such as confocal types, use multiple laser line illuminations of tagged cells to monitor cellular systems, thereby developing images not of the cells but of the molecular networks within and between the cells in real-time using fluorescence imaging techniques (2,3). Current ''filter wheel'' microscopes are also able to measure discrete images of the cells in a short time, with a stepwise changing band set of liquid crystal-based filters, thus generating a datacube of the cell tissue of interest (4).…”

mentioning

confidence: 99%

Multispectral/hyperspectral image enhancement for biological cell analysis

et al. 2006

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Background: Microscopes form projected images from illuminated objects, such as cellular tissue, which are recorded at a distance through the optical system's field of view. A telescope on a satellite or airplane also forms images with a similar optical projection of objects on the ground. Typical visible illuminations form a displayed set of three-color channels (Red Green Blue [RGB]) that are combined from three image sensor arrays (e.g., focal plane arrays) into a single pixel coding for each color present in the image. Analysis of these RGB color images develops a qualitative image representation of the objects. Methods: Independent component analysis (ICA) is used for analysis and enhancement of multispectral images, and compared with the similar and widely used principal component analysis.

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Real-time registration and display of confocal microscope imagery for multiple-band analysis

Abstract: Multiple images acquired in real-time from a confocal microscope in different illumination

Cited by 3 publications

References 6 publications

Fast computation of residual complexity image similarity metric using low‐complexity transforms

Fast computation of residual complexity image similarity metric using low‐complexity transforms

Architectures for high-performance FPGA implementations of neural models

Multispectral/hyperspectral image enhancement for biological cell analysis

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