State of the Art in Information Extraction and Quantitative Analysis for Multimodality Biomolecular Imaging

Ahmed, Waseem; Leavesley, Silas J.; Rajwa, Bartek; Ayyaz, Muhammad Naeem; Ghafoor, Arif; Robinson, J. Paul

doi:10.1109/jproc.2007.913556

Cited by 12 publications

(6 citation statements)

References 112 publications

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“…A range of analysis approaches have also been demonstrated for biomedical spectral imaging applications, including unmixing [8,[57][58][59], PCA and similar approaches [58,[60][61][62], and others [29,63,64]. However, only a limited amount of research has been performed to apply many of the spectral image processing algorithms developed by the remote sensing community to biomedical applications [10].…”

Section: Introductionmentioning

confidence: 99%

A theoretical‐experimental methodology for assessing the sensitivity of biomedical spectral imaging platforms, assays, and analysis methods

Leavesley

Sweat

Abbott

et al. 2017

Journal of Biophotonics

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Spectral imaging technologies have been used for many years by the remote sensing community. More recently, these approaches have been applied to biomedical problems, where they have shown great promise. However, biomedical spectral imaging has been complicated by the high variance of biological data and the reduced ability to construct test scenarios with fixed ground truths. Hence, it has been difficult to objectively assess and compare biomedical spectral imaging assays and technologies. Here, we present a standardized methodology that allows assessment of the performance of biomedical spectral imaging equipment, assays, and analysis algorithms. This methodology incorporates real experimental data and a theoretical sensitivity analysis, preserving the variability present in biomedical image data. We demonstrate that this approach can be applied in several ways: to compare the effectiveness of spectral analysis algorithms, to compare the response of different imaging platforms, and to assess the level of target signature required to achieve a desired performance. Results indicate that it is possible to compare even very different hardware platforms using this methodology. Future applications could include a range of optimization tasks, such as maximizing detection sensitivity or acquisition speed, providing high utility for investigators ranging from design engineers to biomedical scientists.

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Section: Introductionmentioning

confidence: 99%

A theoretical‐experimental methodology for assessing the sensitivity of biomedical spectral imaging platforms, assays, and analysis methods

Leavesley

Sweat

Abbott

et al. 2017

Journal of Biophotonics

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“…A key technique for molecular imaging is fluorescence microscopy, and its advanced version, multispectral (multiband) fluorescence microscopy (MFM). MFM techniques are being widely used to study cell cycle, gene expression, dynamic interactions among cells and cellular components, signaling pathways, and many other cellular processes [1]. However, imaging of live cells or tissues is difficult owing to possible cytotoxicity of fluorescent labels and phototoxicity of the imag-ing process.…”

Section: Introductionmentioning

confidence: 99%

A lowcomplexity speck-based codec for multispectral fluorescence microscopic images

Mansoor

Robinson

Rajwa

2010

2010 IEEE International Conference on Acoustics, Speech and Signal Processing

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The distortion scalable wavelet based coder SPECK is widely referred to in literature as achieving excellent coding performance with very low computational complexity compared to other state-of-the-art encoders. In this paper, we propose a 3D extension to SPECK designed for lossless coding of low-SNR fluorescence microscopic multispectral images by joining and encoding together different wavelet subbands. The idea is based on an observation that in comparison to other image types, fluorescence images have a larger proportion of energy concentrated in the lowest frequency subbands, whereas the energy inside higher frequency subbands constitute a considerably small portion. Also, subbands at the same level of wavelet-transformed image are found to be highly correlated. Consequently, encoding higher subbands by grouping them together in a single block results in a considerable bit saving. In order to maximize the block size of coefficients that can be encoded together, a new set type is introduced. The proposed technique applied to test multispectral microscopic images demonstrates a significant bit savings.Index Terms-3D SPECK, fluorescence imaging, multispectral imaging, microscopic image encoding, low-SNR image encoding.

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“…1 This leads to fivedimensional image sets, and any combination of these dimensions can be acquired depending on the requirements of the application. 2 The general approach is that biologists first develop a hypothesis and then image biological samples to validate their hypotheses.…”

Section: Introductionmentioning

confidence: 99%

Knowledge Representation and Data Mining for Biological Imaging

Ahmed

2008

Computational Systems Bioinformatics

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Biological and pharmaceutical research relies heavily on microscopically imaging cell populations for understanding their structure and function. Much work has been done on automated analysis of biological images, but image analysis tools are generally focused only on extracting quantitative information for validating a particular hypothesis. Images contain much more information than is normally required for testing individual hypotheses. The lack of symbolic knowledge representation schemes for representing semantic image information and the absence of knowledge mining tools are the biggest obstacles in utilizing the full information content of these images. In this paper we first present a graphbased scheme for integrated representation of semantic biological knowledge contained in cellular images acquired in spatial, spectral, and temporal dimensions. We then present a spatio-temporal knowledge mining framework for extracting non-trivial and previously unknown association rules from image data sets. This mechanism can change the role of biological imaging from a tool used to validate hypotheses to one used for automatically generating new hypotheses. Results for an apoptosis screen are also presented.

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State of the Art in Information Extraction and Quantitative Analysis for Multimodality Biomolecular Imaging

Cited by 12 publications

References 112 publications

A theoretical‐experimental methodology for assessing the sensitivity of biomedical spectral imaging platforms, assays, and analysis methods

A theoretical‐experimental methodology for assessing the sensitivity of biomedical spectral imaging platforms, assays, and analysis methods

A lowcomplexity speck-based codec for multispectral fluorescence microscopic images

Knowledge Representation and Data Mining for Biological Imaging

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