Publications (6)4.79 Total impact
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Article: Median-Based Robust Algorithms for Tracing
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ABSTRACT: This paper presents a method to exploit rank statistics to improve fully automatic tracing of neurons from noisy digital confocal microscope images. Previously proposed exploratory tracing (vectorization) algorithms work by recursively following the neuronal topology, guided by responses of multiple directional correlation kernels. These algorithms were found to fail when the data was of lower quality (noisier, less contrast, weak signal, or more discontinuous structures). This type of data is commonly encountered in the study of neuronal growth on microfabricated surfaces. We show that by partitioning the correlation kernels in the tracing algorithm into multiple subkernels, and using the median of their responses as the guiding criterion improves the tracing precision from 41% to 89% for low-quality data, with a 5% improvement in recall. Improved handling was observed for artifacts such as discontinuities and/or hollowness of structures. The new algorithms require slightly higher amounts of computation, but are still acceptably fast, typically consuming less than 2 seconds on a personal computer (Pentium III, 500 MHz, 128 MB). They produce labeling for all somas present in the field, and a graph-theoretic representation of all dendritic/axonal structures that can be edited. Topological and size measurements such as area, length, and tortuosity are derived readily. The efficiency, accuracy, and fully-automated nature of the proposed method makes it attractive for large-scale applications such as high-throughput assays in the pharmaceutical industry, and study of neuron growth on nano/micro-fabricated structures. A careful quantitative validation of the proposed algorithms is provided against manually derived tracing, using a performance measure that combines the precis...03/2004; -
Article: Rapid Automated Three-Dimensional Tracing of Neurons From Confocal Image Stacks
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ABSTRACT: Algorithms are presented for fully automatic three-dimensional (3-D) tracing of neurons that are imaged by fluorescence confocal microscopy. Unlike previous voxel-based skeletonization methods, the present approach works by recursively following the neuronal topology, using a set of 4 directional kernels (e.g., =32), guided by a generalized 3-D cylinder model. This method extends our prior work on exploratory tracing of retinal vasculature to 3-D space. Since the centerlines are of primary interest, the 3-D extension can be accomplished by four rather than six sets of kernels. Additional modifications, such as dynamic adaptation of the correlation kernels, and adaptive step size estimation, were introduced for achieving robustness to photon noise, varying contrast, and apparent discontinuity and/or hollowness of structures. The end product is a labeling of all somas present, graph-theoretic representations of all dendritic/axonal structures, and image statistics such as soma volume and centroid, soma interconnectivity, the longest branch, and lengths of all graph branches originating from a soma. This method is able to work directly with unprocessed confocal images, without expensive deconvolution or other preprocessing. It is much faster that skeletonization, typically consuming less than a minute to trace a 70-MB image on a 500-MHz computer. These properties make it attractive for large-scale automated tissue studies that require rapid on-line image analysis, such as high-throughput neurobiology/angiogenesis assays, and initiatives such as the Human Brain Project.11/2003; -
Article: Multi-View Three-Dimensional Image Montaging & Signal Attenuation Correction for Maximizing the Imaging Depth and Lateral Extent of Confocal Microscopes
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ABSTRACT: -Microscopy and Microanalysis 07/2002; 8:1042 - 1043. · 3.01 Impact Factor -
Article: Rapid automated three-dimensional tracing of neurons from confocal image stacks.
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ABSTRACT: Algorithms are presented for fully automatic three-dimensional (3-D) tracing of neurons that are imaged by fluorescence confocal microscopy. Unlike previous voxel-based skeletonization methods, the present approach works by recursively following the neuronal topology, using a set of 4 x N2 directional kernels (e.g., N = 32), guided by a generalized 3-D cylinder model. This method extends our prior work on exploratory tracing of retinal vasculature to 3-D space. Since the centerlines are of primary interest, the 3-D extension can be accomplished by four rather than six sets of kernels. Additional modifications, such as dynamic adaptation of the correlation kernels, and adaptive step size estimation, were introduced for achieving robustness to photon noise, varying contrast, and apparent discontinuity and/or hollowness of structures. The end product is a labeling of all somas present, graph-theoretic representations of all dendritic/axonal structures, and image statistics such as soma volume and centroid, soma interconnectivity, the longest branch, and lengths of all graph branches originating from a soma. This method is able to work directly with unprocessed confocal images, without expensive deconvolution or other preprocessing. It is much faster that skeletonization, typically consuming less than a minute to trace a 70-MB image on a 500-MHz computer. These properties make it attractive for large-scale automated tissue studies that require rapid on-line image analysis, such as high-throughput neurobiology/angiogenesis assays, and initiatives such as the Human Brain Project.IEEE Transactions on Information Technology in Biomedicine 07/2002; 6(2):171-87. · 1.68 Impact Factor -
Article: Rapid automated three-dimensional tracing of neurons from confocal image stacks.
IEEE Transactions on Information Technology in Biomedicine. 01/2002; 6:171-187. -
Article: Three-Dimensional Light Microscopy: Observation of Thick Objects
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ABSTRACT: Conventional light microscopy is an exceptionally powerful and flexible tool for the observation of cells and tissues, but it can only image very thin, essentially 2-dimensional (2D) specimens. This is the case for both the transmitted light bright field and fluorescent image modes, which are the most frequently used microscopies for histological specimens. However, biological objects, be they individual cells or complex tissues, are 3-dimensional (3D), which means their 3D structure is either difficult or impossible to appreciate from conventional 2D images. A major factor affecting the observation of thick samples is that the specimens are significantly thicker than the depth-of-field of the microscope objective lens, ie, the distance through the thickness of the specimen over which the image is in-focus. This results in degradation of the contrast or visibility of in-focuse image structure by out-of-focus light from specimen regions above and/or below the in-focus region. If the specimen is sufficiently thick, this degradation is so severe that little or no in-focus structure is observed. Relatively recent developments have minimized the affect of the out-of-focus light, making 3D light microscopy possible. Confocal microscopy is an optical method that minimizes the recording of out-of-focus light while ensuring detection of in-focus light. This can be described mathematically as a function that transfers the specimen intensity to a magnified image. This function is referred to as the point-spread-function (psf). The psf of the confocal microscope is sharper than that of the wide-field or conventional microscope producing the improved resolution of the confocal microscopy, especially along the optic axis. The psf is not perfect and, therefore, produces a degraded image. However, its adverse effect can be decreased by digital deconvolution. It is also possible to perform automated quantitative image analysis on 3D images. Examples of automated cell counting in tissues and tracing of branched structures such as neurons are presented.Journal of histotechnology 08/2000; · 0.10 Impact Factor
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Institutions
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2002
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Wadsworth Center, NYS Department of Health
Albany, NY, USA -
Rensselaer Polytechnic Institute
- Department of Electrical, Computer, and Systems Engineering
Troy, NY, USA
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