A new singularity index

Muralidhar, Gautam S.; Bovik, Alan C.; Markey, Mia K.

doi:10.1109/icip.2012.6467249

Cited by 4 publications

(10 citation statements)

References 11 publications

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“…This paper develops detailed theoretical analyses of the detection power and false alarm probabilities of a new 1-D singularity index that was recently designed for impulse detection in signals of arbitrary dimensionality [1], [2]. By design, the singularity index amplifies response to impulses, while at the same time delivering powerful attenuation to edges.…”

Section: Discussionmentioning

confidence: 99%

“…As explained in [1], we assume the nominal value . Hence (2) The singularity index (2) is closely related to an energy operator developed by Teager and Kaiser [11], which has been employed for demodulating AM-FM signals [12]. The index (2) responds strongly to impulse masses whose twice derivative is large and once-derivative is small.…”

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confidence: 99%

“…The singularity index in (2) operates directly on the signal. This is not practical in real applications since derivative computations are not stable in the presence of noise.…”

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confidence: 99%

“…We recently designed a new index to detect impulse singularities in signals of arbitrary dimensionality [1], [2]. It is defined as the dimensionless ratio (1) where , is a 1-D signal, and are the first and second derivatives of , respectively, and the constant .…”

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confidence: 99%

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Noise Analysis of a New Singularity Index

Muralidhar

Bovik

Markey

2013

IEEE Trans. Signal Process.

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Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

“…The singularity index in (2) operates directly on the signal. This is not practical in real applications since derivative computations are not stable in the presence of noise.…”

mentioning

confidence: 99%

mentioning

confidence: 99%

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Noise Analysis of a New Singularity Index

Muralidhar

Bovik

Markey

2013

IEEE Trans. Signal Process.

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“…Indeed, the reliable detection of curvilinear structures in mammograms has been a widely studied problem and continues to be of interest for developing robust computer-aided detection (CADe) and diagnosis (CADx) algorithms (see [10], [11]). Towards solving this difficult aspect of the problem, our proposed model employs a novel singularity index that was recently developed to reliably detect singular points in images [12], [13]. The singularity index can be configured to detect point mass like structures such as impulses in a 1D signal or curvilinear masses in images, while rejecting step edges.…”

Section: Introductionmentioning

confidence: 99%

Disparity Estimation on Stereo Mammograms

Muralidhar

Bovik

Markey

2015

IEEE Trans. on Image Process.

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We consider the problem of depth estimation on digital stereo mammograms. Being able to elucidate 3D information from stereo mammograms is an important precursor to conducting 3D digital analysis of data from this promising new modality. The problem is generally much harder than the classic stereo matching problem on visible light images of the natural world, since nearly all of the 3D structural information of interest exists as complex network of multilayered, heavily occluded curvilinear structures. Toward addressing this difficult problem, we formulate a new stereo model that minimizes a global energy functional to densely estimate disparity on stereo mammogram images, by introducing a new singularity index as a constraint to obtain better estimates of disparity along critical curvilinear structures. Curvilinear structures, such as vasculature and spicules, are particularly salient structures in the breast, and being able to accurately position them in 3D is a valuable goal. Experiments on synthetic images with known ground truth and on real stereo mammograms highlight the advantages of the proposed stereo model over the canonical stereo model.

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A spatiotemporal characterization method for the dynamic cytoskeleton

et al. 2016

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The significant gap between quantitative and qualitative understanding of cytoskeletal function is a pressing problem; microscopy and labeling techniques have improved qualitative investigations of localized cytoskeleton behavior, whereas quantitative analyses of whole cell cytoskeleton networks remain challenging. Here we present a method that accurately quantifies cytoskeleton dynamics. Our approach digitally subdivides cytoskeleton images using interrogation windows, within which box‐counting is used to infer a fractal dimension (D f) to characterize spatial arrangement, and gray value intensity (GVI) to determine actin density. A partitioning algorithm further obtains cytoskeleton characteristics from the perinuclear, cytosolic, and periphery cellular regions. We validated our measurement approach on Cytochalasin‐treated cells using transgenically modified dermal fibroblast cells expressing fluorescent actin cytoskeletons. This method differentiates between normal and chemically disrupted actin networks, and quantifies rates of cytoskeletal degradation. Furthermore, GVI distributions were found to be inversely proportional to D f, having several biophysical implications for cytoskeleton formation/degradation. We additionally demonstrated detection sensitivity of differences in D f and GVI for cells seeded on substrates with varying degrees of stiffness, and coated with different attachment proteins. This general approach can be further implemented to gain insights on dynamic growth, disruption, and structure of the cytoskeleton (and other complex biological morphology) due to biological, chemical, or physical stimuli. © 2016 The Authors. Cytoskeleton Published by Wiley Periodicals, Inc.

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A new singularity index

Cited by 4 publications

References 11 publications

Noise Analysis of a New Singularity Index

Noise Analysis of a New Singularity Index

Disparity Estimation on Stereo Mammograms

A spatiotemporal characterization method for the dynamic cytoskeleton

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