Automated Histology Analysis: Opportunities for signal processing

McCann, Michael T.; Ozolek, John A.; Castro, Carlos; Parvin, Bahram; Kovačević, Jelena

doi:10.1109/msp.2014.2346443

Cited by 110 publications

(58 citation statements)

References 51 publications

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“…In section III C we consider a measurement distinguishing one qubit as in Eq. (5). The resulting invariant can identify all graphs we have tested when combined with the eigenvalues of the adjacency matrix.…”

Section: A Underpinning Theorymentioning

confidence: 99%

Quantum invariants and the graph isomorphism problem

et al. 2019

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Three new graph invariants are introduced which may be measured from a quantum graph state and form examples of a framework under which other graph invariants can be constructed. Each invariant is based on distinguishing a different number of qubits. This is done by applying alternate measurements to the qubits to be distinguished. The performance of these invariants is evaluated and compared to classical invariants. We verify that the invariants can distinguish all non-isomorphic graphs with 9 or fewer nodes. The invariants have also been applied to 'classically hard' strongly regular graphs, successfully distinguishing all strongly regular graphs of up to 29 nodes, and preliminarily to weighted graphs. We have found that although it is possible to prepare states with a polynomial number of operations, the average number of preparations required to distinguish non-isomorphic graph states scales exponentially with the number of nodes. We have so far been unable to find operators which reliably compare graphs and reduce the required number of preparations to feasible levels. arXiv:1711.09842v3 [quant-ph]

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Section: A Underpinning Theorymentioning

confidence: 99%

Quantum invariants and the graph isomorphism problem

et al. 2019

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“…We define a score for each vertex z ∈ V \ S i as follows, (6) whereθ z k ,θ z ,θ z k+1 denote angles that are less than 180 degrees at corners z k−1 z k z, z k zz k+1 , zz k+1 z k+2 , respec-tively. The score f e * (z) indicates (a) the proximity between vertex z and edge e * , measured in terms of the deviation of the summation l z k z + l zz k+1 from l e * , and (b) the irregularity of shape of edges to be built if z is selected and added to the polygon, measured by the deviation of anglesθ z k ,θ z ,θ z k+1 from the a priori mean angle µ θ .…”

Section: B the Reversible-jump Markov Chain Monte Carlomentioning

confidence: 99%

A Stochastic Polygons Model for Glandular Structures in Colon Histology Images

Sirinukunwattana

Snead

Rajpoot

2015

IEEE Trans. Med. Imaging

214

139

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“…We will introduce the major categories of detection and segmentation approaches and explain the mathematical models for basic methods, with discussing their advantages and limitations. The preprocessing techniques including color normalization and image denoising, which are presented in [15], [21], [22], and extraction of regions of interest, which are introduced in [23], [24], [25], prior to the detection or segmentation will not be reviewed in this paper. Meanwhile, although immunohistochemical staining is also used to facilitate manual assessment of image analysis [26], [27], it is beyond the scope of this paper.…”

Section: Introductionmentioning

confidence: 99%

Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review

Xing

Yang

2016

IEEE Rev. Biomed. Eng.

407

220

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Digital pathology and microscopy image analysis is widely used for comprehensive studies of cell morphology or tissue structure. Manual assessment is labor intensive and prone to inter-observer variations. Computer-aided methods, which can significantly improve the objectivity and reproducibility, have attracted a great deal of interest in recent literatures. Among the pipeline of building a computer-aided diagnosis system, nucleus or cell detection and segmentation play a very important role to describe the molecular morphological information. In the past few decades, many efforts have been devoted to automated nucleus/cell detection and segmentation. In this review, we provide a comprehensive summary of the recent state-of-the-art nucleus/cell segmentation approaches on different types of microscopy images including bright-field, phase-contrast, differential interference contrast (DIC), fluorescence, and electron microscopies. In addition, we discuss the challenges for the current methods and the potential future work of nucleus/cell detection and segmentation.

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Automated Histology Analysis: Opportunities for signal processing

Cited by 110 publications

References 51 publications

Quantum invariants and the graph isomorphism problem

Quantum invariants and the graph isomorphism problem

A Stochastic Polygons Model for Glandular Structures in Colon Histology Images

Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review

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