Two-Phase Greedy Pursuit Algorithm for Automatic Detection and Characterization of Transient Calcium Signaling

Chen Kan, Kay Pong Yip, Hui Yang

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

Ca2+ plays an important role in the regulation of cellular functions. Local calcium events, e.g., calcium sparks, not only bring insights into Ca2+ signaling but also contribute to the understanding of various cellular processes. However, it is challenging to detect calcium sparks, due to their transient properties and high level of nonstationary noises in microscopic images. Most of existing algorithms tend to have limitations for the detection of calcium sparks, e.g., empirically defined hard thresholds or poor applicability to nonstationary conditions. This paper presents a novel two-phase greedy pursuit (TPGP) algorithm for automatic detection and characterization of calcium sparks. In Phase I, a coarse-grained search is conducted across the whole image to identify the predominant sparks. In Phase II, adaptive basis function model is developed for the fine-grained representation of detected sparks. It may be noted that the proposed TPGP algorithms overcome the drawback of hard thresholding in most of previous approaches. Furthermore, the morphology of detected sparks is effectively modeled with multiscale basis functions in Phase II, thereby facilitating the analysis of physiological features. We evaluated and validated the TPGP algorithms using both real-word and synthetic images with multiple noise levels and varying baselines. Experimental results show that TPGP algorithms yield better performances than previous hard-thresholding approaches in terms of both sensitivities and positive predicted values. The present research provides the community a robust tool for the automatic detection and characterization of transient calcium signaling.

Original languageEnglish (US)
Article number6774889
Pages (from-to)687-697
Number of pages11
JournalIEEE Journal of Biomedical and Health Informatics
Volume19
Issue number2
DOIs
StatePublished - Mar 1 2015

Fingerprint

Calcium Signaling
Electric sparks
Calcium
Noise
Research

All Science Journal Classification (ASJC) codes

  • Biotechnology
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Health Information Management

Cite this

@article{0690ef0d6e284db78f8791c8c5792cc2,
title = "Two-Phase Greedy Pursuit Algorithm for Automatic Detection and Characterization of Transient Calcium Signaling",
abstract = "Ca2+ plays an important role in the regulation of cellular functions. Local calcium events, e.g., calcium sparks, not only bring insights into Ca2+ signaling but also contribute to the understanding of various cellular processes. However, it is challenging to detect calcium sparks, due to their transient properties and high level of nonstationary noises in microscopic images. Most of existing algorithms tend to have limitations for the detection of calcium sparks, e.g., empirically defined hard thresholds or poor applicability to nonstationary conditions. This paper presents a novel two-phase greedy pursuit (TPGP) algorithm for automatic detection and characterization of calcium sparks. In Phase I, a coarse-grained search is conducted across the whole image to identify the predominant sparks. In Phase II, adaptive basis function model is developed for the fine-grained representation of detected sparks. It may be noted that the proposed TPGP algorithms overcome the drawback of hard thresholding in most of previous approaches. Furthermore, the morphology of detected sparks is effectively modeled with multiscale basis functions in Phase II, thereby facilitating the analysis of physiological features. We evaluated and validated the TPGP algorithms using both real-word and synthetic images with multiple noise levels and varying baselines. Experimental results show that TPGP algorithms yield better performances than previous hard-thresholding approaches in terms of both sensitivities and positive predicted values. The present research provides the community a robust tool for the automatic detection and characterization of transient calcium signaling.",
author = "Chen Kan and Yip, {Kay Pong} and Hui Yang",
year = "2015",
month = "3",
day = "1",
doi = "10.1109/JBHI.2014.2312293",
language = "English (US)",
volume = "19",
pages = "687--697",
journal = "IEEE Journal of Biomedical and Health Informatics",
issn = "2168-2194",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "2",

}

Two-Phase Greedy Pursuit Algorithm for Automatic Detection and Characterization of Transient Calcium Signaling. / Kan, Chen; Yip, Kay Pong; Yang, Hui.

In: IEEE Journal of Biomedical and Health Informatics, Vol. 19, No. 2, 6774889, 01.03.2015, p. 687-697.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Two-Phase Greedy Pursuit Algorithm for Automatic Detection and Characterization of Transient Calcium Signaling

AU - Kan, Chen

AU - Yip, Kay Pong

AU - Yang, Hui

PY - 2015/3/1

Y1 - 2015/3/1

N2 - Ca2+ plays an important role in the regulation of cellular functions. Local calcium events, e.g., calcium sparks, not only bring insights into Ca2+ signaling but also contribute to the understanding of various cellular processes. However, it is challenging to detect calcium sparks, due to their transient properties and high level of nonstationary noises in microscopic images. Most of existing algorithms tend to have limitations for the detection of calcium sparks, e.g., empirically defined hard thresholds or poor applicability to nonstationary conditions. This paper presents a novel two-phase greedy pursuit (TPGP) algorithm for automatic detection and characterization of calcium sparks. In Phase I, a coarse-grained search is conducted across the whole image to identify the predominant sparks. In Phase II, adaptive basis function model is developed for the fine-grained representation of detected sparks. It may be noted that the proposed TPGP algorithms overcome the drawback of hard thresholding in most of previous approaches. Furthermore, the morphology of detected sparks is effectively modeled with multiscale basis functions in Phase II, thereby facilitating the analysis of physiological features. We evaluated and validated the TPGP algorithms using both real-word and synthetic images with multiple noise levels and varying baselines. Experimental results show that TPGP algorithms yield better performances than previous hard-thresholding approaches in terms of both sensitivities and positive predicted values. The present research provides the community a robust tool for the automatic detection and characterization of transient calcium signaling.

AB - Ca2+ plays an important role in the regulation of cellular functions. Local calcium events, e.g., calcium sparks, not only bring insights into Ca2+ signaling but also contribute to the understanding of various cellular processes. However, it is challenging to detect calcium sparks, due to their transient properties and high level of nonstationary noises in microscopic images. Most of existing algorithms tend to have limitations for the detection of calcium sparks, e.g., empirically defined hard thresholds or poor applicability to nonstationary conditions. This paper presents a novel two-phase greedy pursuit (TPGP) algorithm for automatic detection and characterization of calcium sparks. In Phase I, a coarse-grained search is conducted across the whole image to identify the predominant sparks. In Phase II, adaptive basis function model is developed for the fine-grained representation of detected sparks. It may be noted that the proposed TPGP algorithms overcome the drawback of hard thresholding in most of previous approaches. Furthermore, the morphology of detected sparks is effectively modeled with multiscale basis functions in Phase II, thereby facilitating the analysis of physiological features. We evaluated and validated the TPGP algorithms using both real-word and synthetic images with multiple noise levels and varying baselines. Experimental results show that TPGP algorithms yield better performances than previous hard-thresholding approaches in terms of both sensitivities and positive predicted values. The present research provides the community a robust tool for the automatic detection and characterization of transient calcium signaling.

UR - http://www.scopus.com/inward/record.url?scp=84924631361&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84924631361&partnerID=8YFLogxK

U2 - 10.1109/JBHI.2014.2312293

DO - 10.1109/JBHI.2014.2312293

M3 - Article

C2 - 25751845

AN - SCOPUS:84924631361

VL - 19

SP - 687

EP - 697

JO - IEEE Journal of Biomedical and Health Informatics

JF - IEEE Journal of Biomedical and Health Informatics

SN - 2168-2194

IS - 2

M1 - 6774889

ER -