Change detection of medical images using dictionary learning techniques and PCA

Nika, Varvara; Babyn, Paul; Zhu, Hongmei

doi:10.1117/12.2038751

Cited by 2 publications

(1 citation statement)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Others assume that measurements between change points are independent or identically distributed, or assume that change points strictly correspond to level shifts or isolated outliers (Matteson and James, 2014;Zhang et al, 2019). Alternative methods in the biomedical fields tend to be too domainspecific to apply to the the problem of detecting the change point between growth and confluence in ECIS data (Olshen et al, 2004;Nika et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Modeling a Nonlinear Biophysical Trend Followed by Long-Memory Equilibrium with Unknown Change Point

Zhang¹,

Griffin²,

Matteson³

2020

Preprint

View full text Add to dashboard Cite

Measurements of many biological processes are characterized by an initial growth period followed by an equilibrium period. Scientists may wish to quantify features of the growth and equilibrium periods, as well as the timing of the change point between the two. Specifically, we are motivated by problems in the study of electrical cell-substrate impedance sensing (ECIS) data. ECIS is a popular new technology which measures cell behavior noninvasively. Previous studies using ECIS data have found that different cell types can be classified by their equilibrium behavior. Likewise, it has been hypothesized that the presence of certain infections can be identified from cells' equilibrium behavior. However, it can be challenging to identify when equilibrium has been reached, and to quantify the relevant features of cells' equilibrium behavior. In this paper, we assume that measurements during the growth period are independent deviations from a smooth nonlinear function of time, and that measurements during the equilibrium period are characterized by a simple long memory model. We propose a method to simultaneously estimate the parameters of the growth and equilibrium processes and locate the change point between the two. We find that this method performs well in simulations and in practice. When applied to ECIS data, it produces estimates of change points and measures of cell equilibrium behavior which offer improved classification of infected and uninfected cells.

show abstract