Name
Title | ||
|---|---|---|
Superresolution And Em Based Ml Kalman Estimation Of The Stochastic Microtubule Signal Modeled As Three States Random Evolution |
Abstract | ||
|---|---|---|
Data collection of cellular processes (such as Microtubules (MTs) Dynamic Instability) using optical microscopes, are often threatened by either destruction of the specimen or the probe; thereby limiting the extensive period of time that the data can be collected. This leads to scarcity of data. Due to this, we encounter non-uniform sampling of the MT dynamic instability phenomenon relative to the time-lapse observation of the cellular processes. In this paper, we present a novel super-resolution technique to address both non-uniform sampling and limited data availability of MT signals. We use Expectation Maximization (EM) based Maximum Likelihood (ML) estimation using Kalman filters on the interpolated (from non-uniformly sampled) MT signals to optimize prediction of the missing observations in the data. This is followed by correlation-patch based post processing to further refine our predictions. The three-state dynamic instability MT parameters are estimated using wavelet-based peak detection. Experimental results show that our prediction method had superior performance, high SNR and low errors compared to interpolation-only and compressed sensing methods. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/BIBM.2017.8217736 | 2017 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM) |
DocType | ISSN | Citations |
Conference | 2156-1125 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vineetha Menon | 1 | 0 | 1.35 |
| Shantia Yarahmadian | 2 | 15 | 4.37 |
| Vahid Rezania | 3 | 1 | 1.09 |