Identifying, predicting and estimating two dimensional ARMA process for healthcare chromosome pictures

Terence O'Neill, Jack H W Penm

Research output: Contribution to journalArticleResearchpeer-review

Abstract

This paper describes the design of an identification, prediction and estimation algorithm of a two-dimensional (2-D) autoregressive - moving average (ARMA) model using a 2-D innovation process using raw data. This model has been applied to a finite size of electronic healthcare image of human white blood cell chromosomes. An optimum smoothing approach based on this model has been implemented. The mean square error converges in 10 lines, and a steady state estimate of the embedded signal is easily reached. These results point out the desirability of accurate statistical modelling of 2-D or periodic digital data.
Original languageEnglish
Pages (from-to)263-275
Number of pages13
JournalJournal of Mathematical Sciences
Publication statusPublished - 2009

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Chromosomes
Mean square error
Blood
Innovation
Cells

Cite this

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abstract = "This paper describes the design of an identification, prediction and estimation algorithm of a two-dimensional (2-D) autoregressive - moving average (ARMA) model using a 2-D innovation process using raw data. This model has been applied to a finite size of electronic healthcare image of human white blood cell chromosomes. An optimum smoothing approach based on this model has been implemented. The mean square error converges in 10 lines, and a steady state estimate of the embedded signal is easily reached. These results point out the desirability of accurate statistical modelling of 2-D or periodic digital data.",
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Identifying, predicting and estimating two dimensional ARMA process for healthcare chromosome pictures. / O'Neill, Terence; Penm, Jack H W.

In: Journal of Mathematical Sciences, 2009, p. 263-275.

Research output: Contribution to journalArticleResearchpeer-review

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AB - This paper describes the design of an identification, prediction and estimation algorithm of a two-dimensional (2-D) autoregressive - moving average (ARMA) model using a 2-D innovation process using raw data. This model has been applied to a finite size of electronic healthcare image of human white blood cell chromosomes. An optimum smoothing approach based on this model has been implemented. The mean square error converges in 10 lines, and a steady state estimate of the embedded signal is easily reached. These results point out the desirability of accurate statistical modelling of 2-D or periodic digital data.

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