GAUSSIAN MIXTURE MODELS FOR SPOTS IN MICROSCOPY USING A NEW SPLIT/MERGE EM ALGORITHM

被引：5

作者：

Pan, Kangyu ^{[1
]}

Kokaram, Anil ^{[1
]}

Hillebrand, Jens ^{[2
]}

Ramaswami, Mani ^{[2
]}

机构：

[1] Trinity Coll Dublin, Dept Elect & Elect Engn, Dublin 2, Ireland

[2] Trinity Coll Dublin, Inst Neurosci, Dublin 2, Ireland

来源：

2010 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING | 2010年

关键词：

Gaussian mixture model; split-and-merge EM algorithm; spot analysis; mRNA; shape modeling;

D O I：

10.1109/ICIP.2010.5652472

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In confocal microscopy imaging, target objects are labeled with fluorescent markers in the living specimen, and usually appear as spots in the observed images. Spot detection and analysis is therefore an important task but it is still heavily reliant on manual analysis. In this paper, a novel shape modeling algorithm is proposed for automating the detection and analysis of the spots of interest. The algorithm exploits a Gaussian mixture model to characterize the spatial intensity distribution of the spots, and estimates parameters using a novel split-and-merge expectation maximization (SMEM) algorithm. In previous work the split step is random which is an issue for biological analysis where repeatability is important. The new split/merge steps are deterministic, hence more useful, and further do not impact adversely on the optimality of the final result.

引用

页码：3645 / 3648

页数：4

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