Deconvolving Active Contours for Fluorescence Microscopy Images

被引：0

作者：

Helmuth, Jo A. ^{[1
]}

Sbalzarini, Ivo F. ^{[1
]}

机构：

[1] Swiss Fed Inst Technol, Inst Theoret Comp Sci, Zurich, Switzerland

来源：

ADVANCES IN VISUAL COMPUTING, PT 1, PROCEEDINGS | 2009年 / 5875卷

基金：

瑞士国家科学基金会;

关键词：

TRACKING;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We extend active contours to constrained iterative deconvolution by replacing the external energy function with a model-based likelihood. This enables sub-pixel estimation of the outlines of diffraction-limited objects. such as intracellular structures, from fluorescence micrographs. We present an efficient algorithm for solving the resulting optimization problem and robustly estimate object outlines. We benchmark the algorithm on artificial images mid assess its practical utility on fluorescence micrographs of the Golgi and endosomes in live cells.

引用

页码：544 / +

页数：2

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