Classifying Human Blood Samples Using Characteristics of Single Molecules and Cell Structures on Microscopy Images

被引：0

作者：

Borgmann, Daniela ^{[1
]}

Mayr, Sandra ^{[2
]}

Polin, Helene ^{[3
]}

Obritzberger, Lisa ^{[1
]}

Schaller, Susanne ^{[1
]}

Dorfer, Viktoria ^{[1
]}

Jacak, Jaroslaw ^{[2
]}

Winkler, Stephan ^{[1
]}

机构：

[1] Univ Appl Sci Upper Austria, Bioinformat Res Grp, Hagenberg Campus,Softwarepk 13, A-4232 Hagenberg, Austria

[2] Univ Appl Sci Upper Austria, Dept Med Engn, A-4020 Linz, Austria

[3] Red Cross Blood Transfus Serv Upper Austria, A-4017 Linz, Austria

来源：

COMPUTER AIDED SYSTEMS THEORY - EUROCAST 2015 | 2015年 / 9520卷

关键词：

Fluorescence microscopy; Bioinformatics; Image analysis; Machine learning;

D O I：

10.1007/978-3-319-27340-2_39

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper we present a method for the definition of characteristics of single molecules as well as of cell structures on fluorescence microscopy images for classifying human disease states. Fluorescence microscopy is one of the most emerging fields in modern laboratory diagnostics and is used in various research areas, for instance in studies of protein-protein interactions, analyses of cell interactions, diagnostics, or drug distribution studies. We have developed a new combinatory workflow comprising image processing and machine learning techniques to define characteristics out of given fluorescence microscopy images and to classify given images of blood samples according to their level of protein expression (high or low), i.e. according to their disease state. This combinatory workflow is not adapted to a specific illness but is usable for all kinds of diseases that can be characterized using single molecule fluorescence microscopy.

引用

页码：310 / 317

页数：8

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