Paper
19 June 2003 CytometryML: a markup language for analytical cytology
Robert C. Leif, Stephanie H. Leif, Suzanne B. Leif
Author Affiliations +
Abstract
Cytometry Markup Language, CytometryML, is a proposed new analytical cytology data standard. CytometryML is a set of XML schemas for encoding both flow cytometry and digital microscopy text based data types. CytometryML schemas reference both DICOM (Digital Imaging and Communications in Medicine) codes and FCS keywords. These schemas provide representations for the keywords in FCS 3.0 and will soon include DICOM microscopic image data. Flow Cytometry Standard (FCS) list-mode has been mapped to the DICOM Waveform Information Object. A preliminary version of a list mode binary data type, which does not presently exist in DICOM, has been designed. This binary type is required to enhance the storage and transmission of flow cytometry and digital microscopy data. Index files based on Waveform indices will be used to rapidly locate the cells present in individual subsets. DICOM has the advantage of employing standard file types, TIF and JPEG, for Digital Microscopy. Using an XML schema based representation means that standard commercial software packages such as Excel and MathCad can be used to analyze, display, and store analytical cytometry data. Furthermore, by providing one standard for both DICOM data and analytical cytology data, it eliminates the need to create and maintain special purpose interfaces for analytical cytology data thereby integrating the data into the larger DICOM and other clinical communities. A draft version of CytometryML is available at www.newportinstruments.com.
© (2003) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Robert C. Leif, Stephanie H. Leif, and Suzanne B. Leif "CytometryML: a markup language for analytical cytology", Proc. SPIE 4962, Manipulation and Analysis of Biomolecules, Cells, and Tissues, (19 June 2003); https://doi.org/10.1117/12.486320
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Cited by 9 scholarly publications.
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KEYWORDS
Fluorescence correlation spectroscopy

Cell biology

Sensors

Binary data

Flow cytometry

Chemical elements

Data modeling

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