Image Pattern Recognition Techniques Research Articles

Pattern recognition of nucleated cells from the peripheral blood

A data set consisting of 2259 nucleated cells from the peripheral blood, including immature cells, was systematically investigated using image processing and pattern recognition techniques. A total of 94.2% of all cells could be correctly classified into one of 9 possible main classes. Several subsets were also investigated. In particular, the cell types in the neutrophilic maturation sequence were extensively explored.

A hematology data-logging terminal.

Intended as a link between a central analysis facility and remote microscope analyzers at international sites, a data-logging terminal has been developed which exploits basic analyzer control and data acquisition capabilities. The terminal depends on the analyzer's ability to assimilate, interpret, and respond to external commands transferred through its standard communications interface. Within the limits of the analyzer's high-level control language, the data-logging terminal is able to direct microscope operation and data acquisition as well as to initiate algorithms. Internal instrument algorithms can then establish and maintain focus, acquire specified objects, direct various scene segmentation sequences, and execute feature extractions. The data-logging terminal can thus command the analyzer to find scenes of interest and extract data from them. User access to a limited instrument operating interface is provided through its keyboard and alphanumeric display. The terminal can also request and receive image, feature, or count data over the communications interface. Such data are recorded, together with identifiers entered through the keyboard, onto diskettes which may be transferred by mail or telecommunications for processing with the image analysis and pattern recognition techniques available at the central analysis facility. Anticipated applications include evaluation of sample-preparation techniques, correlation of image data with sampling conditions, training of special classifiers, and analysis of instrument performance.

Leukocyte image analysis in the diff3 system

The diff3 System uses image processing and pattern recognition techniques to automatically analyze normal and abnormal white blood cells in a blood smear. The system consists of a spinner which creates a monolayer of cells on a glass slide, a stainer utilizing Wright's stain, the reagents to support the spinner and stainer, and an analyzer for automatic slide handling, analysis and report generation. The analyzer incorporates a wide range of image processing functions, including the generation and storage of gray scale image data, whole-field and partial-field image histogramming, and high-order binary image texture analysis and image transformation using the Golay processor (GLOPR). This paper describes the manner in which these hardware capabilities are used for white cell acquisition, scene segmentation and feature analysis. It concludes with some examples of texture extraction which illustrate the power of the Golay processor as a tool for image analysis.

Computer analysis of cervical cells. Automatic feature extraction and classification.

A prescreening instrument for cervical smears using computerized image processing and pattern recognition techniques requires that single cells in the specimen can be automatically isolated and analyzed. This paper describes a dual wavelength method for automatic isolation of the cytoplasm and nuclei of cells. Density-oriented, shape-oriented and texture-oriented parameters were calculated and evaluated for more than 600 cells. It is shown that the computer can be taught to distinguish between normal and atypical cells with an accuracy of ca. 97%, while human classification reproducibility is ca. 95%. In addition, an attempt to assign a measure of atypia to individual cells is described.