A scene-segmentation method for multispectral cell images.

Y Noguchi; Y Tenjin

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PubMed · 6210008

A scene-segmentation method for multispectral cell images.

Y Noguchi·Y Tenjin

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Abstract

A scene-segmentation method for two-color digitized images acquired from a Papanicolaou-stained cervical smear is proposed. The method first segments a scene into background, red cytoplasm, blue cytoplasm and nuclear regions by a pixel-wise classification and then merges the segmented regions for both types of cytoplasm into a single region. To create the minimum-distance classifier used for the pixel classification, class median vectors are selected from a two-dimensional histogram formed from the optical densities in the red and green images (scanned at 610 nm and 535 nm, respectively). Reference points defined from knowledge about the two-color images played an important role in selecting the vectors for the red and blue cytoplasm. This method was applied to 33 sets of the two-color images. The resulting segmented regions corresponded well with regions apparent to the the human observer. Three different investigations related to the method were carried out; these studies confirmed the suitability of the proposed method.

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Cell Nucleus Cytoplasm Female Humans Papanicolaou Test Risk Staining and Labeling Uterine Cervical Neoplasms Vaginal Smears Journal Article Research Support, Non-U.S. Gov't

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Y Noguchi, Y Tenjin. 1984. A scene-segmentation method for multispectral cell images.. https://pubmed.ncbi.nlm.nih.gov/6210008/

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