Shape features for recognition of Pap smear cells

Shelly D. D. Goggin; Scott D. Janson

doi:10.1117/12.253447

8 October 1996 Shape features for recognition of Pap smear cells

Shelly D. D. Goggin, Scott D. Janson

Proceedings Volume 2823, Statistical and Stochastic Methods for Image Processing; (1996) https://doi.org/10.1117/12.253447
Event: SPIE's 1996 International Symposium on Optical Science, Engineering, and Instrumentation, 1996, Denver, CO, United States

Abstract

Automated cytology relies on the use of features extracted form cell images to classify cells. This paper examines the classification capability of a number of shape features on a database of normal, abnormal and endocervical cell nuclei images. The features include the chain code, the directed Hausdorff distance, measured of the length of the radii of the cell and measures of ellipticity. The area under the receiver operating characteristic curve is used as a figure of merit. For the calculation of the directed Hausdorff distance, the images are filtered using the Sorbel gradient and erosion. The feature in the image with the largest chain code is considered to be the nucleus. The other features use images threshold at a percentage of the maximum intensity in the image. The best feature for the discrimination between normal cells and either abnormal or endocervical cells was the directed Hausdorff distance, but this feature is computationally expensive. The minimum diameter as determined by the chain code was the second best feature for recognizing abnormal cells and is less computationally expensive. Ellipticity was the second best feature for recognizing endocervical cells, which is also less computationally expensive than the directed Hausdorff distance. An optical design for the calculation of directed Hausdorff distance feature is included, which could reduce the computational expense.

Citation Download Citation

Shelly D. D. Goggin and Scott D. Janson "Shape features for recognition of Pap smear cells", Proc. SPIE 2823, Statistical and Stochastic Methods for Image Processing, (8 October 1996); https://doi.org/10.1117/12.253447

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