Shape features for recognition of Pap smear cells

Shape features for recognitionof Pap smear cellsShelly D.D. Goggin and Scott D. JansonUniversity of Colorado at Denver, Department of Electrical Engineering and the Optoelectronic ComputingSystems CenterCampus Box 1 10, P.O. Box 173364, Denver, CO 80217-3364sgoggin@carbon.CUDenver.EDUABSTRACTAutomated cytology relies on the use of features extracted from cell images to classify cells. This paperexamines the classification capability of a number of shape features on a database of normal, abnormal andendocervical cell nuclei images. The features include the chain code, the directed Hausdorff distance, measures ofthe length of the radii of the cell and measures ofellipticity. The area under the receiver operating characteristic(ROC) curve is used as a figure of merit. For the calculation of the directed Hausdorff distance, the images arefiltered using the Sorbel gradient and erosion. The feature in the image with the largest chain code is consideredto be the nucleus. The other features use images thresholded at a percentage of the maximum intensity in theimage. The best feature for the discrimination between normal cells and either abnormal or endocervical cells wasthe directed Hausdorff distance, but this feature in computationally expensive. The minimum diameter asdetermined by the chain code was the second best feature for recognizing abnormal cells and is lesscomputationally expensive. Ellipticity was the second best feature for recognizing endocervical cells, which isalso less computationally expensive than the directed Hausdorff distance. An optical design for the calculation ofdirected Hausdorff distance feature is included, which could reduce the computational expense.KEY WORDS: Shape estimation, mathematical morphology, medical image processing, patternclassification.1. INTRODUCTIONThis paper examines a number of pattern recognition techniques to motivate the development of' opticalprocessing systems for automated diagnostic cytology. Diagnostic cytology is based on high-resolution image thta,which challenges the capabilities of electronic computers. A potential exists for tremendous speed increases fromthe exploitation of the parallel communication capabilities of optics.The application of optical computation to diagnostic cytology has the advantage of having the data initially ina transparent format which could be read into an optoelectronic system without electronic intervention. The cellsare on a transparent microscope slide that can be read by an incoherent light source and converted to a coherentcarrier using an optically addressed spatial light modulator (OASLM). In addition, for some optical processingsystems, "smart" detector arrays, with processing circuits part of the detector plane, can also reduce the bandwidthoutput to electronics for the final stages of processing, display and storage.The diagnostic cytology problem with the largest applicability is the analysis of Pap smears slides for thediagnosis of cervical cancer [1]. Our group has developed an optical system that can effectively identify regions ofinterest (ROIs) in a Pap smear slide [2]. The purpose of the research presented here is to refine the identificationof the ROIs. We have approached the ROl identification problem in a comprehensive manner, using nine patternrecognition techniques with innovative modifications. Receiver operating characteristic (ROC) curves [3] are usedto evaluate the relative merits of the features on a test set of cells from Pap smears. The database of Pap smear cellsused includes samples of normal, abnormal and endocervical cells from multiple slides. In future work, the morepromising techniques will be applied to more slides in our database of 2440 images.This paper presents a systematic comparison of shape techniques for the automated cytology problem, inaddition to presenting an optical systems for improving performance of automated cytology systems. Section 1.1224/SPIE