Segmentation of cytological images using color and mathematical morphology

Screening is a manual activity which involves its subjectivity. A semi-automated computer-based system could contribute to the detection of screening errors by the way of a greater reliability. We intend to design such a system operating on color images from serous cytology. The aim of this paper is to present the strategy of the first part of the system: segmentation. It is based on mathematical morphology tools such as watersheds using color infom1ation in several color spaces. An extension of watershed to an optimal region-growing operator has been used. A pool of cells has been evaluated by experts to score the segmentation success rate. All cells have been isolated whatever their spatial configuration may be. The average success rate is 94.5% for the nuclei and 93% for the cytoplasm. Our morphological color segmentation of cytological serous images is accurate and provides a good tool for the extraction of cells.

[1]  W. Tolles.  SECTION OF BIOLOGY: THE CYTOANALYZER—AN EXAMPLE OF PHYSICS IN MEDICAL RESEARCH* , 1955 .

[2]  Gunther Wyszecki,et al.  Color Science: Concepts and Methods, Quantitative Data and Formulae, 2nd Edition , 2000 .

[3]  H. Pauli.  Proposed extension of the CIE recommendation on “Uniform color spaces, color difference equations, and metric color terms” , 1976 .

[4]  Y. Imasato,et al.  Fundamental study of automatic cyto-screening for uterine cancer. III. New system of automated apparatus (CYBEST) utilizing the pattern recognition method. , 1977, Acta cytologica.

[5]  T. Kanade,et al.  Color information for region segmentation , 1980 .

[6]  Hans M. Aus,et al.  Combined local color and texture analysis of stained cells , 1986, Comput. Vis. Graph. Image Process..

[7]  Catherine Garbay,et al.  Image Structure Representation and Processing: A Discussion of Some Segmentation Methods in Cytology , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  L. Koss.  The Papanicolaou test for cervical cancer detection. A triumph and a tragedy. , 1989, JAMA.

[9]  Serge Beucher.  Segmentation d'images et morphologie mathématique , 1990 .

[10]  Luc Vincent,et al.  Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Quang-Tuan Luong.  1 - La couleur en vision par ordinateur : une revue , 1991 .

[12]  Quang-Tuan Luong,et al.  Color in Computer Vision , 1993, Handbook of Pattern Recognition and Computer Vision.

[13]  D L Rosenthal,et al.  Computer-assisted rescreening of clinically important false negative cervical smears using the PAPNET Testing System. , 1996, Acta cytologica.

[14]  E A Knesel.  Roche Image Analysis Systems, Inc. , 1996, Acta cytologica.

[15]  A. Nelson,et al.  NeoPath, Inc. NeoPath AutoPap 300 Automatic Pap Screener System. , 1996, Acta cytologica.

[16]  A Elmoataz,et al.  Generalized region growing operator with optimal scanning: application to segmentation of breast cancer images , 1997, Journal of microscopy.

[17]  Josef Kittler,et al.  Automatic watershed segmentation of randomly textured color images , 1997, IEEE Trans. Image Process..