Jan Holmquist

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.

High resolution segmentation of cervical cells.

A major problem in the automation of cervical cytology screening is the segmentation of cell images. This paper presents the present status of the work on that problem at the University of Uppsala. A dual resolution system is used. Suspect malignant cells are located at 4 mu resolution. Each such cell is rescanned at 0.5 mu resolution at two different wavelengths, 530 and 570 nm. The nucleus and the cytoplasm are isolated each by two independent methods. For the nucleus adaptive thresholding in the histogram of the 570 nm image and a contouring in a radially transformed version of that image is used. For the cytoplasm a two dimensional thresholding in the 2D histogram and a contouring in a radially transformed version of the 530 nm image is used. If the two nuclear masks agree the surrounding area is checked for disturbing objects. If also the cytoplasm masks agree and are without disturbing objects the whole cell is accepted. The result of the cytoplasm masks agree and are without disturbing objects the whole cell is accepted. The result of the segmentation is thus three categories; free cells, free nuclei and rejected objects. The shape of the objects belonging to the former two categories is checked and irregularly shaped ones are rejected as probably consisting of several overlapping nuclei. Cells passing also this test are classified as normal or malignant. The experience from using this algorithm is discussed and areas for further research are pointed out.

Segmentation of cervical cells: Detection of overlapping cell nuclei☆

Abstract A method for detecting overlapping cell nuclei in Pap-stained cervical smears is described. The algorithm uses information both from the nuclear contour and from the density profile of the nucleus. For the analysis of the nuclear contour the smoothed difference chain code is used. From this code any significant concavities along the contour are found and a number of features describing their size and relative location are computed. If these clearly indicate an overlap situation the object is classified as an overlap. Otherwise a density profile is generated along a line orthogonal to the line joining the two major concavities. This profile is checked for peaks and valleys indicative of an overlap situation and a new set of features are generated and used to classify the object as single or overlapping. The algorithm performed reasonably well when tested on an independent test set of about 240 cell images.

A microspectrophotometric study of Papanicolaou-stained cervical cells as an aid in computerized image processing.

As part of a study of cytologic automation, microspectrophotometric investigation of Papanicolaou-stained cervical cells was performed, using a Leitz MPV-II scanning photometer connected to a PDP 8/F minicomputer. It was shown that the selection of one single wavelength may result in difficulties in detecting boundries between background and cytoplasm and/or between cytoplasm nucleus. A set of two wavelengths, 530 nm and 570 nm, were found to be optimal for the image processing of these cells.

SCANCANS — An interactive scanning cell analysis system☆

This paper describes a computerized, general-purpose, interactive, scanning cell-analysis system developed at the Department of Clinical Cytology of the University Hospital in Uppsala. The system uses a Leitz MPV II scanning photometer, a PDP-8 computer with 24 k of core memory and a Tektronix 4010 graphic display terminal. A command interpreter and overlay structure which makes it simple to add new capabilities to the system is described. Some examples of command strings and the kind of operations the system can perform are included. Finally, the experience gained from using the system and some plans for future extensions are discussed.

A program system for interactive measurements on digitized cell images.

Using a high precision image scanner and a PDP-8/F minicomputer, we have developed a program system for interactive measurements on microscopic images. By giving simple keyboard commands, the operator can run the image scanner and manipulate the digitized images. The interface between the operator and the microscope-computer system is a Tektronix 4010 graphic terminal. The system allows objects to be isolated and parameters to be calculated from each object, e.g., parameters characterizing shape of the object, irregularity in light transmission over the object, area, integrated light transmission, etc. Objects are isolated and parameters are calculated under complete operator control using interactive computer graphics technique. Calculated parameters may be stored in dedicated data records, which are stored in files for later statistical analysis. The system also includes a statistical evaluation part. Technically, the system consists of a command scanner, which translates commands into internal representation, a parser, which checks the syntax of the commands, and an interpreter, which executes the commands. The system is designed so that new commands can be added easily.

A software system to record and analyze digitized cell images

This paper describes basic software for digitization and processing of microscopic cell images used at the Department of Clinical Cytology at Uppsala University Hospital. A family of programs running on a PDP-8 minicomputer which is connected to a Leitz Orthoplan microscope with two image scanners, one diode-array scanner and a moving-stage photometer, is used for data collection. The digitized image data is converted by converted by conversion program to IBM compatible format. The data structures for image processing and statistical evaluation on the IBM system are also described. Finally, some experiences from the use of the software in cytology automation are discussed.

Analysis of grey-level histograms as a method of classifying cells from the uterine cervix

Abstract An accurate single-cell classifier is an essential part of any system that uses image-processing and pattern-recognition techniques for prescreening cervical smears. The cell classifier, however, is heavily dependent on the feature extraction, which must be done automatically. This paper describes an attempt to use the grey-level histogram from a cell as a feature vector, in order to simplify part of the automatic feature-extraction process.