Roughness feature of metaphase chromosome spreads and nuclei for automated cell proliferation analysis

Automated identification of analyzable metaphase chromosomes depicted on microscopic digital images

Visual search and identification of analyzable metaphase chromosomes using optical microscopes is a very tedious and time-consuming task that is routinely performed in genetic laboratories to detect and diagnose cancers and genetic diseases. The purpose of this study is to develop and test a computerized scheme that can automatically identify chromosomes in metaphase stage and classify them into analyzable and un-analyzable groups. Two independent datasets involving 170 images are used to train and test the scheme. The scheme uses image filtering, threshold, and labeling algorithms to detect chromosomes, followed by computing a set of features for each individual chromosome as well as for each identified metaphase cell. Two machine learning classifiers including a decision tree (DT) based on the features of individual chromosomes and an artificial neural network (ANN) using the features of the metaphase cells are optimized and tested to classify between analyzable and un-analyzable cells. Using the DT based classifier the Kappa coefficients for agreement between the cytogeneticist and the scheme are 0.83 and 0.89 for the training and testing datasets, respectively. We apply an independent testing and a 2-fold cross-validation method to assess the performance of the ANN-based classifier. The area under and receiver operating characteristic (ROC) curve is 0.93 for the complete dataset. This preliminary study demonstrates the feasibility of developing a computerized scheme to automatically identify and classify metaphase chromosomes.

Development of a multistage classifier for a monitoring system of cell activity based on imaging of chromosomal dynamics

BACKGROUND

Cell-based assays utilizing digital image cytometry yield multivariate sets of information measuring the efficacy of medicines/chemicals. The use of a HeLa cell line that expresses a GFP-Histone-H1 fusion protein further enhances the performance of these systems, avoiding the use of dyes that may have detrimental influence on cells. Aside from the mitotic index, the distribution of the cell-cycle phases during mitosis can be used as measures of drug/treatment efficacy. Quantification of these parameters, however, requires skill and is time consuming. The purpose of this research was therefore to create a classifier to be incorporated into a system that can automatically identify the cell-cycle phases in a given image.

METHODS

Features based on the shape and texture of the chromosomal regions in images of live HeLa cells were measured and analyzed. Linear discriminant functions were calculated for the eight cell-cycle phases: interphase, prophase, prometaphase, metaphase, early anaphase, anaphase, telophase and cytokinesis.

RESULTS

The multistage linear discriminant classifier developed had an average classification efficiency of 87.30%.

CONCLUSION

We demonstrated the possibility of creating a classifier to discriminate between cell-cycle phases using shape and texture features of chromosomal regions. The classifier can be fused to an algorithm for image segmentation, forming a system to automatically and rapidly measure the aforementioned parameters. The results can then be collated to constitute an assay assessing the effects of a drug or treatment on mammalian cells.

Textural assessment in digital mammograms

This work focuses on testing textural and morphological parameters to assess characteristic features of digital mammograms. The selected images were radiological studies from the Institute Nacional de Cancerologia in Mexico City, evaluated as BI-RADS 4 or 5, meaning "probably malign" or "malign" findings, respectively. All patients were subjected to a biopsy procedure after the image was taken. The study group consisted of patients diagnosed with cancer, while the control group included those without cancer. We propose to analyze textural roughness by the mean height-width ratio of extrema (MHWRE) and morphological features by circularity. Results show good differentiation (correct diagnosis) for 46% of the images, bad differentiation (wrong diagnosis) for 25%, and undetermined diagnosis for 29% of the cases.

Inter-chromosome texture as a feature for automatic identification of metaphase spreads

This paper reports results for a new measure of texture coarseness, as a step towards automation of metaphase finding in cell-proliferation studies. This measure is highly specific to grey-level inter-chromosome coarseness features in microscopic images of metaphase spreads and allows the texture quantification of cytological objects, analysing the intensity profile between chromosome-extrema samples. Chromosome fragments produce patterns of pixels at low resolution, and the local neighbourhood of their individual extrema presents a characteristic coarseness along intensity profiles, on randomly oriented test lines. Results of the use of this new measure on microscope images of fields of metaphases and artifacts are compared with some representative texture measures and the performance of reported metaphase finders. This new measure outperforms the latter, when applied in metaphase detection and elimination of artifacts. This coarseness feature provides a specific metaphase signature that can be used in conjunction with other morphological and textural parameters for automated metaphase discrimination.

Technical report: application of the Metafer2 fluorescence scanning system for the analysis of radiation-induced chromosome aberrations measured by FISH-chromosome painting

The Metafer2 fluorescence scanning system was used for routine analysis of radiation-induced exchange aberrations measured by fluorescence in situ hybridisation (FISH) chromosome painting in human peripheral lymphocytes. The system enables a rapid and unbiased fully-automated finding and image acquisition of fluorescently stained metaphase spreads. The chromosome aberration analysis is performed interactively from stored digitised processed gallery images, presented on a screen. Appropriate software image filters are available to further improve these pictures by background correction, noise reduction and fluorescence signal enhancement. Data sets generated by computer-assisted and manual scoring of radiation-induced reciprocal translocations (2B) and total 2B (2B+related 'one-way' types) or complete dicentrics (2A) and total 2A (2A+related 'one-ways') involving painted target chromosomes 2, 3 or 4 were compared and no significant differences were found.A linear-quadratic dose-response curve for total translocations (2B+'one-ways'+complex-derived types) based on computer-assisted analysis of 27,741 metaphases with chromosome 4 painting was compared to a curve obtained earlier for manually scored translocations in a set of target chromosomes 1, 4 and 12. After extrapolation to the whole genome, no significant difference between both curves was found. From our results it can be derived that computer-assisted aberration analysis using the Metafer2 system is a reliable alternative to manual analysis. Since time saving for computer-assisted translocation analysis is about 50% compared to manual scoring, this system is highly promising for a practical application in retrospective biodosimetry of human radiation exposure.

Automatic identification of metaphase spreads and nuclei using neural networks

The mitotic index (MI) is an important measure in cell proliferation studies. Determination of the MI is usually made by light-microscope analysis of slide preparations. The analyst identifies and counts thousands of cells and reports the percentage of mitotic shapes found among the interphase nuclei. Full automation of this process is an ambitious task, because there can exist very few mitotic shapes among hundreds of nuclei and thousands of artifacts, resulting in a high probability of false positives, i.e. objects erroneously identified as mitosis or nuclei. A semi-automated approach for MI calculation is reported, based on the development of a neural network (NN) for automatic identification of metaphase spreads and stimulated nuclei in digital images of microscope preparations at 10X magnification. After segmentation of the objects on each image, ten different morphometrical, photometrical and textural features are measured on each segmented object. An NN is used to classify the feature vectors into three classes: metaphases, nuclei and artifacts. The system has been able to classify correctly approximately 91% of the objects in each class, in a test set of 191 mitosis, 331 nuclei and 387 artifacts, obtained from 30 different microscope slides. Manual editing of false positives from the metaphase classification results allows the calculation of the MI with an error of 6.5%.