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A Novel Approach to Automated Cell Counting for Studying Human Corneal Epithelial Cells

This research paper presents a novel method for automated cell counting in human corneal epithelial cells. The study focuses on identifying different cell types in grayscale images and overcomes challenges like fading dyes and cell clustering. The proposed technique shows superior performance compared to existing methods and has potential for future improvements.

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A Novel Approach to Automated Cell Counting for Studying Human Corneal Epithelial Cells

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  1. A Novel Approach to Automated Cell Counting for Studying Human Corneal Epithelial Cells NamrataBandekar, Alex Wong, David Clausi and Maud Gorbet

  2. Outline • Background • Problem Description • Methodology • Results • Conclusions

  3. Background • Study on biocompatibility of lens cleaning solutions and lens materials • Interaction of multi-purpose solutions (MPS) with eye cells • Measure cell viability and cell activation • Human corneal epithelial cells (HCECs) acquired for in vitro study

  4. Problem Description • Identify the number of different types of cells in the grayscale images • Resolution: 1388x1040 • Scale: approximately 1 μm/pixel *Images obtained from Prof. Gorbet

  5. Challenges • The dye fades with time • Fading time: Order of a few minutes • Cannot use global thresholding • Cell clustering • Noise • Debris • Cell shape and size • Cannot use morphology

  6. Methodology - Overview

  7. Methodology • Detect the nuclei (local maxima) • Local thresholding • Non-maximum suppression *J. Canny, “A computational approach to edge detection,” Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. PAMI-8, no. 6, pp. 679 –698, 1986.

  8. Methodology • Use nucleus and background seeds • Seeded region growing • Overgrows regions * R. Adams and L. Bischof, “Seeded region growing,” Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 16, no. 6, pp. 641 –647, 1994.

  9. Methodology • Cluster separation • Process overgrown regions • Use an adaptive thresholding technique for the clusters

  10. Methodology • Cell body • Get seeds by considering neighbours of nuclei • Apply seeded region growing for 3 classes: background, nucleus and cell body

  11. Testing Criterion • F1-measure where and • Accuracy * C. van Rijsbergen, Information Retrieval (2nd ed.), Butterworth-Heinemann, Newton, MA, USA, 1979.

  12. Results • Performance compared to the distance regularized level set technique (DRLSE). • Run on a 2 GB RAM and 3.2 GHz machine. • Both algorithms implemented in MATLAB. *C. Li, C. Xu, C. Gui, and M. D. Fox, ”Distance Regularized Level Set Evolution and its Application to Image Segmentation”,IEEETransactions on Image Processing, vol. 19, no. 12, pp. 3243-3254, 2010.

  13. Results • DRLSE fails to detect individual cells in clusters. Result of the proposed technique Original image DRLSE result

  14. Results • DRLSE cannot detect cells with low contrast between nucleus and cell body. Original image DRLSE result Result of the proposed technique

  15. Results • DRLSE fails to detect ghost cells. Original image DRLSE result Result of the proposed technique

  16. Results • Performance averaged over 27 images

  17. Results • The proposed algorithm fails in the following cases: • Overlapping ghost cells • Background illumination variation

  18. Conclusions • Greater than 90% accuracy for nucleated cells • Robust towards low contrast • Superior to state-of-the-art techniques such as DRLSE • Future Work • Use background illumination subtraction • Concavity or notch detection for ghost cell clusters • Classification of nucleated cells in to corneal cells and white blood cells

  19. Questions?

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