2D Spatial Filtering Images of Cardiac Cells for Nuclei Isolation

Jessica E. Herrmann, Ha D.H. Le, Theresa C. RizkBE110: Physiological Systems Analysis, School of Engineering and Applied Sciences, Harvard University, Cambridge, MA

ABSTRACT

METHODS

RESULTS

RESULTS

DISCUSSION

REFERENCES

ACKNOWLEDGEMENTSWe wish to acknowledge: the Shiu Laboratory at the University of Utah for providing the images; Al-Khazraji et al for providing their automated cell-counting algorithm for academic purposes; Rajan and Kannan for their program of creating a generalized Sobel operator; our trained users for their contributions; and the teaching staff of Biomedical Engineering 110.  

• Reliably quantifying nuclei in an image of cells to determine cellular density can lend insight into how diseases affect organ tissue.

• Low quality and high variability among image samples result in inaccurate and inconsistent nuclei counts when manually counting or utilizing unoptimized software algorithms

• Our goal was to develop a 2D spatial filter that could isolate nuclei from images of cardiac cells and software for accurately quantifying cells

• We compared three filtering methods (Laplacian of the Gaussian, Gaussian Edge Detection, and binary thresholding) against initial user counts to determine that Gaussian edge detection yielded counts that were most consistent with the user data

• We applied color deconvolution and a selective median filter to the cardiac cell images in ImageJ prior to applying a Gaussian smoothing filter and a Sobel operator for edge detection in MATLAB

• Our cell counts fell inside the 95% confidence intervals generated from our user counts, but not all images resulted in statistically significant data (<5% error)

• These results suggest that our algorithm shows promise for success, but that we must obtain more trained users to further optimize our algorithm

Figure 10. Nuclei counts as found by (A) the trained user count average and (D) the 2D spatial filter developed. • The high standard error of the mean

values (B) suggests variability among the trained users.

• While some images have under 5% error (E), other images are above the 5% error threshold.

• This suggests that several of our software-generated counts were not statistically significant.

• Software-generated counts across all 8 images fell inside the 95% confidence interval (C) based on user counts with a t-test analysis, indicating promise for our algorithm.

a. Application of color deconvolution, selective median filter, Gaussian smoothing filter, and Sobel operator for edge detection results in promising nuclei counts when compared to trained user counts, though improvements remain

b. Results from statistical analysis suggest that a greater trained user sample size might improve optimization protocols

c. Optimizing our 2D spatial filter would also require: a more rigorous protocol for training users; a better method for the conversion binary; further testing of the sigma values for the Gaussian smoothing filter; and greater consideration of discrepancies and errors in the image

d. An elaboration of this project would be to use our optimized nuclei counts and resulting cellular density values to determine types of cells in diseased tissue image samples

Figure 11. A comparison of the nuclei counts generated by our 2D spatial filter and the average nuclei counts from our trained users (including error bars for standard error of mean). While some images (such as Image 1) had close counts between the trained user average and software-generated numbers, others were more variable (such as Image 6).

• Images with higher percent errors were often marked with more irregularities (clumps of nuclei or photographical errors, see fig. 13).

• The variability in user counts and %error (figures 14 and 15) demonstrate the human bias inherent in manual counting of images, which we aim to minimize with our standardized and numerical system.

Figure 13. Photographical anomaly found in several images.

Figure 12. A compilation of our optimized parameters for the different steps of our filtering process.

Trained Users 3 and 5 had particularly high nuclei counts compared to other trained users

Figure 14.A comparison of the nuclei counts from 6 trained users across the sample of images (including errors bars for standard error of mean).

Figure 15. Graph of Feedback Loop system results testing a range of threshold values to optimize binary conversion filter and minimize % error.

Figure 1: Protocol used to train users in accurately identifying nuclei.

Figure 3. Application of color deconvolution in ImageJ to isolate purple stained nuclei

Figure 4. Selective median filtering in ImageJ to remove outliers.

A) Accrue nuclei counts from trained users1. Develop and apply protocol for training users

(Figure 1)2. Obtain trained user data in the form of manual

nuclei counts

B) Isolate purple stained nuclei from the cell images3. Apply color deconvolution for a hematoxylin and

eosin stain to isolate the purple entities (Figure 3)4. Apply a selective median filter to remove outliers

(Figure 4)5. Optimize the deconvolution and median filter

parameters

C) Gaussian Edge Detection6. Apply a Gaussian smoothing filter to remove

noise (Figure 5a; 6a)7. Apply a Sobel operator to detect nuclei edges

(Figures 4b; 6b, c)8. Optimize the Gaussian filter dimensions (Figure 6)

Denoised Image Gaussian Filtered Image Edge Detected Image

Figure 5: Further noise elimination with a Gaussian smoothing filter (a) and edge detection with a Sobel operator (b).

Figure 6: Examining different Gaussian filter dimensions to select the ideal matrix size.

Figure 7: Convolution masks used for the Gaussian smoothing filter (a) and the Sobel operator (b, c).

Figure 8: Detection of connected components on edge-detected image and labeling with increasing numbers.

D) Nuclei Quantification1. Threshold to convert to binary image (Figure 8)2. Detect connected components of image3. Label and quantify each connected component4. Divide the total pixel area of the connected components

by the average pixel area of a single component to calculate the total number of nuclei

E) Statistical Analysis5. Calculate standard error of the mean for trained users6. Define a 95% confidence interval for the mean using the

sample mean7. Calculate the percent error for automated nuclei counts8. Use a feedback loop (Figure 9) to optimize the filter

based on percent error calculations at different binary threshold values

Figure 9: Feedback loop to describe the optimization of the binary image using the im2bw function in MATLAB.

Im 1 Im 2 Im 3 Im 4 Im 5 Im 6 Im 7 Im 80

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% Error for Different Threshold Values

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Comparing nuclei-detection software against trained users

Laplacian of the Gaussian Gaussian Edge Detection Binary Thresholding Average

H&E Stained Images of Rat 90 Heart by Quadrant

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Figure 2. Comparison of 3 filtering methods yielded preference for Gaussian Edge detection

• Al-Khazraji, B. K., Medeiros, P. J., Novielli, N. M., & Jackson, D. N. (2011). An automated cell-counting algorithm for fluorescently-stained cells in migration assays. Biological Procedures Online, 13, 9. http://doi.org/10.1186/1480-9222-13-9.

• Fisher, R., Perkins, S., Walker, A., and Wolfart, E. (2003). Gaussian Smoothing. Retrieved from http://homepages.inf.ed.ac.uk/rbf/HIPR2/gsmooth.htm.

• Fisher, R., Perkins, S., Walker, A., and Wolfart, E. (2003). Sobel Edge Detector. Retrieved from http://homepages.inf.ed.ac.uk/rbf/HIPR2/sobel.htm.

• Rajan, J. and Kannan, K. (2008). Generalised Sobel Filter for Edge Detection. Retrieved from http://www.mathworks.com/matlabcentral/fileexchange/21344-generalised-sobel-filter-for-edge detection/content/GSobel.m.

Sobel Operator (X-Direction)

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