1. • Less than 5% error when
comparing nuclei count
from MATLAB with the
nuclei count from the
trained user
• Goals:
1. Minimize error
2. Decrease manual
hours computing
nuclei count
2D Spatial Filtering Images of Cardiac Cells for Nuclei Isolation
Jessica E. Herrmann, Ha D.H. Le, Theresa C. Rizk
Efficiently and accurately measuring nuclei count can provide a better understanding of changes in the
heart due to disease progression
GENERAL PROCEDURE:
1. Optimize 2D spatial filter in ImageJ
2. Run the filter and use MATLAB to count the nuclei
3. Have a trained user manually count the nuclei
4. Perform statistical analysis to determine error
ASSUMPTIONS AND LIMITATIONS:
• Limitation or assumption.
• Another limitation or assumption.
• Image analysis can provide
quantifiable data (e.g.: cell density)
• It is deterred by low quality images
that are often varied across samples
• How can we develop a filter to isolate
nuclei from an image of cardiac cells
in order to enhance quantification of
the nuclei?
EXPECTEDOUTCOMEIMPACT
METHODOLOGY
PROBLEMCONCEPT
Hypothesis: Fourier transforms
and image processing software
can be used to develop a 2D
spatial filter that will isolate the
nuclei in images of cardiac cells.
Signs of
inflammatio
n
Cell thickness
Ventricular
wall thickness
How does chronic kidney
disease affect the heart?
Counting nuclei
more accurately
Cell
Density
Convolution Mask
Potential Filters
Laplacian of the Gaussian
1. Gaussian filter to remove noise
2. Fourier Transform to sharpen
3. Laplacian to detect edges
Gaussian Edge Detection
1. Gaussian filter to remove noise
2. Fourier Transform to sharpen
3. Gradient
Threshold Filtering of Binary Image
1. Convert RGB to grey scale
2. Threshold to produce binary image
3. Erosion and dilation
h[i, j]
x[i, j] y[i, j]
?
Countx

2. Color deconvolution
Updates and Progress
• Color deconvolution of Image
• Laplacian of the Gaussian
1. Apply “despeckle” filter to de-noise
2. Remove outliers
3. Gaussian filter
4. Apply Laplacian filter to the image [this filter only works in Fiji]
5. Run MATLAB image analysis
• Gaussian Edge Detection
1. Steps 1-3 above
2. Apply “find edges” (Sobel edge detector,
uses gradient of image intensity function)
3. Run MATLAB image analysis
• Threshold Filtering
1. Convert convoluted image to binary
2. Erode and dilate to enhance image
3. Apply Sobel edge detector
4. Run MATLAB image analysis
Laplacian Results: 778 nuclei counted
Gaussian Results: 395 nuclei counted
Threshold Filtering Results:
Gaussian edge Detection
MATLAB script to count the objects
First increases the contrast of the
image
Converts grayscale to binary image by
using
thresholding
Further removes background noise
Counts objects in the image

3. Obstacles
• No method for testing whether nuclei are being eroded through
filtering
• Nuclei touching in image can
be perceived as single entity
• Balance between decreasing
noise and maintaining integrity of object edges
• Altering size of nuclei – misleading data
• Some noise still remains after filtering and can be counted as nuclei