miQglia

A set of MATLAB scripts for quantification of microglia images acquired through brightfield microscopy.

Image processing and enhancement

Marker-controller watershed segmentation of microglia

Fractal analysis

Image Processing

Input images are preprocessed to enhace SNR for better cell segmentation. The current preprocessing steps are:

1. Conversion to maximum intensity projection (MIP)
2. Denoising with 2D Weiner filter
3. Background feature removal with a top-hat filter
4. FFT bandpass filter
5. Image sharpening

The culmination of image processing in this order yielded the best segmentation results.

Watershed Image Segmentation

Steps in marker-controlled watershed segmentation

The processed image is subtly blurred with a Gaussian filter, using the MATLAB function imgaussfilt. A microglia mask (MM) is then created by thresholding the Gaussian smoothed image, which is also subsequently used to create a soma seed mask (SSM) by applying a higher threshold to demarcate bright regions likely to be soma. Regions in SMM were shrunk to point-pixel and regions outside of MM were removed to identify and index soma locations (SL). Then, a composite image is created for marker-controlled watershed segmentation by imposing SL onto inverted gaussian-smoothed as regional minima. The composite image undergoes watershed segmentation to identify microglia. Microglia touching the border or not overlapping with SL are first removed, then eroded by two pixels to clean up watershed segmentation results. Microglia are then quantified by metrics in the table below and any microglia with exclusion criteria are rejected and removed.

Microglia Quantification

Skeleton Analysis

Metrics are based on skeletonized microglia to capture their complicated network process

1. BranchPoints

Total number of branch points detected in microglia.

2. EndPoints

Total number of end points detected in microglia.

3. Branches

Total number of branched segments in image.

4. MaxBranchLength

Length of the largest branch found in microns (μm).

5. AverageBranchLength

Average branch length in microns (μm).

6. TotalBranchLength

Sum of all processes lengths on microglia.

Fractal Analysis

A set of measures to quantify complexity of microglia structure.

1. Cell Area

Area of pixels occupied by cell in microns (μm2) i.e., area of microglia. Also known as cell area (CA).

2. Cell Perimeter

Distance around the boundary of microglia in microns (μm) i.e. perimeter of microglia. Also known as cell perimeter (CP).

3. Fractal Dimension

Unitless measure of microglia complexity

4. Cell Circularity

Roundness of microglia. A perfect circle has a value of 0, whereas an ellipsoid has a value of 1.

5. Span Ratio

Ratio of convex hull ellipse’s minor length to major length, a measure of microglia elongation

6. Density

Ratio of cell area (CA) to the convex hull area (CHA). This measure is an indicator of cell size. The higher the density, the smaller the microglia.

7. Convex Hull Area

Convex hull is the smaller convex polygon that containing the whole microglia. All interior angles of a convex hull are smaller than 180ᵒ. The convex hull area (CHA) is the area of pixels occupied by convex hull in microns (μm).

8. Convex Hull Perimeter

Distance around the boundary of convex hull in microns (μm) i.e. perimeter of convex hull.

9. Convex Hull Circularity

Roundness of convex hull. A perfect circle has a value of 0, whereas an ellipsoid has a value of 1.

10. Roughness

Ratio of cell perimeter (CP) to convex hull perimeter (CHP).

Usage

Change the variable pixel_micron in L31 of ramificationStats.m to your pixel to micron ratio. Then, call the function ramificationStats('Path to input image', 'Path to output directory') to compute, process, and quantify an image containing microglia.

Example:

>> cell_stats = ramificationStats("D:\Datasets\Morphology\data\Benchmark\Input\w1t5m18_s20_40x_Sub2.TIF",'D:\SystemFiles\siddh\\Projects\\Mouse_Histology\Output');