SMLMBoxer

convolve(imagestack, kernel; use_gpu=false)

Convolve imagestack with given kernel.

Arguments

• imagestack: Input array of image data
• kernel: Kernel to convolve with

Keyword Arguments

• use_gpu: Whether to use GPU

Returns

• filtered_stack: Convolved image stack

dog_filter(imagestack, args)

Apply DoG filter to imagestack based on args.

Arguments

• imagestack: Input array of image data
• args: Arguments with sigma values

Returns

• filtered_stack: Filtered image stack

dog_kernel(s1, s2)

Compute difference of Gaussian kernels.

Arguments

• sigma_small: Sigma for small Gaussian
• sigma_large: Sigma for large Gaussian

Returns

• dog: Difference of Gaussians kernel

fillbox!(box, imagestack, row, col, im, boxsize)

Fill a box with a crop from the imagestack.

Arguments

• box: Array to fill with box crop
• imagestack: Input image stack
• row, col, im: Coords for crop
• boxsize: Size of box

Returns

• boxcoords: Upper Left corners of boxes N x (row, col, im)

findlocalmax(imagestack, kernelsize; minval=0.0, use_gpu=false)

Find the coordinates of local maxima in an image.

Arguments

• imagestack: An array of real numbers representing the image data.
• kernelsize: The size of the kernel used to identify local maxima.

Keyword Arguments

• minval: The minimum value a local maximum must have to be considered valid (default: 0.0).
• use_gpu: Whether or not to use GPU acceleration (default: false).

Returns

• coords: The coordinates of the local maxima in the image.

gaussian_2d(sigma, ksize)

Create a 2D Gaussian kernel.

Arguments

• sigma: Standard deviation
• kernelsize: Kernel size

Returns

• kernel: Normalized 2D Gaussian kernel

genlocalmaximage(imagestack, kernelsize; minval=0.0, use_gpu=false)

Generate an image highlighting the local maxima.

Arguments

• imagestack: An array of real numbers representing the image data.
• kernelsize: The size of the kernel used to identify local maxima.

Keyword Arguments

• minval: The minimum value a local maximum must have to be considered valid (default: 0.0).
• use_gpu: Whether or not to use GPU acceleration (default: false).

Returns

• localmaximage: An image with local maxima highlighted.

getboxes(; kwargs...)

Detect particles/blobs in a multidimensional image stack and return coordinates and boxed regions centered around local maxima.

Arguments

• imagestack::AbstractArray{<:Real}: The input image stack. Should be 2D or 3D.
• boxsize::Int: Size of the box to cut out around each local maximum (pixels).
• overlap::Real: Amount of overap allowed between boxes (pixels).
• sigma_small::Real: Sigma for small Gaussian blur kernel (pixels).
• sigma_large::Real: Sigma for large Gaussian blur kernel (pixels).
• minval::Real: Minimum value to consider as a local maximum.
• use_gpu::Bool: Perform convolution and local max finding on GPU.

Returns

• boxstack::AbstractArray{<:Real}: Array with dimensions (boxsize, boxsize, nboxes). Each image in the stack contains a small subregion from imagestack centered around a local maximum
  • boxcoords::Matrix{Float32}: Coordinates of boxes N x (row, col, frame).
  • maxcoords::Matrix{Float32}: Coordinates of boxes N x (row, col, frame).

Details on filtering

The image stack is convolved with a difference of Gaussians (DoG) filter to identify blobs and local maxima. The DoG is computed from two Gaussian kernels with standard deviations sigma_small and sigma_large.

The convolution is performed either on CPU or GPU, depending on use_gpu. After filtering, local maxima above minval are identified. Boxes are cut out around each maximum, excluding overlaps.

Examples

boxes, boxcoords, maxcoords = getboxes(imagestack, boxsize=7, overlap=2.0,  
                         sigma_small=1.0, sigma_large=2.0)

getboxstack(imagestack, coords, args::GetBoxesArgs)

Cut out box regions from imagestack centered on coords.

Arguments

• imagestack: Input image stack
• coords: Coords of box centers
• args: Parameters

Returns

• boxstack: Array with box crops from imagestack

maxima2coords(imagestack)

Get coordinates of all non-zero pixels in input stack

Arguments

• imagestack: Input image stack

Returns

• coords: List of coords for each frame

removeoverlap(coords, args)

Remove overlapping coords based on distance.

Arguments

• coords: List of coords
• args: Parameters

Returns

• coords: Coords with overlaps removed

reshapeforflux(arr::AbstractArray)

Reshape array to have singleton dims for Flux.jl convolution.

Arguments

• arr: Input array, must be 2D or 3D

Returns

• Reshaped array with added singleton dimensions