pygempick 1.1.6

Open Source Batch Gold Particle Picking & Procesing for Immunogold Diagnostics

#### Open Source Batch Gold Particle Picking & Procesing for Immunogold Diagnostics

### THis is the Installation guide for the PyGemPick module

PyGemPick is the cummulation of Joseph Marsilla's research project
under Dr. Avi Chakrabartty. This module contains functions that enable
filtering, detection, and modeling of immunogold particles on TEM micrographs.

The main project goal was to greate an open source batch gold particle picking
module built in python that could detect gold particles regardless of the amount
of counterstaining present in the IGEM (Immunogold Electron Microscopy) micrograph.

This module has three main dependencies that are needed before usage

1. [OpenCV (cv2)](https://opencv.org/) [(install using conda)](https://stackoverflow.com/questions/23119413/how-do-i-install-python-opencv-through-conda)
2. [Pandas (pd)](https://pandas.pydata.org/)
3. [Numpy (np)](http://www.numpy.org/)

I would suggest installing a new anaconda environment using [anaconda](https://conda.io/docs/user-guide/getting-started.html)
terminal into which you can import all the required modules for your project.
Pandas and Numpy can be installed through any terminal using _**pip install pandas, numpy**_

The project will be updated in the upcoming weeks with tutorials on how
to use the functions given within pygempick. This module was built to
help researchers diagnose pateints with rare protein misfolding diseases
like ATTR, AD, FTD and ALS using novel Immunogold diagnostic techniques.

_**NEW:**_ This update contains supplementary 11 supplementary
documents that will help you use the module. We cover image compression,
image picking with singular and duplicate filtering, statistical analysis,
separation & efficiency tests to test the algorithm's useability.

**Sample Image Data will be provided and shall be located in the DATA folder**

## Installation

pip install pygempick

> import pygempick.core as py
> import pygempick.modeling as mod
> import pygempick.spatialstats as spa

#### Note numpy, pandas and opencv modules dependencies are needed prior installation.

**NEW:** This update contains supplementary 11 supplementary
documents that will help you use the module. We cover image compression,
image picking with singular and duplicate filtering, statistical analysis,
separation & efficiency tests to test the algorithm's fairness.

For more information visit the github!


##Functions:

* 'py.compress(orig-img)'

a function that takes an original large scale electron
micrograph image and compresses it such that 1px = aproximately one nanometer.
the exact pixle dimentions for a 3.1x compression are given below.

* 'py.back_eq(image)'

background equalization provided by solution presented
[here].(https://stackoverflow.com/questions/39231534/get-darker-lines-of-an-image-using-opencv)

* 'py.hclap_filt(p,image, noise)'

New High Contrast Laplace Filter. Applies a [HCLAP Kernel](https://en.wikipedia.org/wiki/Discrete_Laplace_operator) Takes odd scaling parameter p > 5, regular
compressed image, if noise == 'yes' will add median blur after filter applied.

* 'py.hlog_filt(p, image, noise)'

New High-Contrast Laplace of Gaussian Filter. Applies a [HCLOG Kernel](http://fourier.eng.hmc.edu/e161/lectures/gradient/node8.html) to each
image to produce a single binary image as an output. Takes odd and even scaling #
parameters 18+ , input image is regular py.compress image output,
if noise == 'yes' will add median blur after filter applied.

* 'py.dog_filt(p, image)'

[Difference of Gaussian Filter](http://www.tjscientific.com/2017/01/31/using-python-and-opencv-to-create-a-difference-of-gaussian-filter/).
Input is an odd number p to determine size of DOG kernel, input is an py.compress output image, if noise == 'yes' will
add median blur after filter applied.

* 'py.bin_filt(p, image)'

Smart Binary Filtering. Uses the average gray pixel intensity values to determing
the starting [threshold position](https://docs.opencv.org/2.4/doc/tutorials/imgproc/threshold/threshold.html).
Takes odd scaling parameter p, input image is a py.compress output image

_**Note:**_ TEM migrograph filtering using simple binary thresholding was first
completed in 2003 with one of the first gold particle picking algorithms [GoldFinder](https://www.sciencedirect.com/science/article/pii/S104784770200624X).

* _**New:**_ 'key_filt(keypoints1, keypoints2)'

Allows you to scandetected keypoints and eliminate duplicates! Allows you
to detect partciles with more than one filter. Returns updated keypoints
1 with the removed keypoints and number of duplicate(s) detected.

* 'py.pick(image, minAREA, minCIRC, minCONV, minINER, minTHRESH)'

Input image is a binary image from one of the above filters, next have to set
the parameters to optimize [OpenCv's Simple Blob Detector](https://www.learnopencv.com/blob-detection-using-opencv-python-c/)
Detects immunogold particles on filtered binary image by optimizing picking
across 4 main paramaters using OpenCv's simple blob detector. Have to optimize
for each set separately on a per class or per trial basis.

#####Gold Particle Picking Parameters

1. minArea = lowest area in pixels of a detected gold particle (20 px**2)
2. minCirc = lowest circularity value of a detected gold particle [.78 is square]
3. minConv = lowest convextivity parameter which is Convexity is defined as the (Area of the gold particle / Area of it’s convex hull)
4. minINER = minimum inertial ratio (filters gold particles based on eliptical properties, 1 is a complete circle)

* 'py.snapshots(folder, keypoints, gray_img, i)'

folder = folder location where snapshots will be saved, keypoints = the detected
keypoints from py.pick function , gray_img = compressed grayscale image, i = image number.

Takes an compressed grayscale image and uses the detected keypoints as a marker
to take a snapshot of within a 100px radius of that gold particle's position.
*Researchers use this to analyze the morphological properties of protein aggregates*


* 'mod.draw(n, test_number, noise, images):

function to draws test micrograph sets that will be used in subsequent
efficiency or separation tests.

1. Test number 1 is draw only circles, 2 is draw both circles and ellipses.
2. Noise if == 'yes' then, randomly distibuted gaussian noise will be drawn
according to mu1, sig1.
3. images are the number of images in the set - used with n which is number of
particles detected in the actual set to calulate the particle density of model
set.

* 'mod.imgclass(inv_img)'

Uses a compressed grayscale image from [cv2.cvt_color(RGB2GRAY)](https://docs.opencv.org/2.4/modules/imgproc/doc/miscellaneous_transformations.html)
and returns the intensity histogram and related bins position w/ im_class.

* 'mod.septest(p,image)'

Let p be a range of integers ranging from [1, x], let image be a grayscale
image produced after original image compression and conversion to grayscale
using OpenCv's function cv2.cvtColor(orig_img, cv2.COLOR_RGB2GRAY).

Completes separation test for single filter comparrison.

* _**New**_: mod.septest2(p, image, hlogkey):

let p be a range of integers ranging from [1, x] , let image be a grayscale
image produced after original image compression and conversion to grayscale
using OpenCv's function cv2.cvtColor(orig_img, cv2.COLOR_RGB2GRAY).

hlogkey = the keypoints of detected image fitered with HLOG filter - this ensures
faster particle detection since we aren't running the same filtering step more
than once!

Completes separation test for _**dual high-contrase filter comparrison**_.

* 'mod.fitpcf(data)'

Data is the input from a csv created by sta.bin2csv, file is in format of
pcf-dr#-error.csv'. Function initially created to plot graphs for image sets
with varrying concentrations of AB aggregates in solution

Output : built to produce one graph, with fitted curve for positive control(s).
Equation fitted to probability distribution for Complete Spatial Randomness of
the distribution of IGEM particles across EM micrographs.


* 'spa.gamma(a,b,r)'

* a = width of image in pixels
* b = height of the image in pixels
* r is the diatance of the donut from which correlation was calculated.

Function taken from work by [Philemonenko et al 2000](http://nucleus.img.cas.cz/pdf_publications/PHI_Statistical%20evaluation%20of%20Colocalization%20Patterns_01.pdf)
that was used as a window covariogram to correct [Ripley's K function](http://wiki.landscapetoolbox.org/doku.php/spatial_analysis_methods:ripley_s_k_and_pair_correlation_function) for boundary conditions.

* 'spa.pcf(r, N, p0, p1)'

* r is the radius of the donut taken with bin width dr.
* N is the degree PCF (Pair Correlation Function) is the probability distribution of a CSR
related process that we will used to fit our normalized version of

This is a python based solution to [Philmoneko's Statistical Evaluation of Colocalization Patterns in Immunogold Labeling Experiments](http://nucleus.img.cas.cz/pdf_publications/PHI_Statistical%20evaluation%20of%20Colocalization%20Patterns_01.pdf).
The PCF distribution for calculating the colocolization of immunogold particles
on transmission electorn microgrpahs is represented here.

* 'spa.record_kp(i, keypoints, data)'

* i is the image number counter
* keypoints is the list of keypoints of Gold particles detected by py.pick
* data is an empty pandas dataframe.

This function recods the x,y positions of the keypoints detected in each image.
Run in for loop to add results for each image to dataframe which can be then exported
into a csv for easy access. (completed in spa.bin2csv )


* 'spa.bin2csv(images)'

function takes a list of filelocations from glob.glob (asks for the
filtering parameters) then it outputs a csv of the x and y coordinates of
keypoints for every image in images.

For example row 1 contains the x coordinate of the keypoints in image 1 and
row 2 contains the y coordinates in image 1 ect...

* 'spa.bin2df(images)'

images is a set of images from folder using glob.glob() function,
records the keypoint positions found in each image and outputs a pandas
df with detected keypoint centers in (x,y) pixel coordinates.

* 'spa.csv2pcf(data, dr)'

* takes the filename `data` from a csv produced by bin2csv() and outputs
non-normalized scale invarient k (cross-corelation) and pcf (pair-correlation)
statisticaldata from the spatial distribution of the paticles on each micrograph.
(determines wheter the nul-hypothesis of CSR [Complete Spatial Randomness](https://en.wikipedia.org/wiki/Complete_spatial_randomness) is
upheld or voided...). Analyzed by bin2csv. Example output provided in docs.

* dr is the donut width as defined by philmonenko et al, 2000

* 'spa.keypoints2pcf(data_set, dr)'

* Input folder with CSV files of keypoints for different tests
Need to know Image number and average particles detected in each set
(**example**: data_set = glob.glob('/home/joseph/Documents/PHY479/Data/anto/*.csv'))

* dr is the donut width as defined by [philmonenko et al, 2000 article](http://nucleus.img.cas.cz/pdf_publications/PHI_Statistical%20evaluation%20of%20Colocalization%20Patterns_01.pdf)
on immunogold particle colocolization and spatial statistcs.

**output:** pcf-dr{}-error.csv - columns dr (sampling radius), pcf
([pair correlation coefficient](https://en.wikipedia.org/wiki/Pearson_correlation_coefficient)),
dpcf (propogated uncertainty in pcf)