How PPReCOGG Works
==================

.. note:: For more details, you can check out my Master's Thesis_ 
            for which this was written.

.. _Thesis: _static/260528685_Szymborski_Joseph_Experimental_Medicine_thesis.pdf

Extracting Texture Features from Pixel of Biopsy Images
-------------------------------------------------------
PPReCOGG extracts texture features from any image, but is specifically made for
extracting texture features from immunoflourescently labelled mammary tumour
biopsy sections. Cell patterning unique to certain stages of cancers can be
recognised this way, and help clinicians make decisions about breast cancer
diagnoses.

PPReCOGG starts by selecting a random sampling of pixels, and centering windows
of multiple sizes over them, convolving the window by Gabor kernels of multiple
orientations, and calculating the mean and std.dev of the pixel values of the 
convolved windows.

.. image:: _static/gabor_diagram.png

Classifying Pixels
------------------
Using the k-Nearest-Neighbor algorithm, it's possible to classify features from 
unknown pixels according to features of pixels whose classes are known.

This results in maps like these:

.. image:: _static/pprecogg_results.png