Cell Detection

A Python project that detects the pixel locations of individual cells in a microscope slide. The script filters out background noise in the slide using multiple techniques and groups pixels into cells using a flood-fill algorithm.

Input Slide	Detected Cells

Setup

virtualenv env
. env/bin/activate
pip install -r requirements.txt

Running

. env/bin/activate
python cell-detection/main.py -i 'data/raw/testSlide1.png'

Arguments

• Input (-i or --input-file) the path to the input file or directory of files
• Output (-o or --output-file) the path of the output json file (default is results.json)

Options

• Show process (--show-process) displays images to show the steps of filtering

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Output File Format

The output file contains a JSON object with the array "cells" as the only key. Each element in the "cells" array is a "cell" object, which is an array of pixel locations that cell occupies, represented by the x and y pixel coordinates of each pixel. A basic example is as follows:

{
  "cells": [
    [
        {
            "x": 40,
            "y": 21
        },
        {
            "x": 40,
            "y": 22
        },
    ],
  ]
}

Slide Processing Algorithm

1. Convert to grayscale, normalize pixels to range of [0, 1]
2. Filter out noise by setting pixels in [0, 0.3] to 0, all others to 1
3. Remove any cells that are unreasonably small (likely noise)
4. Expand/merge existing cells to avoid splicing single cells into multiple
5. Count final cells with flood-fill search

Processing Stage	Slide
Input
Grayscale
Simple Noise Gate
Cell Size Filter, Expand/Merge