Cell Movement and Morphology Analysis

Tracking cells over using CV and microscopy images. ENGR 1050 (Scientific Computing).

Project Overview & Motivation

This project analyzes cell morphology and movement over time using microscopy images and computer vision techniques. By segmenting cells, extracting contours, and tracking changes in shape, area, and brightness, I quantitatively assessed differences in cell behavior across experimental conditions. This workflow automates what would otherwise be manual, time-consuming image analysis. Check out the project notebook here.

Methods

  • Image preprocessing: Converting .tif images to grayscale and applying thresholding to segment cells from background noise.
  • Feature extraction (OpenCV): Drawing contours, measuring changes in cell area & brightness intensity, and calculating cell centroids.
  • Movement analysis: Tracking centroid displacement over time and measuring relative changes in area and brightness between time points.
  • Automation: Implemented batch analysis for multiple cell images across two cell types, exported results to Excel for downstream analysis, and implemented unit tests for validation.

Results

Successfully quantified cell velocity across time-lapse images and morphological changes in area and brightness.

Image after grayscaling and thresholding for segmentation (left) and after drawing a contour (right).

Skills Used

  • Programming: OpenCV, Pandas, NumPy