CS585 Final Project

Title: Living Cells Segmentation
Team members:
- Zilin Zhang(U87038789)
- Fanga Xu(U42180537)
- Yicheng Li(U29503597)
Date: 5/1/2020

Project Problem Definition

For our project, we aim to realize the segmentation and analysis of living cells in microscope images. The study of the morphology of cells is very important to biologists. However, locating, identifying, and counting the cells manually are tedious and time-consuming tasks that could be simplified by means of automatic analysis, in which segmentation is a crucial step. So this project would try to develop our own program to segment different cells and make it robust enough to apply in different datasets.

Datasets Description

We use datasets collected by BU-BIL(Boston University - Biomedical Image Library). These datasets consist of six independent cell datasets. Each dataset contains 35~69 images stored as ‘.png’ files. Besides, every dataset has the golden standard segmented results labeled by experts of cells. So we have a way to compare our results and see how well our algorithm is. Every image has one and only one cell required to be seperated.


  1. Preprocessing
    1. Image Smoothing
      • Median Blur
      • Gaussian Smoothing
    2. Contrast Enhancement
    3. Segment bright regions and dark regions
  2. Region Growing
    1. Initial seeds list
    2. Select a seed point and determine whether 8 - neighborhood have similar properties to the pixel point. If so, the neighborhood continues to be calculated as a seed point
    3. When all the seeds have been traversed, the algorithm stop
  3. Edge Connection
    1. Get bigger objects of dark-region image and bright-region image and merge them
    2. Get the edge of objects in new image by using canny edge detection
    3. Connect the closer edge pixel of each row and column
  4. Contours Approximation
    1. Decided how similar we want our contours and the curve are
    2. Use the poly approximation algorithm to approximates a polygonal curve

Results Summary