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Showing posts from 2014

Selenium testing in Jenkins with an in-memory X server

This articles explains how to setup integration testing of web applications (that require a browser instance) on a Jenkins server. The article assumes you are able to understand the title. If you need a reminder follow the links below.

What is Selenium? Read here.
What is Jenkins? Read here.
What is an X server? Read here.

If you are still puzzled, this article is not for you.

Integration testing of web application is more complicated than unit testing, because they require a browser instance to be running. In order to run a browser (such as Firefox) you need and X server. The testing server usually doesn't have one.

One solution to this is to run the browser instance on another machine through VNC. This has the advantage of not using the same resources as the testing server. However it requires setting up a new machine, and configuring the integration tests to run a browser in it.

Another solution is to use Xvfb, a display server that implements the X11 protocol, but does not re…

Basic cell counting and segmentation in Matlab

Counting cells manually is a tedious error prone process for humans. Given a large data set of microscopy images this task can be achieved much faster by means of basic computer vision techniques. In this tutorial we will segment cells from an image following a method similar to the one presented by Yongming Chen in 1999. The method uses basic morphological operations and the watershed algorithm to segment the cells. Nowadays better methods for cell segmentation exist. This method was chosen for its simplicity and ease of implementation.

We start with an image of cell-like structures by Anna-Katerina Hadjantonakis and Virginia E Papaioannou.

A = imread('cells.jpg');
We convert the image to grayscale:
I = rgb2gray(A);

To be able to extract the dimmer cells, it is necessary to perform some local contrast adjustments
I = adapthisteq(I);


Objects on the borders can be caused by noise and other artifacts. We can eliminate objects on the borders of the image like this:
I = imclearborder(…