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Thinking like an image analyst, Part IV: Detecting fibers as objects

Pearl V. Ryder In the previous post in this “Thinking like an image analyst” series, I explained how I enhanced fibers to increase their brightness and applied a background subtraction to decrease the intensity of the background. In combination with masking out very bright debris pixels ( Part II of...

Thinking like an image analyst, Part III: Enhancing fibers for detection

Pearl V. Ryder In the previous blog post in this series, I demonstrated how I masked out debris that was brighter than the fibers of interest. In this post, I’ll walk through how I enhanced the fibers in order to increase their brightness and decreased the background intensity of the images. If you...

Thinking like an image analyst, Part II: Removing bright debris from analysis

Pearl V. Ryder In the first post of this series, I gave an overview of this project and explained how I imported the data into CellProfiler. If you’d like to follow along in CellProfiler, the pipeline and images for this project are available here. Now that the images have been imported, I could...

Thinking like an image analyst, Part I: Project overview and data import

Pearl V. Ryder One thing I’ve found very helpful in my journey as an image analyst is learning from others by observing their thought process when they tackle a new problem. In this series of five blog posts, my goal is to share how I approached an image analysis project that I recently undertook as...

Help! How does the Robust Background method work?

Pearl V. Ryder The Robust Background algorithm is a powerful algorithm for automatically setting thresholds to segment objects of interest when your image contains mostly background. However, since it contains the largest number of tunable parameters of any thresholding algorithm in CellProfiler, it...