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Writer's pictureHSIYUN WEI

Bioimage Analysis Toolkit: From Processing to Feature Extraction with Python and ImageJ



Project Title: Bioimage Analysis for Cell Feature Extraction

Overview

This project aimed at developing a comprehensive framework for analyzing biological images to extract and quantify cellular features. By focusing on practical applications in biological research, the project sought to facilitate the understanding of cellular behaviors and characteristics through advanced image processing techniques.

Tools and Technologies

  • Languages: Python

  • Libraries: NumPy, Matplotlib, SciPy, scikit-image

  • Software: Jupyter Notebook, ImageJ

  • Techniques: Image processing, filtering, segmentation, feature extraction

Approach and Methodology

The project employed a series of steps to process and analyze cell images:

  1. Image Preprocessing: Utilizing Jupyter Notebook for interactive analysis, initial preprocessing involved reading and visualizing raw cellular images alongside their segmentation results.

  2. Postprocessing: I developed algorithms to remove cells touching the image border, enhancing the clarity and accuracy of the segmentation.

  3. Edge Detection: By identifying cell edges, we were able to further refine our understanding of cell morphology.

  4. Quantitative Analysis: Key cellular features, such as mean intensity, membrane intensity, area, and perimeter, were extracted. This quantitative approach allowed for detailed cellular analysis.

  5. Statistical and Visual Analysis: Utilizing Python libraries, I performed statistical analysis to understand the distribution of cellular features and visualized these findings through histograms, box plots, and scatter plots, offering insights into cell size distribution and feature correlations.

Results and Impact

The project successfully developed a pipeline that can extract critical cellular features from bioimages, providing quantitative insights into cell morphology and behavior. The analysis revealed variations in cell size, shape, and internal structure, highlighting the potential for further biological research applications. This work not only demonstrates the power of image analysis in understanding biological processes but also offers a foundation for future studies in cellular behavior and disease diagnosis.

Visuals


  • Histograms of cell area distribution


  • Box plots comparing mean cell and membrane intensities


  • Scatter plots of cell outline length over cell area with linear regression analysis


Code and Repository Link : GitHub Repository

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