In life sciences, a substantial portion of data is visual, such as data from microscopy, X-ray, CT, or MRI. These images contain a wealth of information when transformed from images into knowledge.

Particularly in biomedical image analysis, images can provide scientists with valuable information and speed up their analysis with automation (no one wants to count cells manually!).

At Zeeks, we provide training and workshops in biomedical image analysis, and one of the most commonly asked questions is, "Should I use Fiji, Python, or MATLAB for image analysis?"

Unfortunately, there isn't a straightforward answer, as it depends on what data you work with, whether you feel comfortable with coding, and other personal preferences. So, let's take a deep dive into comparing Fiji, Python, and MATLAB.

If you want a free guide to image analysis resources, click here.

If you want to learn more about biomedical image analysis, then click here.

Fiji

Fiji, an open-source image processing package based on ImageJ, is widely used in the scientific community for its user-friendly interface and extensive plugin library. It provides various image visualization, processing, and analysis tools, making it suitable for most biological and medical images.

One of the main advantages of Fiji is its ease of use, especially for beginners. With its intuitive graphical user interface (GUI), users can quickly perform common tasks such as image filtering, segmentation, and measurement without writing any code. Fiji's plugin ecosystem also allows users to extend its functionality to suit specific analysis needs.

We use Fiji for most image analysis courses to teach the basics of image analysis, automation via Macros, and introductory AI classes.

Stick with Fiji if, …

• It does what you need to do (keep it simple!).

• You don't speak another language (yet).

• You want to make use of a large, vibrant community (especially the image.sc community!).

• You work with large 3D+ data (light-sheet data etc).

• You work with data that need stitching.

Python

Python has emerged as a powerhouse and versatile programming language for many different scientific computing applications, including image analysis. With libraries such as NumPy, SciPy, and scikit-image, Python provides robust tools for manipulating and analyzing images efficiently.

One of the key advantages of Python is its flexibility and scalability. Users can leverage various libraries and frameworks to build custom image analysis pipelines tailored to their requirements. Additionally, Python's syntax is clean and easy to understand, making it accessible to novice and experienced programmers.

Moreover, Python's integration with other scientific libraries and tools, such as TensorFlow and PyTorch for deep learning, further expands its capabilities for advanced image analysis tasks.

However, it can be overwhelming for beginners to understand environments and libraries.

"Python is the second best language for everything," said Van Lindberg, general counsel for the Python Software Foundation. 

MATLAB

MATLAB has long been a cornerstone of data and image analysis, offering a comprehensive toolset for image processing. With built-in functions and toolboxes dedicated to image processing, MATLAB provides a convenient environment for prototyping algorithms and conducting research.

One of MATLAB's strengths is its extensive documentation and community support. Users can find plenty of resources, tutorials, and examples to help them get started with image analysis tasks. MATLAB's interactive environment also enables users to explore and experiment with different algorithms in real-time.

However, MATLAB's proprietary nature and licensing costs may deter some users, especially those working in academic or open-source environments. Furthermore, MATLAB's performance can sometimes be slower than other languages, especially for computationally intensive tasks.

Conclusion for image analysis

In conclusion, the choice between Fiji, Python, and MATLAB for image analysis depends on various factors, including user experience, task complexity, and computational requirements. Fiji offers a user-friendly interface and extensive plugin library, making it suitable for beginners and typical image processing tasks. Python provides flexibility and scalability, allowing users to build custom analysis pipelines. MATLAB offers a comprehensive environment with built-in image processing tools and extensive documentation.

Ultimately, the best tool for image analysis will depend on your specific needs and preferences, so you need to decide what works best for you and your data!

If you want a free guide to image analysis resources, click here.