A Quick Overview of Contrast Enhancement and Its Variants for Medical Image Processing

In the last blog, I covered the pre-processing of Electron Macroscopic Images. By following the same theme of pre-processing, the basics of adaptive contrast enhancement and its application to the medical imaging will be covered in this article.

Medical images are degraded mainly because of the following two factors:

1. Noise, which is inevitable during the data acquisition process.
2. Low contrast, which occurs due to inconsistent illumination and several other factors.

For the further applications in medical image processing, fixing the above artifacts are necessary.

Outcomes

This blog mainly focused on the improvement of contrast in the medical images using the state-of-the-art methods as the preservation of the pixel brightness and optimal contrast are crucial for the further processing, e.g. creating the training data set in network design. Precisely, the take away points of this blog are:

1. Readers will learn about the image pixel manipulations in terms of the image enhancement and de-enhancement
2. An overview of the state-of-the-art transformation methods along with their effects.
3. The reader will be able to pick the right transformation based on the input medical image.