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image binarization and pixel count


tags: binarization pixel count thresholding gaussian
categories: image processing

One of the critical aspects of image analysis involves quantifying pixel data, a process that’s integral to various scientific and medical applications. In this blog, we’ll try to explore the process of converting microscopic images into binary representations for pixel counting.

Steps involved

Conversion to Gray Images

  • First, raw microscopic images are converted to grayscale images. Grayscale images feature a single channel that represents pixel values from 0 (black) to 255 (white).
  • By focusing entirely on grayscale values, this stage streamlines further processing.

Gaussian Filter Application

  • The gaussian filter(image filtering) is used to eliminate noise from the image.
  • Noise reduction is crucial for accurate pixel counting.
  • The outcome is a smoother image, free from unwanted noises.

Image thresholding Algorithm Selection

  • The image thresholding algorithm is crucial to the process.
  • The chosen algorithm automates the threshold value determination.
  • We choose the `threshold_yen` algorithm because of its efficiency.
  • The threshold value is used as a starting point for pixel binarization.

Binarization: Creating Binary Images

  • Binarization is the process of converting grayscale pixels into binary values (0 or 1).
  • Pixels below the threshold turn black (0), while those above turn white (1).
  • This step effectively creates a binary image for further analysis.

Counting Black and White Pixels

  • The pixel counting phase is pivotal.
  • Counting the black (0) and white (1) pixels provides quantitative insights.
  • The counts reveal the distribution of these pixels within the image.

Pixel’s Percentage Calculation

  • Percentages can be consider as the areas occupied by each pixel type.

Technical Significance

  • The technique extends beyond pixel manipulation; it has wide applications.
  • Pixel-level analysis is invaluable in sectors such as medical research and materials science.
  • Changes in pixel distribution between “before” and “after” images can be precisely quantified.

In the Lab: Practical Example

  • Imagine tracking cellular changes using this technique.
  • Quantifying alterations in pixel distribution aids in understanding cell behavior.

Converting microscopic images into binary representations for pixel counting is a time-consuming process with multiple technical steps. Gray conversion, noise reduction, image thresholding, binarization, pixel counting, and percentage computation are all steps in the process of converting a raw image to a binary representation. The significance of this technique extends across other disciplines, where pixel-level research enables significant insights and breakthroughs. Researchers and practitioners can realize the full potential of microscopic image analysis by mastering these steps.

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