---

-   References :
    -   <https://haesleinhuepf.github.io/BioImageAnalysisNotebooks/intro.html>
-   Questions :
    -   what is [thresholding]({{< relref "2021-05-27--05-20-06Z--thresholding.md" >}}) and binarization
    -   how can we calculate the relative area of certain object in the image.

---
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 {#steps-involved}


### Conversion to Gray Images {#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 {#gaussian-filter-application}

-   The gaussian filter([image filtering]({{< relref "2021-05-28--12-02-59Z--image_convolution.md" >}})) 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 {#image-thresholding-algorithm-selection}

-   The [image thresholding]({{< relref "2021-05-27--05-20-06Z--thresholding.md" >}}) 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-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 {#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 {#pixel-s-percentage-calculation}

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


## Technical Significance {#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 {#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]({{< relref "2021-05-27--05-20-06Z--thresholding.md" >}}), 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.