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In this course, you’ll learn the basics of medical image analysis using Python. You will learn to display and interpret X-ray and CT scans. This course uses relevant Python libraries and commands on medical images for format conversion, segmentation, and analyzing metadata. You can successfully complete this course with only a basic understanding of Python and no background in medical imaging.

Medical Image Analysis in Python

# Introduction to windowing

**Windowing** is also known as gray-level mapping, contrast stretching, histogram modification, or contrast enhancement. This process manipulates the CT image in grayscale via the CT numbers. It changes the appearance of the picture to highlight particular structures. The brightness and contrast of the image are adjusted through the **window level** and the **window width**, respectively. We’ll talk more about window level and window width later.

# Hounsfield units (HU)
**Hounsfield units (HU)** are dimensionless units universally used in **Computed
 Tomography (CT)** scanning. They express CT numbers in a standardized, convenient form. Hounsfield units are obtained from a linear transformation of the measured #key# attenuation: The lessening in signal value over distance #key# coefficients.

The **attenuation coefficient** measures how easily a material can be penetrated by an incident energy beam (for example, in an ultrasound or X-ray). It quantifies how weak the beam gets by passing through the material. This weakening is caused by the arbitrarily-assigned densities of air and pure water.

The radiodensity of distilled water and air at **standard temperature** and **pressure (STP)** is `0 HU` and `-1000 HU`, respectively.
 
>**Note:**  `STP` is $0$ °C, and pressure is 105 pascals at sea-level. It results in a scale running from $-1000$ HU for air to $~+2000$ HU for very dense bone (such as the cochlea) and over $3000$ for metals. Generally, that’s why CT images use 12-bit images able to store values between $−1024$ to $3071$.

# Window width
The transition of dark to light structures occurs during a larger transition to a narrow window width ($<1000$ HU). A significantly wider window ($> 2000$ HU) will cause various attenuation between soft tissues to be darkened.
## Wide window
Wide window (WW) is defined as $400-2000$ HU. It is best used in the areas of acute differing attenuation values. Good examples of these types of areas are the lungs or cortical tissue, where air and vessels are present side by side.
## Narrow window
Narrow window is $50-350$ HU. This window is excellent when examining areas of similar attenuation, like soft tissue.





Learn about windowing and its attributes.