In a DICOM (Digital Imaging and Communications in Medicine) image, each pixel (short for "picture element") represents a specific location on the image and has a corresponding value that determines its color or intensity. The value of each pixel is stored as a digital number, which can range from 0 to a maximum value that is specific to the image's bit depth.

In a grayscale DICOM image, each pixel represents the intensity of the image at that particular location. The value of each pixel corresponds to the brightness of the image at that location, with higher values indicating a brighter intensity and lower values indicating a darker intensity.

In a color DICOM image, each pixel is made up of multiple values that represent the intensity of the red, green, and blue (RGB) channels at that location. The combination of these three values determines the overall color of the pixel.

In addition to the pixel values, DICOM images also contain metadata that provides information about the image, such as the modality (e.g. CT, MRI, etc.), the patient's name and identification number, and the acquisition parameters used to acquire the image. This metadata is stored in a header that is separate from the pixel data.

Overall, each pixel in a DICOM image represents the intensity or color of the image at a specific location, and the combination of all of the pixels in the image forms the complete image.

Hello

Maybe this information will be helpful for you:

1. DICOM tags in real-life sample image:

(0028,1052) RescaleIntercept: -1024

(0028,1053) RescaleSlope: 1

(0028,1054) RescaleType: 'HU',

2.

HU = pixel_value * RescaleSlope + RescaleIntercept

http://www.idlcoyote.com/fileio_tips/hounsfield.html

3.

http://dicomlookup.com/lookup.asp?sw=Tnumber&q=(0028,1052)

http://dicomlookup.com/lookup.asp?sw=Tnumber&q=(0028,1053)

http://dicomlookup.com/lookup.asp?sw=Tnumber&q=(0028,1054)

4.

https://dicom.nema.org/medical/dicom/2018b/output/chtml/part03/sect_C.11.2.html

The DICOM file contains both the image data in the image matrix and the meta-data, containing other information about the patient, the modality, acquisition time and parameters, etc.

So the image matrix, formed of pixels, contains only the grayscale value assigned to each particular pixel. It does not contain the HU, because DICOM is used for all imaging modalities and not only for CT.

To visualize each slice, window\level operation is applied. Refer here for more details: https://kevalnagda.github.io/ct-windowing .

So, changes of 'window' value leads to image contrast change, and changes of 'level' value leads to image brightness change.

As a starting point see Adrians answer above, then consider windowing as both Adrian & Svitlana have referenced as well.