01 / The question
When should one threshold become several?
This project compared thresholding methods on DICOM/CT images and investigated how noise changes the separation between intensity classes.
02 / The intuition
A mechanism worth testing.
A threshold is a decision rule tied to an image distribution. A method that works on one histogram may fail after acquisition conditions change.
03 / The work
Inside the method.
Explore each part of the approach.
01Read the image
DICOM image arrays and intensity histograms provided the starting point.
02Compare decision rules
Iterative global thresholds, Otsu, multi-Otsu and adaptive methods were explored.
03Add perturbations
Noise experiments and mask comparisons examined sensitivity to changing image statistics.
04 / The observations
What emerged.
The notes describe comparative thresholding and noise analysis across several methods.
This account is based on recorded project notes. Original reports, figures and datasets are not embedded here.
05 / The limits
Where the evidence stops.
An intensity class is not automatically an anatomical structure. Ground-truth definitions and the original masks are required to interpret segmentation quality.
The academic foundations