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SU-E-I-02: Size-Dependent Computed Tomography Histogram Analysis: Towards Breast Tissue Segmentation.

S D Mann¹, J P Shah¹, M P Tornai¹

¹Duke University, Durham, North Carolina.

Medical Physics

|May 19, 2017

Summary

This summary is machine-generated.

Object size impacts scatter-corrected CT histograms for breast tissue segmentation. Gaussian peak centroids show an inverse linear relationship with object size, enabling more accurate segmentation in dedicated breast CT imaging.

Keywords:

Computed tomography Cone beam computed tomography Cumulative distribution functions Curve fitting Data analysis Image analysis Medical image reconstruction Medical imaging Tissue engineering Tissues

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Area of Science:

Medical Imaging
Biomedical Engineering
Radiology

Background:

Dedicated breast CT offers improved imaging of breast tissue.
Scatter correction is crucial for accurate CT image analysis.
Threshold-based segmentation relies on accurate histogram analysis.

Purpose of the Study:

To investigate the influence of object size on scatter-corrected CT histograms.
To evaluate the utility of these histograms for threshold-based tissue segmentation in breast imaging.

Main Methods:

Utilized a quasi-monochromatic dedicated breast CT system with scatter correction.
Imaged phantoms simulating breast tissues and analyzed clinical datasets.
Reconstructed images and analyzed histograms using Gaussian fitting to assess object size effects.

Main Results:

Object size showed low correlation with histogram standard deviation.
Gaussian peak centroids exhibited an inverse linear relationship with object size, independent of material.
Clinical data confirmed a similar linear relationship between centroids and breast radius.

Conclusions:

Gaussian distribution functions can segment breast tissue in scatter-corrected dedicated breast CT.
Object size-independent attenuation variations aid in consistent Gaussian curve fitting.
Proper scaling or analysis of size-dependent slices is essential for accurate segmentation.