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Human Brown Adipose Tissue Depots Automatically Segmented by Positron Emission Tomography/Computed Tomography and Registered Magnetic Resonance Images

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A minimum spanning forest based classification method for dedicated breast CT images.

Robert Pike¹, Ioannis Sechopoulos², Baowei Fei³

¹Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia 30329.

Medical Physics

|November 2, 2015

Summary

This summary is machine-generated.

An automated algorithm accurately classifies breast tissue in CT scans. This method, using a minimum spanning forest, achieved high accuracy for segmenting fat, glandular, and skin tissues.

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Area of Science:

Medical Imaging
Computer-Aided Diagnosis
Biomedical Engineering

Background:

Accurate breast tissue classification is crucial for medical applications.
Dedicated breast CT imaging offers detailed anatomical visualization.
Automated methods are needed to improve efficiency and consistency in image analysis.

Purpose of the Study:

To develop and validate an automated algorithm for classifying fat, glandular, and skin tissues in dedicated breast CT images.
To assess the performance of a novel minimum spanning forest-based classification approach.

Main Methods:

Utilized a multiscale bilateral filter for noise reduction and artifact correction.
Employed morphological processing and a support vector machine (SVM) for initial tissue identification.
Implemented a 2D minimum spanning forest algorithm for pixelwise segmentation based on spatial and intensity data.

Main Results:

The automated classification achieved high accuracy compared to manual segmentation.
Average DICE overlap ratios were 96.9% for fat, 89.8% for glandular tissue, and 89.5% for skin tissue.

Conclusions:

A 2D minimum spanning forest-based method effectively classifies breast tissues in dedicated CT images.
This automated approach has potential applications in dense breast quantification and radiation dose assessment.