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Computed Tomography

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...

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Related Experiment Video

Updated: May 9, 2026

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations

Published on: January 8, 2013

Statistical texture modeling for medical volume using linear tensor coding.

Junping Deng¹, Xu Qiao, Yen-Wei Chen

¹College of Information Science and Engineering, Ritsumeikan University, Kusatsu 5250072, Japan.

Computational and Mathematical Methods in Medicine

|July 24, 2013

Summary

This summary is machine-generated.

Linear Tensor Coding (LTC) offers a compact way to represent medical volumes. Selecting distinctive bases significantly improves classification accuracy for medical imaging data.

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Area of Science:

Medical imaging analysis
Data representation and compression

Background:

Medical volume data requires efficient representation for analysis.
Current methods may not optimally capture features for classification tasks.

Purpose of the Study:

To introduce Linear Tensor Coding (LTC) as a novel compact representation for medical volumes.
To enhance classification accuracy by selecting distinctive LTC bases.

Main Methods:

Developed Linear Tensor Coding (LTC) for representing medical volumes.
Utilized correlations between category labels and LTC coefficients to select distinctive bases.
Applied selected bases for improved classification.

Main Results:

Achieved a compact representation of medical volumes using LTC.
Demonstrated significant improvement in classification accuracy through basis selection.
Showcased the effectiveness of LTC in medical data analysis.

Conclusions:

Linear Tensor Coding (LTC) provides an effective method for medical volume representation.
Basis selection based on label correlations enhances classification performance.
LTC is a promising technique for medical imaging classification.