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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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Updated: May 2, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

An automatic mass detection system in mammograms based on complex texture features.

Shen-Chuan Tai, Zih-Siou Chen, Wei-Ting Tsai

IEEE Journal of Biomedical and Health Informatics

|March 11, 2014

Summary

This summary is machine-generated.

This study introduces an automatic computer-aided detection (CADe) system to improve mammogram analysis. The system uses texture features to help radiologists detect breast cancer masses more accurately.

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Published on: February 18, 2015

Area of Science:

Medical Imaging
Radiology
Artificial Intelligence in Medicine

Background:

Mammography interpretation is challenging due to complex tissue structures.
Radiologists miss 10-30% of tumors owing to ambiguous lesion margins and fatigue.
Computer-aided detection (CADe) systems aim to assist radiologists in identifying mammographic lesions.

Purpose of the Study:

To present an automatic CADe system for mammographic mass detection.
To utilize local and discrete texture features for enhanced lesion identification.

Main Methods:

Segmentation of adaptive square regions of interest (ROIs) for suspicious areas.
Two novel feature extraction methods using co-occurrence matrix and optical density transformation.
Stepwise linear discriminant analysis for classification and feature performance evaluation.

Main Results:

The proposed system demonstrates satisfactory performance in detecting mammographic masses.
The system effectively describes local texture and photometric distributions within ROIs.

Conclusions:

The developed CADe system shows promise in aiding radiologists with mammographic mass detection.
The integration of texture features and advanced analysis improves diagnostic accuracy in breast cancer screening.