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Related Concept Videos

Classification of Systems-II01:31

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
Classification of Systems-I01:26

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
Classification of Signals01:30

Classification of Signals

In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
Computed Tomography01:10

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...
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...

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Related Experiment Video

Updated: Jun 15, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

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

Published on: August 30, 2013

Contourlet-based mammography mass classification using the SVM family.

Fatemeh Moayedi1, Zohreh Azimifar, Reza Boostani

  • 1Computer Vision and Pattern Recognition Laboratory, School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran. moayyedi@cse.shirazu.ac.ir

Computers in Biology and Medicine
|February 26, 2010
PubMed
Summary

This study introduces an automated mammogram mass classification system using contourlet transform for feature extraction and genetic algorithms for selection. The method achieves high accuracy, aiding in early breast cancer detection.

Related Experiment Videos

Last Updated: Jun 15, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

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

Published on: August 30, 2013

Area of Science:

  • Medical Imaging
  • Computer-Aided Diagnosis
  • Biomedical Engineering

Background:

  • Mammography is crucial for early breast cancer detection.
  • Accurate mass classification in mammograms is essential for diagnosis.
  • Automated systems can improve efficiency and consistency in mammogram analysis.

Purpose of the Study:

  • To design and develop an automatic mass classification system for mammograms.
  • To evaluate the effectiveness of contourlet transform for feature extraction.
  • To assess the performance of various machine learning classifiers for mass classification.

Main Methods:

  • Preprocessing: pectoral muscle removal and region of interest segmentation.
  • Feature Extraction: Contourlet transform to obtain coefficients.
  • Feature Selection: Genetic algorithm for a compact and discriminative texture feature set.
  • Classification: Successive Enhancement Learning (SEL) weighted SVM, Support Vector-based Fuzzy Neural Network (SVFNN), and Kernel SVM.

Main Results:

  • Classification accuracies achieved: 96.6% (SEL weighted SVM), 91.5% (SVFNN), and 82.1% (Kernel SVM) on the MIAS dataset.
  • The proposed method demonstrated efficient computational time.
  • Contourlet-based features combined with advanced classifiers proved effective.

Conclusions:

  • The developed automatic mass classification system is powerful, efficient, and practical.
  • Contourlet transform and genetic algorithm-based feature selection enhance classifier performance.
  • The approach shows significant potential for improving mammogram analysis and diagnosis.