scholarly journals Selection of Spatiotemporal Features in Breast MRI to Differentiate between Malignant and Benign Small Lesions Using Computer-Aided Diagnosis

2012 ◽  
Vol 2012 ◽  
pp. 1-8
Author(s):  
F. Steinbruecker ◽  
A. Meyer-Baese ◽  
C. Plant ◽  
T. Schlossbauer ◽  
U. Meyer-Baese
Keyword(s):  
Breast Mri ◽  
Computer Aided Diagnosis ◽  
Spatiotemporal Features ◽  
Computer Aided ◽  
Dynamical Features ◽  
Detection And Diagnosis ◽  
Analysis System ◽  
Global And Local ◽  
Aided Diagnosis ◽  
Optimal Feature

Automated detection and diagnosis of small lesions in breast MRI represents a challenge for the traditional computer-aided diagnosis (CAD) systems. The goal of the present research was to compare and determine the optimal feature sets describing the morphology and the enhancement kinetic features for a set of small lesions and to determine their diagnostic performance. For each of the small lesions, we extracted morphological and dynamical features describing both global and local shape, and kinetics behavior. In this paper, we compare the performance of each extracted feature set for the differential diagnosis of enhancing lesions in breast MRI. Based on several simulation results, we determined the optimal feature number and tested different classification techniques. The results suggest that the computerized analysis system based on spatiotemporal features has the potential to increase the diagnostic accuracy of MRI mammography for small lesions and can be used as a basis for computer-aided diagnosis of breast cancer with MR mammography.

scholarly journals Methods Used in Computer-Aided Diagnosis for Breast Cancer Detection Using Mammograms: A Review

2020 ◽  
Vol 2020 ◽  
pp. 1-21 ◽  
Author(s):  
Saleem Z. Ramadan
Keyword(s):  
Breast Cancer ◽  
Cancer Detection ◽  
The United States ◽  
Computer Aided Diagnosis ◽  
Current Level ◽  
Breast Cancer Detection ◽  
Cad Systems ◽  
Computer Aided ◽  
Detection And Diagnosis ◽  
Aided Diagnosis

According to the American Cancer Society’s forecasts for 2019, there will be about 268,600 new cases in the United States with invasive breast cancer in women, about 62,930 new noninvasive cases, and about 41,760 death cases from breast cancer. As a result, there is a high demand for breast imaging specialists as indicated in a recent report for the Institute of Medicine and National Research Council. One way to meet this demand is through developing Computer-Aided Diagnosis (CAD) systems for breast cancer detection and diagnosis using mammograms. This study aims to review recent advancements and developments in CAD systems for breast cancer detection and diagnosis using mammograms and to give an overview of the methods used in its steps starting from preprocessing and enhancement step and ending in classification step. The current level of performance for the CAD systems is encouraging but not enough to make CAD systems standalone detection and diagnose clinical systems. Unless the performance of CAD systems enhanced dramatically from its current level by enhancing the existing methods, exploiting new promising methods in pattern recognition like data augmentation in deep learning and exploiting the advances in computational power of computers, CAD systems will continue to be a second opinion clinical procedure.

Computer-aided diagnosis of mass-like lesion in breast MRI: Differential analysis of the 3-D morphology between benign and malignant tumors

2013 ◽  
Vol 112 (3) ◽  
pp. 508-517 ◽  
Author(s):  
Yan-Hao Huang ◽  
Yeun-Chung Chang ◽  
Chiun-Sheng Huang ◽  
Tsung-Ju Wu ◽  
Jeon-Hor Chen ◽  
...  
Keyword(s):  
Malignant Tumors ◽  
Breast Mri ◽  
Computer Aided Diagnosis ◽  
Differential Analysis ◽  
Computer Aided ◽  
Aided Diagnosis

Computer-aided diagnosis of diagnostically challenging lesions in breast MRI: a comparison between a radiomics and a feature-selective approach

2016 ◽  
Author(s):  
Sebastian Hoffmann ◽  
Marc Lobbes ◽  
Ivo Houben ◽  
Katja Pinker-Domenig ◽  
Georg Wengert ◽  
...  
Keyword(s):  
Breast Mri ◽  
Computer Aided Diagnosis ◽  
Selective Approach ◽  
Computer Aided ◽  
Aided Diagnosis
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