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Computer-aided detection/diagnosis of breast cancer in mammography and ultrasound: a review

Abstract Breast cancer is the most common form of cancer among women worldwide. Early detection of breast cancer can increase treatment options and patients' survivability. Mammography is the gold standard for breast imaging and cancer detection. However, due to some limitations of this modality suc... Full description

Journal Title: Clinical imaging 2013, Vol.37 (3), p.420-426
Main Author: Jalalian, Afsaneh
Other Authors: Mashohor, Syamsiah B.T , Mahmud, Hajjah Rozi , Saripan, M. Iqbal B , Ramli, Abdul Rahman B , Karasfi, Babak
Format: Electronic Article Electronic Article
Language: English
Subjects:
Quelle: Alma/SFX Local Collection
Publisher: United States: Elsevier Inc
ID: ISSN: 0899-7071
Link: https://www.ncbi.nlm.nih.gov/pubmed/23153689
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recordid: cdi_proquest_miscellaneous_1671514224
title: Computer-aided detection/diagnosis of breast cancer in mammography and ultrasound: a review
format: Article
creator:
  • Jalalian, Afsaneh
  • Mashohor, Syamsiah B.T
  • Mahmud, Hajjah Rozi
  • Saripan, M. Iqbal B
  • Ramli, Abdul Rahman B
  • Karasfi, Babak
subjects:
  • Accuracy
  • Algorithms
  • Artificial Intelligence
  • Breast
  • Breast cancer
  • Breast Neoplasms - diagnostic imaging
  • Breasts
  • Cancer
  • Classification
  • Computer-aided detection
  • Computer-aided diagnosis
  • Diagnosis
  • Female
  • Gold
  • Humans
  • Image Enhancement - methods
  • Image Interpretation, Computer-Assisted - methods
  • Imaging
  • Mammography
  • Mammography - methods
  • Medical imaging
  • Methods
  • Neural networks
  • Pattern Recognition, Automated - methods
  • Radiology
  • Reproducibility of Results
  • Sensitivity and Specificity
  • Standards
  • Studies
  • Survivability
  • Ultrasonic imaging
  • Ultrasonography, Mammary - methods
  • Ultrasound
  • Wavelet transforms
ispartof: Clinical imaging, 2013, Vol.37 (3), p.420-426
description: Abstract Breast cancer is the most common form of cancer among women worldwide. Early detection of breast cancer can increase treatment options and patients' survivability. Mammography is the gold standard for breast imaging and cancer detection. However, due to some limitations of this modality such as low sensitivity especially in dense breasts, other modalities like ultrasound and magnetic resonance imaging are often suggested to achieve additional information. Recently, computer-aided detection or diagnosis (CAD) systems have been developed to help radiologists in order to increase diagnosis accuracy. Generally, a CAD system consists of four stages: (a) preprocessing, (b) segmentation of regions of interest, (c) feature extraction and selection, and finally (d) classification. This paper presents the approaches which are applied to develop CAD systems on mammography and ultrasound images. The performance evaluation metrics of CAD systems are also reviewed.
language: eng
source: Alma/SFX Local Collection
identifier: ISSN: 0899-7071
fulltext: fulltext
issn:
  • 0899-7071
  • 1873-4499
url: Link


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descriptionAbstract Breast cancer is the most common form of cancer among women worldwide. Early detection of breast cancer can increase treatment options and patients' survivability. Mammography is the gold standard for breast imaging and cancer detection. However, due to some limitations of this modality such as low sensitivity especially in dense breasts, other modalities like ultrasound and magnetic resonance imaging are often suggested to achieve additional information. Recently, computer-aided detection or diagnosis (CAD) systems have been developed to help radiologists in order to increase diagnosis accuracy. Generally, a CAD system consists of four stages: (a) preprocessing, (b) segmentation of regions of interest, (c) feature extraction and selection, and finally (d) classification. This paper presents the approaches which are applied to develop CAD systems on mammography and ultrasound images. The performance evaluation metrics of CAD systems are also reviewed.
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subjectAccuracy ; Algorithms ; Artificial Intelligence ; Breast ; Breast cancer ; Breast Neoplasms - diagnostic imaging ; Breasts ; Cancer ; Classification ; Computer-aided detection ; Computer-aided diagnosis ; Diagnosis ; Female ; Gold ; Humans ; Image Enhancement - methods ; Image Interpretation, Computer-Assisted - methods ; Imaging ; Mammography ; Mammography - methods ; Medical imaging ; Methods ; Neural networks ; Pattern Recognition, Automated - methods ; Radiology ; Reproducibility of Results ; Sensitivity and Specificity ; Standards ; Studies ; Survivability ; Ultrasonic imaging ; Ultrasonography, Mammary - methods ; Ultrasound ; Wavelet transforms
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abstractAbstract Breast cancer is the most common form of cancer among women worldwide. Early detection of breast cancer can increase treatment options and patients' survivability. Mammography is the gold standard for breast imaging and cancer detection. However, due to some limitations of this modality such as low sensitivity especially in dense breasts, other modalities like ultrasound and magnetic resonance imaging are often suggested to achieve additional information. Recently, computer-aided detection or diagnosis (CAD) systems have been developed to help radiologists in order to increase diagnosis accuracy. Generally, a CAD system consists of four stages: (a) preprocessing, (b) segmentation of regions of interest, (c) feature extraction and selection, and finally (d) classification. This paper presents the approaches which are applied to develop CAD systems on mammography and ultrasound images. The performance evaluation metrics of CAD systems are also reviewed.
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pmid23153689
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