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Title: Computer-Assisted Diagnosis System for Breast Cancer in Computed Tomography Laser Mammography (CTLM)

Abstract

Computed tomography laser mammography (Eid et al. Egyp J Radiol Nucl Med, 37(1): p. 633–643, 1) is a non-invasive imaging modality for breast cancer diagnosis, which is time-consuming and challenging for the radiologist to interpret the images. Some issues have increased the missed diagnosis of radiologists in visual manner assessment in CTLM images, such as technical reasons which are related to imaging quality and human error due to the structural complexity in appearance. The purpose of this study is to develop a computer-aided diagnosis framework to enhance the performance of radiologist in the interpretation of CTLM images. The proposed CAD system contains three main stages including segmentation of volume of interest (VOI), feature extraction and classification. A 3D Fuzzy segmentation technique has been implemented to extract the VOI. The shape and texture of angiogenesis in CTLM images are significant characteristics to differentiate malignancy or benign lesions. The 3D compactness features and 3D Grey Level Co-occurrence matrix (GLCM) have been extracted from VOIs. Multilayer perceptron neural network (MLPNN) pattern recognition has developed for classification of the normal and abnormal lesion in CTLM images. The performance of the proposed CAD system has been measured with different metrics including accuracy, sensitivity, and specificitymore » and area under receiver operative characteristics (AROC), which are 95.2, 92.4, 98.1, and 0.98%, respectively.« less

Authors:
;  [1];  [2];  [3]
  1. Universiti Putra Malaysia, Faculty of Computer and Communication Systems (Malaysia)
  2. Universiti Putra Malaysia, Faculty of Medicine (Malaysia)
  3. Islamic Azad University, Faculty of Computer and Information Technology Engineering (Iran, Islamic Republic of)
Publication Date:
OSTI Identifier:
22795712
Resource Type:
Journal Article
Journal Name:
Journal of Digital Imaging (Online)
Additional Journal Information:
Journal Volume: 30; Journal Issue: 6; Other Information: Copyright (c) 2017 Society for Imaging Informatics in Medicine; Country of input: International Atomic Energy Agency (IAEA); Journal ID: ISSN 1618-727X
Country of Publication:
United States
Language:
English
Subject:
62 RADIOLOGY AND NUCLEAR MEDICINE; ACCURACY; ANGIOGENESIS; BIOMEDICAL RADIOGRAPHY; CLASSIFICATION; COMPUTERIZED TOMOGRAPHY; DIAGNOSIS; FUZZY LOGIC; IMAGES; MAMMARY GLANDS; NEOPLASMS; NEURAL NETWORKS; PATTERN RECOGNITION; PERFORMANCE; SENSITIVITY

Citation Formats

Jalalian, Afsaneh, Mashohor, Syamsiah, Mahmud, Rozi, Karasfi, Babak, Iqbal Saripan, M., E-mail: iqbal@upm.edu.my, and Ramli, Abdul Rahman, E-mail: arr@upm.edu.my. Computer-Assisted Diagnosis System for Breast Cancer in Computed Tomography Laser Mammography (CTLM). United States: N. p., 2017. Web. doi:10.1007/S10278-017-9958-5.
Jalalian, Afsaneh, Mashohor, Syamsiah, Mahmud, Rozi, Karasfi, Babak, Iqbal Saripan, M., E-mail: iqbal@upm.edu.my, & Ramli, Abdul Rahman, E-mail: arr@upm.edu.my. Computer-Assisted Diagnosis System for Breast Cancer in Computed Tomography Laser Mammography (CTLM). United States. https://doi.org/10.1007/S10278-017-9958-5
Jalalian, Afsaneh, Mashohor, Syamsiah, Mahmud, Rozi, Karasfi, Babak, Iqbal Saripan, M., E-mail: iqbal@upm.edu.my, and Ramli, Abdul Rahman, E-mail: arr@upm.edu.my. Fri . "Computer-Assisted Diagnosis System for Breast Cancer in Computed Tomography Laser Mammography (CTLM)". United States. https://doi.org/10.1007/S10278-017-9958-5.
@article{osti_22795712,
title = {Computer-Assisted Diagnosis System for Breast Cancer in Computed Tomography Laser Mammography (CTLM)},
author = {Jalalian, Afsaneh and Mashohor, Syamsiah and Mahmud, Rozi and Karasfi, Babak and Iqbal Saripan, M., E-mail: iqbal@upm.edu.my and Ramli, Abdul Rahman, E-mail: arr@upm.edu.my},
abstractNote = {Computed tomography laser mammography (Eid et al. Egyp J Radiol Nucl Med, 37(1): p. 633–643, 1) is a non-invasive imaging modality for breast cancer diagnosis, which is time-consuming and challenging for the radiologist to interpret the images. Some issues have increased the missed diagnosis of radiologists in visual manner assessment in CTLM images, such as technical reasons which are related to imaging quality and human error due to the structural complexity in appearance. The purpose of this study is to develop a computer-aided diagnosis framework to enhance the performance of radiologist in the interpretation of CTLM images. The proposed CAD system contains three main stages including segmentation of volume of interest (VOI), feature extraction and classification. A 3D Fuzzy segmentation technique has been implemented to extract the VOI. The shape and texture of angiogenesis in CTLM images are significant characteristics to differentiate malignancy or benign lesions. The 3D compactness features and 3D Grey Level Co-occurrence matrix (GLCM) have been extracted from VOIs. Multilayer perceptron neural network (MLPNN) pattern recognition has developed for classification of the normal and abnormal lesion in CTLM images. The performance of the proposed CAD system has been measured with different metrics including accuracy, sensitivity, and specificity and area under receiver operative characteristics (AROC), which are 95.2, 92.4, 98.1, and 0.98%, respectively.},
doi = {10.1007/S10278-017-9958-5},
url = {https://www.osti.gov/biblio/22795712}, journal = {Journal of Digital Imaging (Online)},
issn = {1618-727X},
number = 6,
volume = 30,
place = {United States},
year = {2017},
month = {12}
}