TY  - JOUR
AU  - Radović, Miloš
AU  - Milošević, Marina
AU  - Ninković, Srđan
AU  - Filipović, Nenad
AU  - Peulić, Aleksandar
PY  - 2015
UR  - https://gery.gef.bg.ac.rs/handle/123456789/710
AB  - BACKGROUND: Reading mammograms is a difficult task and for this reason any development that may improve the performance in breast cancer screening is of great importance. OBJECTIVE: We proposed optimized computer aided diagnosis (CAD) system, equipped with reliability estimate module, for mass detection on digitized mammograms. METHODS: Proposed CAD system consists of four major steps: preprocessing, segmentation, feature extraction and classification. We propose a simple regression function as a threshold function for extraction of potential masses. By running optimization procedure we estimate parameters of the preprocessing and segmentation steps thus ensuring maximum mass detection sensitivity. In addition to the classification, where we tested seven different classifiers, the CAD system is equipped with reliability estimate module. RESULTS: By performing segmentation 91.3% of masses were correctly segmented with 4.14 false positives per image (FPpi). This result is improved in the classification phase where, among the seven tested classifiers, multilayer perceptron neural network achieved the best result including 77.4% sensitivity and 0.49 FPpi. CONCLUSION: By using the proposed regression function and parameter optimization we were able to improve segmentation results comparing to the literature. In addition, we showed that CAD system has high potential for being equipped with reliability estimate module.
PB  - IOS Press, Amsterdam
T2  - Technology and Health Care
T1  - Parameter optimization of a computer-aided diagnosis system for detection of masses on digitized mammograms
VL  - 23
IS  - 6
SP  - 757
EP  - 774
DO  - 10.3233/THC-151034
UR  - https://hdl.handle.net/21.15107/rcub_gery_710
ER  - 

@article{
author = "Radović, Miloš and Milošević, Marina and Ninković, Srđan and Filipović, Nenad and Peulić, Aleksandar",
year = "2015",
abstract = "BACKGROUND: Reading mammograms is a difficult task and for this reason any development that may improve the performance in breast cancer screening is of great importance. OBJECTIVE: We proposed optimized computer aided diagnosis (CAD) system, equipped with reliability estimate module, for mass detection on digitized mammograms. METHODS: Proposed CAD system consists of four major steps: preprocessing, segmentation, feature extraction and classification. We propose a simple regression function as a threshold function for extraction of potential masses. By running optimization procedure we estimate parameters of the preprocessing and segmentation steps thus ensuring maximum mass detection sensitivity. In addition to the classification, where we tested seven different classifiers, the CAD system is equipped with reliability estimate module. RESULTS: By performing segmentation 91.3% of masses were correctly segmented with 4.14 false positives per image (FPpi). This result is improved in the classification phase where, among the seven tested classifiers, multilayer perceptron neural network achieved the best result including 77.4% sensitivity and 0.49 FPpi. CONCLUSION: By using the proposed regression function and parameter optimization we were able to improve segmentation results comparing to the literature. In addition, we showed that CAD system has high potential for being equipped with reliability estimate module.",
publisher = "IOS Press, Amsterdam",
journal = "Technology and Health Care",
title = "Parameter optimization of a computer-aided diagnosis system for detection of masses on digitized mammograms",
volume = "23",
number = "6",
pages = "757-774",
doi = "10.3233/THC-151034",
url = "https://hdl.handle.net/21.15107/rcub_gery_710"
}

Radović, M., Milošević, M., Ninković, S., Filipović, N.,& Peulić, A.. (2015). Parameter optimization of a computer-aided diagnosis system for detection of masses on digitized mammograms. in Technology and Health Care
IOS Press, Amsterdam., 23(6), 757-774.
https://doi.org/10.3233/THC-151034
https://hdl.handle.net/21.15107/rcub_gery_710

Radović M, Milošević M, Ninković S, Filipović N, Peulić A. Parameter optimization of a computer-aided diagnosis system for detection of masses on digitized mammograms. in Technology and Health Care. 2015;23(6):757-774.
doi:10.3233/THC-151034
https://hdl.handle.net/21.15107/rcub_gery_710 .

Radović, Miloš, Milošević, Marina, Ninković, Srđan, Filipović, Nenad, Peulić, Aleksandar, "Parameter optimization of a computer-aided diagnosis system for detection of masses on digitized mammograms" in Technology and Health Care, 23, no. 6 (2015):757-774,
https://doi.org/10.3233/THC-151034 .,
https://hdl.handle.net/21.15107/rcub_gery_710 .

TY  - CONF
AU  - Radović, Miloš
AU  - Đoković, Marina
AU  - Peulić, Aleksandar
AU  - Filipović, Nenad
PY  - 2013
UR  - https://gery.gef.bg.ac.rs/handle/123456789/603
AB  - One of the leading causes of cancer death among women is breast cancer. In our work we aim at proposing a prototype of a medical expert system (based on data mining techniques) that could significantly aid medical experts to detect breast cancer. This paper presents the CAD (computer aided diagnosis) system for the detection of normal and abnormal pattern in the breast. The proposed system consists of four major steps: the image preprocessing, the feature extraction, the feature selection and the classification process that classifies mammogram into normal (without tumor) and abnormal (with tumor) pattern. After removing noise from mammogram using the Discrete Wavelet Transformation (DWT), first is selected the region of interest (ROI). By identifying the boundary of the breast, it is possible to remove any artifact present outside the breast area, such as patient markings. Then, a total of 20 GLCM features are extracted from the ROI, which were used as inputs for classification algorithms. In order to compare the classification results, we used seven different classifiers. Normal breast images and breast image with masses (total 322 images) used as input images in this study are taken from the mini-MIAS database.
PB  - IEEE, New York
C3  - 2013 IEEE 13th International Conference on Bioinformatics and Bioengineering (BIBE)
T1  - Application of Data Mining Algorithms for Mammogram Classification
UR  - https://hdl.handle.net/21.15107/rcub_gery_603
ER  - 

@conference{
author = "Radović, Miloš and Đoković, Marina and Peulić, Aleksandar and Filipović, Nenad",
year = "2013",
abstract = "One of the leading causes of cancer death among women is breast cancer. In our work we aim at proposing a prototype of a medical expert system (based on data mining techniques) that could significantly aid medical experts to detect breast cancer. This paper presents the CAD (computer aided diagnosis) system for the detection of normal and abnormal pattern in the breast. The proposed system consists of four major steps: the image preprocessing, the feature extraction, the feature selection and the classification process that classifies mammogram into normal (without tumor) and abnormal (with tumor) pattern. After removing noise from mammogram using the Discrete Wavelet Transformation (DWT), first is selected the region of interest (ROI). By identifying the boundary of the breast, it is possible to remove any artifact present outside the breast area, such as patient markings. Then, a total of 20 GLCM features are extracted from the ROI, which were used as inputs for classification algorithms. In order to compare the classification results, we used seven different classifiers. Normal breast images and breast image with masses (total 322 images) used as input images in this study are taken from the mini-MIAS database.",
publisher = "IEEE, New York",
journal = "2013 IEEE 13th International Conference on Bioinformatics and Bioengineering (BIBE)",
title = "Application of Data Mining Algorithms for Mammogram Classification",
url = "https://hdl.handle.net/21.15107/rcub_gery_603"
}

Radović, M., Đoković, M., Peulić, A.,& Filipović, N.. (2013). Application of Data Mining Algorithms for Mammogram Classification. in 2013 IEEE 13th International Conference on Bioinformatics and Bioengineering (BIBE)
IEEE, New York..
https://hdl.handle.net/21.15107/rcub_gery_603

Radović M, Đoković M, Peulić A, Filipović N. Application of Data Mining Algorithms for Mammogram Classification. in 2013 IEEE 13th International Conference on Bioinformatics and Bioengineering (BIBE). 2013;.
https://hdl.handle.net/21.15107/rcub_gery_603 .

Radović, Miloš, Đoković, Marina, Peulić, Aleksandar, Filipović, Nenad, "Application of Data Mining Algorithms for Mammogram Classification" in 2013 IEEE 13th International Conference on Bioinformatics and Bioengineering (BIBE) (2013),
https://hdl.handle.net/21.15107/rcub_gery_603 .