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 .

TY  - JOUR
AU  - Đoković, Marina
AU  - Peulić, Aleksandar
AU  - Jovanović, Željko
AU  - Damnjanović, Đorđe
PY  - 2012
UR  - https://gery.gef.bg.ac.rs/handle/123456789/461
AB  - Image compression research aims at reducing the number of bits needed to represent an image by removing the spatial and spectral redundancies as much as possible. New algorithms for image compression based on wavelets have been developed. These methods have resulted in practical advances such as: lossless and lossy compression, progressive transmission by pixel, accuracy and resolution, region of interest coding and others. The various wavelet based image coding schemes are discussed in this paper. Each of these schemes finds use in different applications owing to their unique characteristics. The methods of lossy compression that we concentrated on are the following: the EZW algorithm, the SPIHT algorithm, the WDR algorithm, and the ASWDR algorithm. These are relatively recent algorithms which achieve some of the lowest errors per compression rate and highest perceptual quality yet reported. After describing these algorithms in detail, we show and discuss the experimental results obtained for three different types of images. We also showed that some important features of image, such as standard deviation and mean pixel intensity values, only slightly change after compression. This fact is very important in medical image compression.
PB  - Editura Stiintifica Fmr, Bucharest
T2  - Metalurgia International
T1  - Image data compression based on discrete wavelet transformation
VL  - 17
IS  - 9
SP  - 179
EP  - 190
UR  - https://hdl.handle.net/21.15107/rcub_gery_461
ER  - 

@article{
author = "Đoković, Marina and Peulić, Aleksandar and Jovanović, Željko and Damnjanović, Đorđe",
year = "2012",
abstract = "Image compression research aims at reducing the number of bits needed to represent an image by removing the spatial and spectral redundancies as much as possible. New algorithms for image compression based on wavelets have been developed. These methods have resulted in practical advances such as: lossless and lossy compression, progressive transmission by pixel, accuracy and resolution, region of interest coding and others. The various wavelet based image coding schemes are discussed in this paper. Each of these schemes finds use in different applications owing to their unique characteristics. The methods of lossy compression that we concentrated on are the following: the EZW algorithm, the SPIHT algorithm, the WDR algorithm, and the ASWDR algorithm. These are relatively recent algorithms which achieve some of the lowest errors per compression rate and highest perceptual quality yet reported. After describing these algorithms in detail, we show and discuss the experimental results obtained for three different types of images. We also showed that some important features of image, such as standard deviation and mean pixel intensity values, only slightly change after compression. This fact is very important in medical image compression.",
publisher = "Editura Stiintifica Fmr, Bucharest",
journal = "Metalurgia International",
title = "Image data compression based on discrete wavelet transformation",
volume = "17",
number = "9",
pages = "179-190",
url = "https://hdl.handle.net/21.15107/rcub_gery_461"
}

Đoković, M., Peulić, A., Jovanović, Ž.,& Damnjanović, Đ.. (2012). Image data compression based on discrete wavelet transformation. in Metalurgia International
Editura Stiintifica Fmr, Bucharest., 17(9), 179-190.
https://hdl.handle.net/21.15107/rcub_gery_461

Đoković M, Peulić A, Jovanović Ž, Damnjanović Đ. Image data compression based on discrete wavelet transformation. in Metalurgia International. 2012;17(9):179-190.
https://hdl.handle.net/21.15107/rcub_gery_461 .

Đoković, Marina, Peulić, Aleksandar, Jovanović, Željko, Damnjanović, Đorđe, "Image data compression based on discrete wavelet transformation" in Metalurgia International, 17, no. 9 (2012):179-190,
https://hdl.handle.net/21.15107/rcub_gery_461 .

TY  - JOUR
AU  - Đoković, Marina
AU  - Peulić, Aleksandar
AU  - Filipović, Nenad
PY  - 2011
UR  - https://gery.gef.bg.ac.rs/handle/123456789/410
AB  - In this paper, we present a multiresolution scheme to detect stellate lesions in mammograms. Multiresolution analysis is used to analyze the images at different frequencies with different resolutions. First we removed the noise from mammograms using Multiresolution analysis and then we detected tumors. Then, using the Embedded Zerotree Wavelet (EZW) algorithm, we compressed denoising mammographic image and showed that by applying the algorithm to detect tumors in the compressed image we obtained the same results as in the case of non-compressed images. Experimental results obtained from the mammographic images of patients recorded in the Clinical Center in Kragujevac, show that using multiresolution algorithm can be detected tumors of different sizes.
PB  - Drunpp-Sarajevo, Sarajevo
T2  - HealthMed
T1  - Automatic identification breast cancer using multiresolution algorithm
VL  - 5
IS  - 6
SP  - 2051
EP  - 2064
UR  - https://hdl.handle.net/21.15107/rcub_gery_410
ER  - 

@article{
author = "Đoković, Marina and Peulić, Aleksandar and Filipović, Nenad",
year = "2011",
abstract = "In this paper, we present a multiresolution scheme to detect stellate lesions in mammograms. Multiresolution analysis is used to analyze the images at different frequencies with different resolutions. First we removed the noise from mammograms using Multiresolution analysis and then we detected tumors. Then, using the Embedded Zerotree Wavelet (EZW) algorithm, we compressed denoising mammographic image and showed that by applying the algorithm to detect tumors in the compressed image we obtained the same results as in the case of non-compressed images. Experimental results obtained from the mammographic images of patients recorded in the Clinical Center in Kragujevac, show that using multiresolution algorithm can be detected tumors of different sizes.",
publisher = "Drunpp-Sarajevo, Sarajevo",
journal = "HealthMed",
title = "Automatic identification breast cancer using multiresolution algorithm",
volume = "5",
number = "6",
pages = "2051-2064",
url = "https://hdl.handle.net/21.15107/rcub_gery_410"
}

Đoković, M., Peulić, A.,& Filipović, N.. (2011). Automatic identification breast cancer using multiresolution algorithm. in HealthMed
Drunpp-Sarajevo, Sarajevo., 5(6), 2051-2064.
https://hdl.handle.net/21.15107/rcub_gery_410

Đoković M, Peulić A, Filipović N. Automatic identification breast cancer using multiresolution algorithm. in HealthMed. 2011;5(6):2051-2064.
https://hdl.handle.net/21.15107/rcub_gery_410 .

Đoković, Marina, Peulić, Aleksandar, Filipović, Nenad, "Automatic identification breast cancer using multiresolution algorithm" in HealthMed, 5, no. 6 (2011):2051-2064,
https://hdl.handle.net/21.15107/rcub_gery_410 .