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Automatic identification breast cancer using multiresolution algorithm

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Authors
Đoković, Marina
Peulić, Aleksandar
Filipović, Nenad
Article (Published version)
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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.
Keywords:
Multiresolution analysis of mammograms / Discrete Wavelet Transformation / Embedded zerotree wavelet / Breast cancer
Source:
HealthMed, 2011, 5, 6, 2051-2064
Publisher:
  • Drunpp-Sarajevo, Sarajevo
Funding / projects:
  • Application of biomedical engineering for preclinical and clinical practice (RS-41007)

ISSN: 1840-2291

WoS: 000299863300029

[ Google Scholar ]
1
URI
https://gery.gef.bg.ac.rs/handle/123456789/410
Collections
  • Radovi istraživača
Institution/Community
Geografski fakultet
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  - conv_1499
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 = "conv_1499"
}
Đoković, M., Peulić, A.,& Filipović, N.. (2011). Automatic identification breast cancer using multiresolution algorithm. in HealthMed
Drunpp-Sarajevo, Sarajevo., 5(6), 2051-2064.
conv_1499
Đoković M, Peulić A, Filipović N. Automatic identification breast cancer using multiresolution algorithm. in HealthMed. 2011;5(6):2051-2064.
conv_1499 .
Đoković, Marina, Peulić, Aleksandar, Filipović, Nenad, "Automatic identification breast cancer using multiresolution algorithm" in HealthMed, 5, no. 6 (2011):2051-2064,
conv_1499 .

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