Segmentation for the enhancement of microcalcifications in digital mammograms
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2014
Članak u časopisu (Objavljena verzija)
Metapodaci
Prikaz svih podataka o dokumentuApstrakt
Microcalcification clusters appear as groups of small, bright particles with arbitrary shapes on mammographic images. They are the earliest sign of breast carcinomas and their detection is the key for improving breast cancer prognosis. But due to the low contrast of microcalcifications and same properties as noise, it is difficult to detect microcalcification. This work is devoted to developing a system for the detection of microcalcification in digital mammograms. After removing noise from mammogram using the Discrete Wavelet Transformation (DWT), we first selected the region of interest (ROI) in order to demarcate the breast region on a mammogram. Segmenting region of interest represents one of the most important stages of mammogram processing procedure. The proposed segmentation method is based on a filtering using the Sobel filter. This process will identify the significant pixels, that belong to edges of microcalcifications. Microcalcifications were detected by increasing the cont...rast of the images obtained by applying Sobel operator. In order to confirm the effectiveness of this microcalcification segmentation method, the Support Vector Machine (SVM) and k-Nearest Neighborhood (k-NN) algorithm are employed for the classification task using cross-validation technique.
Ključne reči:
Mammography / microcalcifications detection / Discrete Wavelet Transformation / Sobel operator / cross-validationIzvor:
Technology and Health Care, 2014, 22, 5, 701-715Izdavač:
- IOS Press, Amsterdam
DOI: 10.3233/THC-140841
ISSN: 0928-7329
PubMed: 25059254
WoS: 000345307900004
Scopus: 2-s2.0-84911095838
Kolekcije
Institucija/grupa
Geografski fakultetTY - JOUR AU - Milošević, Marina AU - Janković, Dragan AU - Peulić, Aleksandar PY - 2014 UR - https://gery.gef.bg.ac.rs/handle/123456789/609 AB - Microcalcification clusters appear as groups of small, bright particles with arbitrary shapes on mammographic images. They are the earliest sign of breast carcinomas and their detection is the key for improving breast cancer prognosis. But due to the low contrast of microcalcifications and same properties as noise, it is difficult to detect microcalcification. This work is devoted to developing a system for the detection of microcalcification in digital mammograms. After removing noise from mammogram using the Discrete Wavelet Transformation (DWT), we first selected the region of interest (ROI) in order to demarcate the breast region on a mammogram. Segmenting region of interest represents one of the most important stages of mammogram processing procedure. The proposed segmentation method is based on a filtering using the Sobel filter. This process will identify the significant pixels, that belong to edges of microcalcifications. Microcalcifications were detected by increasing the contrast of the images obtained by applying Sobel operator. In order to confirm the effectiveness of this microcalcification segmentation method, the Support Vector Machine (SVM) and k-Nearest Neighborhood (k-NN) algorithm are employed for the classification task using cross-validation technique. PB - IOS Press, Amsterdam T2 - Technology and Health Care T1 - Segmentation for the enhancement of microcalcifications in digital mammograms VL - 22 IS - 5 SP - 701 EP - 715 DO - 10.3233/THC-140841 UR - https://hdl.handle.net/21.15107/rcub_gery_609 ER -
@article{ author = "Milošević, Marina and Janković, Dragan and Peulić, Aleksandar", year = "2014", abstract = "Microcalcification clusters appear as groups of small, bright particles with arbitrary shapes on mammographic images. They are the earliest sign of breast carcinomas and their detection is the key for improving breast cancer prognosis. But due to the low contrast of microcalcifications and same properties as noise, it is difficult to detect microcalcification. This work is devoted to developing a system for the detection of microcalcification in digital mammograms. After removing noise from mammogram using the Discrete Wavelet Transformation (DWT), we first selected the region of interest (ROI) in order to demarcate the breast region on a mammogram. Segmenting region of interest represents one of the most important stages of mammogram processing procedure. The proposed segmentation method is based on a filtering using the Sobel filter. This process will identify the significant pixels, that belong to edges of microcalcifications. Microcalcifications were detected by increasing the contrast of the images obtained by applying Sobel operator. In order to confirm the effectiveness of this microcalcification segmentation method, the Support Vector Machine (SVM) and k-Nearest Neighborhood (k-NN) algorithm are employed for the classification task using cross-validation technique.", publisher = "IOS Press, Amsterdam", journal = "Technology and Health Care", title = "Segmentation for the enhancement of microcalcifications in digital mammograms", volume = "22", number = "5", pages = "701-715", doi = "10.3233/THC-140841", url = "https://hdl.handle.net/21.15107/rcub_gery_609" }
Milošević, M., Janković, D.,& Peulić, A.. (2014). Segmentation for the enhancement of microcalcifications in digital mammograms. in Technology and Health Care IOS Press, Amsterdam., 22(5), 701-715. https://doi.org/10.3233/THC-140841 https://hdl.handle.net/21.15107/rcub_gery_609
Milošević M, Janković D, Peulić A. Segmentation for the enhancement of microcalcifications in digital mammograms. in Technology and Health Care. 2014;22(5):701-715. doi:10.3233/THC-140841 https://hdl.handle.net/21.15107/rcub_gery_609 .
Milošević, Marina, Janković, Dragan, Peulić, Aleksandar, "Segmentation for the enhancement of microcalcifications in digital mammograms" in Technology and Health Care, 22, no. 5 (2014):701-715, https://doi.org/10.3233/THC-140841 ., https://hdl.handle.net/21.15107/rcub_gery_609 .