Detection of microcalcification clusters in digitized X-ray mammograms using unsharp masking and image statistics

Pelin KUŞ; İrfan KARAGÖZ

Makale detayları

Detection of microcalcification clusters in digitized X-ray mammograms using unsharp masking and image statistics

Yazarlar:

Pelin KUŞ | İrfan KARAGÖZ

Yayıncı:

TUBITAK

Dergi:

Turkish Journal of Electrical Engineering and Computer Science 2013 Vol.21 Issue Sup.1, pp.2048-2061

Belge Tipi:

Makale

Yayın Tarihi:

11/2012

Belge Dili:

Türkçe

Document ID (Org No.):

2000743488

Sıklık:

İki Aylık

eISSN:

1303-6203

pISSN:

1300-0632

Özet:

 

A fully automated method for detecting microcalcification (MC) clusters in  regions of interest (ROIs) extracted from digitized X-ray mammograms is  proposed. In the first stage, an unsharp masking is used to perform the  contrast enhancement of the MCs. In the second stage, the ROIs are  decomposed into a 2-level contourlet representation and the reconstruction  is obtained by eliminating the low-frequency subband in the second level. In  the third stage, statistical textural features are extracted from the ROIs  and they are classified using support vector machines. To test the  performance of the method, 57 ROIs selected from the Mammographic Image  Analysis Society's MiniMammogram database are used. The true positive and  false positive rates are used to evaluate the performance of the  classification, and the results are compared with those from other studies  presented in the literature. The results show that the classification method  of unsharp masking and low-band eliminated image statistics is convenient  for MC cluster detection. In particular, a true positive rate of about  94% is achieved at the rate of 0.06 false positives per image.

 

A fully automated method for detecting microcalcification (MC) clusters in  regions of interest (ROIs) extracted from digitized X-ray mammograms is  proposed. In the first stage, an unsharp masking is used to perform the  contrast enhancement of the MCs. In the second stage, the ROIs are  decomposed into a 2-level contourlet representation and the reconstruction  is obtained by eliminating the low-frequency subband in the second level. In  the third stage, statistical textural features are extracted from the ROIs  and they are classified using support vector machines. To test the  performance of the method, 57 ROIs selected from the Mammographic Image  Analysis Society's MiniMammogram database are used. The true positive and  false positive rates are used to evaluate the performance of the  classification, and the results are compared with those from other studies  presented in the literature. The results show that the classification method  of unsharp masking and low-band eliminated image statistics is convenient  for MC cluster detection. In particular, a true positive rate of about  94% is achieved at the rate of 0.06 false positives per image.

Makale detayları

Detection of microcalcification clusters in digitized X-ray mammograms using unsharp masking and image statistics

Yazarlar: Pelin KUŞ | İrfan KARAGÖZ

Yayıncı: TUBITAK

Dergi: Turkish Journal of Electrical Engineering and Computer Science 2013 Vol.21 Issue Sup.1, pp.2048-2061

Belge Tipi: Makale

Yayın Tarihi: 11/2012

Belge Dili: Türkçe

Document ID (Org No.): 2000743488

Sıklık: İki Aylık

eISSN: 1303-6203

pISSN: 1300-0632

Sayı

Yazarlar:

Pelin KUŞ | İrfan KARAGÖZ

Yayıncı:

TUBITAK

Dergi:

Turkish Journal of Electrical Engineering and Computer Science 2013 Vol.21 Issue Sup.1, pp.2048-2061

Belge Tipi:

Makale

Yayın Tarihi:

11/2012

Belge Dili:

Türkçe

Document ID (Org No.):

2000743488

Sıklık:

İki Aylık

eISSN:

1303-6203

pISSN:

1300-0632