BibTex RIS Kaynak Göster
Yıl 2015, Cilt: 36 Sayı: 3, 2269 - 2277, 13.05.2015

Öz

Kaynakça

  • De Oliveira, M., Braz, J., Cardoso, P., Gattass, M., " Detection of Masses in Digital Mammograms using K-means and Support Vector Machine", Electronic Letters on Computer Vision and Image Analysis, vol. 8, No 2, pp. 39-50, 2009.
  • Dominguez, A., Nandi, A., "Detection of masses in mammograms via statistically based enhancement, multilevel-thresholding segmentation, and region selection", journal of Computerized Medical Imaging and Graphics, Vol. 32, No. 4, pp. 304-315, 2008.
  • Ozekes S., Osman O., Camurcu A.Y., "Mammographic Mass Detection Using A Mass Template", Korean J Radiol, Vol. 6, No. 3, p.221-228, 2005.
  • Zheng L., Chan A.K., "An Artificial Algorithm for Tumor Detection in Screening Mammograms", IEEE Transactions on Medical Imaging, Vol. 20, No. 7, p. 559-567, 2001. [5] Dzung V., Nguyen D.T., Dzung T, Pham V.T., "An Automated Method to Segment and Classify Masses in Mammograms", International Journal of Electrical and Computer Engineering, Vol. 8, No. 4, 2009
  • Bovis K., Singh S., Fieldsend J, Pinder Ch., "Identification of masses in digital mammograms With MLP and RBF Nets", in Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks Com, Vol 1, pp. 342-347, 2000.
  • Christoyanni I., Dermatas E., Kokkinakis G, "Fast detection of masses in computer-aided mammography", IEEE Signal Process. Mag., Vol. 17, No. 1, pp. 54-64, 2000.
  • Yang S., Wang C., Chung Y., Hsu G., Lee S., Chung P., Chang C., "A computer aided system for mass detection and classification in digitized mammograms", Biomedical Engineering-Application-Basis & Communications, Vol. 17, No. 5, pp. 215-228, 2005.
  • Massotti M., Campanini R., "Texture classification using invariant ranklet features", Pattern Recognition Letters, Vol. 29, No. 14, pp. 1980-1986, 2008.
  • Haralick R.M., Shanmugan K., Dinstein I., "Textural Features for Image Classification", IEEE Transactions on Systems,Man and Cybernetics, Vol. 3, No. 6, pp. 610-621, 1973.
  • Soh L., Tsatsoulis C., "Texture Analysis of SAR Sea Ice Imagery Using Gray Level Co- occurrence Matrices", IEEE Transaction on Geoscience and Remote Sensing, Vol. 37, No. 2, pp. 780-795, 1999
  • Hall M.A., "Correlation-based Feature Subset Selection for Machine Learning", Ph.D. Thesis, University of Waikato, Hamilton, New Zealand 1999.
  • Chawla N.V., "Synthetic Minority Over-sampling Technique", Journal of Artificial Intelligence Research, Vol. 16, pp. 321-357, 2002.
  • Domingos P., "MetaCost: A general method for making classifiers cost-sensitive", Fifth International Conference on Knowledge Discovery and Data Mining, pp. 155-164, 1999.
  • Breiman L., "Bagging predictors", Machine Learning, Vol. 24, No. 2, pp. 123-140, 1996.
  • Freund Y., Schapire R.E., "Experiments with a new boosting algorithm", Thirteenth International Conference on Machine Learning, San Francisco, pp. 148-156, 1996.
  • Rodriguez J.J, Kuncheva L., Alonso C., "Rotation Forest: A new classifier ensemble method". IEEE Transactions on Pattern Analysis and Machine Intelligence. Vol. 28, No. 10, pp. 1619-1630, 2006.

An Efficient Method for Detection of Masses in Mammogram Images

Yıl 2015, Cilt: 36 Sayı: 3, 2269 - 2277, 13.05.2015

Öz

Abstract. Breast cancer is one of the most common cancers among women. Mammography is currently the most effective method for early detection of breast cancer. In this paper, a method is proposed for detecting masses in mammogram images. First, based on a specific algorithm, image is segmented and a number of the suspicious regions are obtained. Then, many features are extracted from these regions. To reduce the features, a supervised feature selection method is used. In the final step, a cost-sensitive classifier has been used for classification of the samples. This approach was tested on all images having mass from mini-MIAS data set. Based on the classification results, the percentage of true positive detection rate was 91% false-positive detection was 14% and the area under ROC curve was achieved 96%.

Kaynakça

  • De Oliveira, M., Braz, J., Cardoso, P., Gattass, M., " Detection of Masses in Digital Mammograms using K-means and Support Vector Machine", Electronic Letters on Computer Vision and Image Analysis, vol. 8, No 2, pp. 39-50, 2009.
  • Dominguez, A., Nandi, A., "Detection of masses in mammograms via statistically based enhancement, multilevel-thresholding segmentation, and region selection", journal of Computerized Medical Imaging and Graphics, Vol. 32, No. 4, pp. 304-315, 2008.
  • Ozekes S., Osman O., Camurcu A.Y., "Mammographic Mass Detection Using A Mass Template", Korean J Radiol, Vol. 6, No. 3, p.221-228, 2005.
  • Zheng L., Chan A.K., "An Artificial Algorithm for Tumor Detection in Screening Mammograms", IEEE Transactions on Medical Imaging, Vol. 20, No. 7, p. 559-567, 2001. [5] Dzung V., Nguyen D.T., Dzung T, Pham V.T., "An Automated Method to Segment and Classify Masses in Mammograms", International Journal of Electrical and Computer Engineering, Vol. 8, No. 4, 2009
  • Bovis K., Singh S., Fieldsend J, Pinder Ch., "Identification of masses in digital mammograms With MLP and RBF Nets", in Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks Com, Vol 1, pp. 342-347, 2000.
  • Christoyanni I., Dermatas E., Kokkinakis G, "Fast detection of masses in computer-aided mammography", IEEE Signal Process. Mag., Vol. 17, No. 1, pp. 54-64, 2000.
  • Yang S., Wang C., Chung Y., Hsu G., Lee S., Chung P., Chang C., "A computer aided system for mass detection and classification in digitized mammograms", Biomedical Engineering-Application-Basis & Communications, Vol. 17, No. 5, pp. 215-228, 2005.
  • Massotti M., Campanini R., "Texture classification using invariant ranklet features", Pattern Recognition Letters, Vol. 29, No. 14, pp. 1980-1986, 2008.
  • Haralick R.M., Shanmugan K., Dinstein I., "Textural Features for Image Classification", IEEE Transactions on Systems,Man and Cybernetics, Vol. 3, No. 6, pp. 610-621, 1973.
  • Soh L., Tsatsoulis C., "Texture Analysis of SAR Sea Ice Imagery Using Gray Level Co- occurrence Matrices", IEEE Transaction on Geoscience and Remote Sensing, Vol. 37, No. 2, pp. 780-795, 1999
  • Hall M.A., "Correlation-based Feature Subset Selection for Machine Learning", Ph.D. Thesis, University of Waikato, Hamilton, New Zealand 1999.
  • Chawla N.V., "Synthetic Minority Over-sampling Technique", Journal of Artificial Intelligence Research, Vol. 16, pp. 321-357, 2002.
  • Domingos P., "MetaCost: A general method for making classifiers cost-sensitive", Fifth International Conference on Knowledge Discovery and Data Mining, pp. 155-164, 1999.
  • Breiman L., "Bagging predictors", Machine Learning, Vol. 24, No. 2, pp. 123-140, 1996.
  • Freund Y., Schapire R.E., "Experiments with a new boosting algorithm", Thirteenth International Conference on Machine Learning, San Francisco, pp. 148-156, 1996.
  • Rodriguez J.J, Kuncheva L., Alonso C., "Rotation Forest: A new classifier ensemble method". IEEE Transactions on Pattern Analysis and Machine Intelligence. Vol. 28, No. 10, pp. 1619-1630, 2006.
Toplam 16 adet kaynakça vardır.

Ayrıntılar

Bölüm Derleme
Yazarlar

Javad Haddadnıa

Omid Rahmanı-seryasat Bu kişi benim

Hossein Ghayoumı-zadeh Bu kişi benim

Hamidreza Rabıee Bu kişi benim

Yayımlanma Tarihi 13 Mayıs 2015
Yayımlandığı Sayı Yıl 2015 Cilt: 36 Sayı: 3

Kaynak Göster

APA Haddadnıa, J., Rahmanı-seryasat, O., Ghayoumı-zadeh, H., Rabıee, H. (2015). An Efficient Method for Detection of Masses in Mammogram Images. Cumhuriyet Üniversitesi Fen Edebiyat Fakültesi Fen Bilimleri Dergisi, 36(3), 2269-2277.
AMA Haddadnıa J, Rahmanı-seryasat O, Ghayoumı-zadeh H, Rabıee H. An Efficient Method for Detection of Masses in Mammogram Images. Cumhuriyet Üniversitesi Fen Edebiyat Fakültesi Fen Bilimleri Dergisi. Mayıs 2015;36(3):2269-2277.
Chicago Haddadnıa, Javad, Omid Rahmanı-seryasat, Hossein Ghayoumı-zadeh, ve Hamidreza Rabıee. “An Efficient Method for Detection of Masses in Mammogram Images”. Cumhuriyet Üniversitesi Fen Edebiyat Fakültesi Fen Bilimleri Dergisi 36, sy. 3 (Mayıs 2015): 2269-77.
EndNote Haddadnıa J, Rahmanı-seryasat O, Ghayoumı-zadeh H, Rabıee H (01 Mayıs 2015) An Efficient Method for Detection of Masses in Mammogram Images. Cumhuriyet Üniversitesi Fen Edebiyat Fakültesi Fen Bilimleri Dergisi 36 3 2269–2277.
IEEE J. Haddadnıa, O. Rahmanı-seryasat, H. Ghayoumı-zadeh, ve H. Rabıee, “An Efficient Method for Detection of Masses in Mammogram Images”, Cumhuriyet Üniversitesi Fen Edebiyat Fakültesi Fen Bilimleri Dergisi, c. 36, sy. 3, ss. 2269–2277, 2015.
ISNAD Haddadnıa, Javad vd. “An Efficient Method for Detection of Masses in Mammogram Images”. Cumhuriyet Üniversitesi Fen Edebiyat Fakültesi Fen Bilimleri Dergisi 36/3 (Mayıs 2015), 2269-2277.
JAMA Haddadnıa J, Rahmanı-seryasat O, Ghayoumı-zadeh H, Rabıee H. An Efficient Method for Detection of Masses in Mammogram Images. Cumhuriyet Üniversitesi Fen Edebiyat Fakültesi Fen Bilimleri Dergisi. 2015;36:2269–2277.
MLA Haddadnıa, Javad vd. “An Efficient Method for Detection of Masses in Mammogram Images”. Cumhuriyet Üniversitesi Fen Edebiyat Fakültesi Fen Bilimleri Dergisi, c. 36, sy. 3, 2015, ss. 2269-77.
Vancouver Haddadnıa J, Rahmanı-seryasat O, Ghayoumı-zadeh H, Rabıee H. An Efficient Method for Detection of Masses in Mammogram Images. Cumhuriyet Üniversitesi Fen Edebiyat Fakültesi Fen Bilimleri Dergisi. 2015;36(3):2269-77.