Computer-aided mammography classification of malignant mass regions and normal regions based on novel texton features

2012 12th International Conference on Control, Automation, Robotics and Vision, ICARCV 2012

Volumn , Issue , 2012, Pages 1431-1436

  Li, Xi Zhao ^a   Williams, Simon ^a   Lee, Gobert ^a   Deng, Min ^b  

^a FLINDERS UNIVERSITY   (Australia)

^b SOUTH CHINA UNIVERSITY OF TECHNOLOGY   (China)

Author keywords

filter bank; Fisher Classifier; malignant; mammography classification; normal; ROC curve; texton

Indexed keywords

FISHER CLASSIFIER; MALIGNANT; MAMMOGRAPHY CLASSIFICATION; NORMAL; ROC CURVES; TEXTON;

FILTER BANKS; IMAGE CLASSIFICATION;

ROBOTICS;

EID: 84876063050     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICARCV.2012.6485399     Document Type: Conference Paper

References (16)

1. Heang-Ping Chan, DaTong Wei, M. A. Helvie, B. Sahiner, D. D. Adler, M. M. Goodsitt, and N. Petrick. Computer-aided classification of mammographic masses and normal tissue: linear discriminant analysis in texture feature space. Physics in Medicine and Biology, 40(5), 1995.
2. Y-C. Gong and S. Petroudi. Texture based mammogram classification and segmentation. In Michael Brady Susan M. Astley, Chris Rose, and Reyer Zwiggelaar, editors, Digital Mammography 8th International Workshop, IWDM 2006, Manchester, volume 4046. SpringerLink, 2006.
3. L. Hadjiiski, B. Sahiner, HP. Chan, N. Petrick, MA. Helvie, and M. Gurcan. Analysis of temporal changes of mammographic features: Computer-aided classification of malignant and benign breast masses. Medical Physics, 28(11):2309-2317, Nov 2001.
4. KR. Hoffman and JE. Gray. In the next decade automated computer analysis will be accepted sole method to seperate "normal" from "abnormal" radiological images. Medical Physics, 26(1):1-4, Jan 1999.
5. WP. Jr. Kegelmeyer, JM. Pruneda, PD. Bourland, A. Hillis, MW. Riggs, and ML. Nipper. Computeraided mammographic screening for spiculated lesions. Radiology, 191(2):331-337, May 1994.
6. Gobert N. Y. Lee. Classification of Masses in Digital Mammograms and Significance of Classification Performance. PhD thesis, School of Informatics and Engineering, Faculty of Science and Engineering, Flinders University, Mar 2004.
7. Xi-Zhao. Li, Simon Williams, and Murk J. Bottema. Intensity independent texture analysis in screening mammograms. In Digital Mammography 11th International Workshop, IWDM 2012, pages 474-481, 2012.
8. CE. Metz. Basic principles of roc analysis, Oct 1978.
9. CE. Metz. Roc methodology in radiologic imaging. Investigative radiology, 21(9):720-733, Sep 1986.
10. NR. Mudigonda, RM. Rangaraj, and JE. Desautels. Gradient and texture analysis for the classification of mammographic masses. IEEE Transactions on Medical Imaging, 19(10):1032-1043, Oct 2000.
11. S. Petroudi, T. Kadir, and M. Brady. Automatic classification of mammographic parenchymal patterns: a statistical approach. In Engineering in Medicine and Biology Society. Proceedings of the 25th Annual International Conference of the IEEE, volume 1, pages 798-801, Sep 2003.
12. Richard. Pfisterer and Farzin. Aghdasi. Tumor detection in digitized mammograms by image texture analysis. Optical Engineering, 40(2):209-216, Feb 2001.
13. B. Sahiner, Heang-Ping Chan, N. Petrick, Datong Wei, M. A. Helvie, D. D. Adler, and M. M. Goodsitt. Classification of mass and normal breast tissue: A convolution neural network classifier with spatial domain and texture images. IEEE Transactions on Medical Imaging, 15(5): 598-610, Oct 1996.
14. M. Varma and A. Zisserman. Texture classification: Are filter banks necessary? In Computer Vision and Pattern Recognision, 2003. Proceedings. 2003 IEEE Computer Society Conference on, volume 2, pages 691-698, Jun 2003.
15. Manik Varma and Andrew Zisserman. A statistical approach to texture classification from single images. International Journal of Computer Vision, 62(1-2):61-81, April-May 2005.
16. Carl J. Vyborny. Can computer help radiologists read mammograms? In Rediology, volume 191, pages 315- 317, May 1994.