Texture-based features for classification of mammograms using decision tree

Neural Computing and Applications

Volumn 23, Issue 3-4, 2013, Pages 1011-1017

  Mohanty, Aswini Kumar ^a   Senapati, Manas Ranjan ^b   Beberta, Swapnasikta ^c   Lenka, Saroj Kumar ^d  

^a SIKSHA O ANUSANDHAN DEEMED TO BE UNIVERSITY   (India)

^b Krupajal Group Of Institutions   (India)

^c BPUT   (India)

^d MODY UNIVERSITY OF SCIENCE AND TECHNOLOGY   (India)

Author keywords

Benign mass; Decision tree; Gray level co occurrence matrix; Gray level run length matrix, mammograms; Malignant mass; Receiver operating characteristics; Texture features; Textures analysis

Indexed keywords

BENIGN MASS; GRAY LEVEL CO-OCCURRENCE MATRIX; MALIGNANT MASS; RECEIVER OPERATING CHARACTERISTICS; RUN LENGTH; TEXTURE FEATURES;

DATA MINING; DECISION TREES; DISEASES; FEATURE EXTRACTION; MATRIX ALGEBRA; TEXTURES; X RAY SCREENS;

MAMMOGRAPHY;

EID: 84884574830     PISSN: 09410643     EISSN: None     Source Type: Journal    
DOI: 10.1007/s00521-012-1025-z     Document Type: Article

References (47)

1. Majid AS, de Paredes ES, Doherty RD, Sharma N, Salvador X (2003) Missed breast carcinoma: pitfalls and pearls. Radiographics 23: 881-895.
2. Osmar RZ, Antonie M-L, Coman A (2002) Mammography classification by association rulebased classifier. MDM/KDD2002 International workshop on multimedia data mining with (ACM SIGKDD 2002, Edmonton, Alberta, Canada, 17-19 July 2002), pp 62-69.
3. Xie X, Gong Y, Wan S, Li X (2005) Computer aided detection of SARS based on radiographs data mining. In: Proceedings of the 2005 IEEE engineering in medicine and biology 27th annual conference Shanghai, China, 1-4 Sept 2005.
4. Shuyan W, Mingquan Z, Guohua G (2005) Application of fuzzy cluster analysis for medical image data mining. In: Proceedings of the IEEE international conference on mechatronics & automation Niagara Falls, Canada, July 2005.
5. Jensen R, Qiang S (2004) Semantics preserving dimensionality reduction: rough and fuzzy-rough based approaches. IEEE Trans Knowl Data Eng 16: 1457-1471.
6. Walid E, Hakim H (2006) A new cost sensitive decision tree method application for mammograms classification. IJCSNS Int J Comp Sci Netw Secur, 6 No. 11.
7. Liu Y, Zhang D, Lu G (2008) Region based image retrieval with high-level semantics using decision tree learning. Pattern Recognit 41: 2554-2570.
8. Polat K, Günes S (2009) A novel hybrid intelligent method based on C4. 5 decision tree classifier and one-against-all approach for multi-class classification problems. Expert Syst Appl 36: 1587-1592.
9. Chen C, Lee G (1997) Image segmentation using multitiresolution wavelet analysis and expectation maximum (em) algorithm for mammography. Int J Imaging Syst Technol 8(5): 491-504.
10. Wang T, Karayaiannis N (1998) Detection of microcalcifications in digital mammograms using wavelets. IEEE Trans Med Imaging 17(4): 498-509.
11. Christiyanni I et al (2000) Fast detection of masses in computer aided mammography. IEEE Signal Process Mag 54-64.
12. Faderl S, Keating MJ, Do KA, Liang SY, Kantarjian HM, O'Brien S et al (2002) Expression profile of 11 proteins and their prognostic significance in patients with chronic lymphocytic leukemia (CLL). Leukemia 16: 1045-1052.
13. Quinlan J (1993) C4. 5: programs for machine learning. Morgan Kaufmann, San Francisco, CA.
14. http://www. rulequest. com/see5-info. html. http://marathon. csee. usf. edu/Mammography/Database. html.
15. Cherif chaabani A, Boujelben A, Mahfoudhi A, Abid M (2010) An automatic-pre-processing method for mammographic images. JDCTA Int J Digit Content Technol Appl 4(3): 190-201.
16. Hanmandlu M, Madasu VK, Vasikarla S (2004) A fuzzy Approach to texture segmentation. Proc Int Conf Inf Technol: Coding and Computing 1: 636-642.
17. Chalana V, Kim Y (1997) A methodology for evaluation of boundary algorithms on medical images. IEEE Trans Med Imaging 16(5): 642-652.
18. Perkins WA (1980) Area segmentation of images using edge points. IEEE Trans Pattern Anal Mach Intell PAMI-2: 8-15.
19. Torheim G, Godtliebsen F, Axelson D, Kvistad KA, Haraldseth O, Rinck PA (2001) Feature extraction and classification of dynamic contrast-enhanced T2*-weighted breast image data. IEEE Trans Med Imaging 20: 1293-1301.
20. Sohail A, Bhattacharya P, Mudur SP, Krishnamurthy S (2011) Classification of ultrasound medical images using distance based feature selection and fuzzy-SVM. Pattern Recognit Image Anal, Lecture notes in computer science. 6669: 176-183. doi: 10. 1007/978-3-642-21257-4_22.
21. Haralick RM, Shanmugam K, Dinstein I (1973) Textural features for image classification. IEEE Trans Syst Man Cybern SMC-3: 610-621.
22. Gupta R, Undrill PE (1997) The use of texture analysis to identify suspicious masses in mammography. Phys Med Biol 15: 835-855.
23. Chan HP et al (1995) Computer-aided classification of mammographic masses and normal tissue: linear discriminant analysis in texture feature space. Phys Med Biol 40: 857-876.
24. Wei D, Chan HP, Helvie MA, Sahiner B, Petrick N, Adler DD, Goodsitt MM (1995) Classification of mass and normal breast tissue on digital mammograms: multiresolution texture analysis. Med Phys 22: 1501-1513.
25. Gibbs P, Turnbull LW (2003) Textural analysis of contrast-enhanced MR images of the breast. Magn Reson Med 50: 92-98.
26. Mohanty AK, Senapati MR, Lenka SK (2012) A novel image mining technique for classification of mammograms using hybrid feature selection. Neural Comput Appl. doi: 10. 1007/s00521-012-0881-x.
27. Conners RW, Harlow CA (1980) A theoretical comparison texture algorithms. IEEE Trans Pattern Anal Mach Intell 2: 204-222.
28. Arivazhagan S, Ganesan L (2003) Texture classification using wavelet transform. Pattern Recognit Lett 24: 1513-1521.
29. Hiremath PS, Shivashankar S (2006) Texture classification using wavelet packet decomposition. ICGSTs GVIP J 6(2): 77-80.
30. Albregtsen F (1995) Statistical texture measures computed from gray level run-length matrices. University of Oslo.
31. Hall-Beyer M (2004) GLCM texture: a tutorial v. 2. 7. 1, on-line document, www. fp. ucalgary. ca/mhallbey/tutorial. htm.
32. Haralick RM, Shanmugam K, Dinstein I (1973) Texture features for image classification. IEEE Trans SMC 3(6): 610-621.
33. Kulak E (2002) Image analysis of textural Features for Content based retrieval. Thesis, Sabanci University.
34. Tang X (1998) Texture information in run-length matrices. IEEE Trans Image Process 7(11): 1602-1609.
35. Chu A, Sehgal CM (1990) Use of gray value distribution of run lengths for texture analysis. Pattern Recognit Lett 415-420.
36. Dasarathy BV, Holder EB (1991) Image characterizations based on joint gray level-run length distributions. Pattern Recognit Lett 12(8): 497-502.
37. Nisbet R, Elder J, Miner G (2009) Handbook of statistical analysis and data mining applications. Academic Press, Burlington.
38. Quinlan J (1986) Induction of decision trees. Machine Learning 1: 81-106.
39. Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and regression trees. Wadsworth & Brooks/Cole Advanced Books & Software, Monterey, CA.
40. Loh WY, Shih YS (1997) Split selection methods for classification trees. Stat Sin 7: 815-840.
41. Michael JA, Gordon SL (1997) Data mining technique: for marketing, sales and customer support. Wiley, New York.
42. Han JW, Kamber M (2006) Data mining concepts and techniques, 2nd edn. Morgan Kaufmann Publishers, San Francisco.
43. Dudoit S, Fridlyand J, Speed TP (2000) Comparison of discrimination methods for the classification of tumors using gene expression data. Technical report 576, Department of Statistics, University of California at Berkeley, Berkeley, CA.
44. Quinlan JR (1996) Improved use of continuous attributes in C4. 5. J Artif Intell Res 4: 77-90.
45. Murthy SK (1998) Automatic construction of decision trees from data: a multi-disciplinary survey. Data Min Knowl Disc 2(4): 345-389.
46. Zou KH, O'Malley AJ, Mauri L (2007) Receiver-operating characteristic analysis for evaluating diagnostic tests and predictive models. J Am Heart Assoc 654-657. doi: 10. 1161/CIRCULATIONAHA. 105. 594929.
47. Park SH, Goo JM, Jo C-H (2004) Receiver operating characteristic (roc) curve: practical review for radiologists. Korean J Radiol 5(1): 11-18.