Abstract
Purpose
Architectural distortion is an important sign of early breast cancer. We present methods for computer-aided detection of architectural distortion in mammograms acquired prior to the diagnosis of breast cancer in the interval between scheduled screening sessions.
Methods
Potential sites of architectural distortion were detected using node maps obtained through the application of a bank of Gabor filters and linear phase portrait modeling. A total of 4,224 regions of interest (ROIs) were automatically obtained from 106 prior mammograms of 56 interval-cancer cases, including 301 true-positive ROIs, and from 52 mammograms of 13 normal cases. Each ROI was represented by three types of entropy measures of angular histograms composed with the Gabor magnitude response, angle, coherence, orientation strength, and the angular spread of power in the Fourier spectrum, including Shannon’s entropy, Tsallis entropy for nonextensive systems, and Rényi entropy for extensive systems.
Results
Using the entropy measures with stepwise logistic regression and the leave-one-patient-out method for feature selection and cross-validation, an artificial neural network resulted in an area under the receiver operating characteristic curve of 0.75. Free-response receiver operating characteristics indicated a sensitivity of 0.80 at 5.2 false positives (FPs) per patient.
Conclusion
The proposed methods can detect architectural distortion in prior mammograms taken 15 months (on the average) before clinical diagnosis of breast cancer, with a high sensitivity and a moderate number of FPs per patient. The results are promising and may be improved with additional features to characterize subtle abnormalities and larger databases including prior mammograms.
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Knutzen AM, Gisvold JJ (1993) Likelihood of malignant disease for various categories of mammographically detected, nonpalpable breast lesions. Mayo Clinic Proc 68: 454–460
Matsubara T, Ichikawa T, Hara T, Fujita H, Kasai S, Endo T, Iwase T (2004) Novel method for detecting mammographic architectural distortion based on concentration of mammary gland, vol 1268. International congress series, Elsevier B.V., pp 867–871 (Proceedings of the 18th international congress and exhibition on computer assisted radiology and surgery (CARS2004))
Yankaskas BC, Schell MJ, Bird RE, Desrochers DA (2001) Reassessment of breast cancers missed during routine screening mammography: a community based study. Am J Roentgenol 177: 535–541
Burrell H, Evans A, Wilson A, Pinder S (2001) False-negative breast screening assessment: what lessons we can learn?. Clin Radiol 56: 385–388
Schneider MA (2001) Better detection: Improving our chances. In: Yaffe MJ (ed) Digital mammography: 5th international workshop on digital mammography. Medical Physics Publishing, Toronto, ON, Canada, pp 3–6
Tang J, Rangayyan RM, Xu J, Naqa IE, Yang Y (2009) Computer-aided detection and diagnosis of breast cancer with mammography: recent advances. IEEE Trans Inf Technol Biomed 13(2): 236–251
Doi K (2006) Diagnostic imaging over the last 50 years: research and development in medical imaging science and technology. Phys Med Biol 51: R5–R27
Baker JA, Rosen EL, Lo JY, Gimenez EI, Walsh R, Soo MS (2003) Computer-aided detection (CAD) in screening mammography: Sensitivity of commercial CAD systems for detecting architectural distortion. Am J Roentgenol 181: 1083–1088
Rangayyan RM, Prajna S, Ayres FJ, Desautels JEL (2008) Detection of architectural distortion in mammograms acquired prior to the detection of breast cancer using Gabor filters, phase portraits, fractal dimension, and texture analysis. Int J Comput Assist Radiol Surg 2(6): 347–361
van Dijck JAAM, Verbeek ALM, Hendriks JHCL, Holland R (1993) The current detectability of breast cancer in a mammographic screening program. Cancer 72(6): 1933–1938
Sameti M, Ward RK, Morgan-Parkes J, Palcic B (2009) Image feature extraction in the last screening mammograms prior to detection of breast cancer. IEEE J Sel Top Signal Process 3(1): 46–52
Rangayyan RM, Banik S, Desautels JEL (2010) Computer-aided detection of architectural distortion in prior mammograms of interval cancer. J Digit Imaging 23(5): 611–631
Banik S, Rangayyan RM, Desautels JEL (2011) Detection of architectural distortion in prior mammograms. IEEE Trans Med Imaging 30(2): 279–294
Broeders MJM, Onland-Moret NC, Rijken HJTM, Hendriks JHCL, Verbeek ALM, Holland R (2003) Use of previous screening mammograms to identify features indicating cases that would have a possible gain in prognosis following earlier detection. Eur J Cancer 39: 1770–1775
Nemoto M, Honmura S, Shimizu A, Furukawa D, Kobatake H, Nawano S (2009) A pilot study of architectural distortion detection in mammograms based on characteristics of line shadows. Int J Comput Assist Radiol Surg 4(1): 27–36
Karssemeijer N, te Brake GM (1996) Detection of stellate distortions in mammograms. IEEE Trans Med Imaging 15(5): 611–619
Tourassi GD, Delong DM, Floyd CE Jr (2006) A study on the computerized fractal analysis of architectural distortion in screening mammograms. Phys Med Biol 51(5): 1299–1312
Sampat MP, Markey MK, Bovik AC (2006) Measurement and detection of spiculated lesions. In: IEEE southwest symposium on image analysis and interpretation, pp 105–109. IEEE Computer Society
Guo Q, Shao J, Ruiz VF (2009) Characterization and classification of tumor lesions using computerized fractal-based texture analysis and support vector machines in digital mammograms. Int J Comput Assist Radiol Surg 4(1): 11–25
Nakayama R, Watanabe R, Kawamura T, Takada T, Yamamoto K, Takeda K (2008) Computer-aided diagnosis scheme for detection of architectural distortion on mammograms using multiresolution analysis. In: Proceedings of the 22nd international congress and exhibition on computer assisted radiology and surgery (CARS 2008), vol 3(1). Barcelona, Spain, pp S418–S419
Ayres FJ, Rangayyan RM (2007) Reduction of false positives in the detection of architectural distortion in mammograms by using a geometrically constrained phase portrait model. Int J Comput Assist Radiol Surg 1: 361–369
Sumkin JH, Holbert BL, Herrmann JS, Hakim CA, Ganott MA, Poller WR, Shah R, Hardesty LA, Gur D (2003) Optimal reference mammography: a comparison of mammograms obtained 1 and 2 years before the present examination. Am J Roentgenol 180: 343–346
Varela C, Karssemeijer N, Hendriks JHCL, Holland R (2005) Use of prior mammograms in the classification of benign and malignant masses. Eur J Radiol 56: 248–255
Majid AS, de Paredes ES, Doherty RD, Sharma NR, Salvador X (2003) Missed breast carcinoma: pitfalls and pearls. RadioGraphics 23: 881–895
Rangayyan RM, Banik S, Desautels JEL (2011) Detection of architectural distortion in prior mammograms using measures of angular distribution. In: Summers RM, van Ginneken B (eds) Proceedings of SPIE medical imaging 2011: computer aided diagnosis, Orlando, FL, February, vol 7963, pp 796308:1–9
Banik S, Rangayyan RM, Desautels JEL (2011) Rényi entropy of angular spread for detection of architectural distortion in prior mammograms. In: Proceedings of the 2011 IEEE International Symposium on Medical Measurements and Applications (MeMeA), pp 609–612, Bari, Italy
Banik S, Rangayyan RM, Desautels JEL (2011) Detection of architectural distortion in prior mammograms of interval cancer using measures of angular spread and Tsallis entropy. In: Proceedings of the 25th international congress and exhibition: computer assisted radiology and surgery, vol 6. Berlin, Germany, pp S188–S189
Alberta Cancer Board (2004) Screen test: Alberta program for the early detection of breast cancer–2001/03 Biennial Report. Alberta, Canada. http://www.cancerboard.ab.ca/screentest
Rangayyan RM (2005) Biomedical image analysis. CRC Press, Boca Raton, FL
Rao AR (1990) A taxonomy for texture description and identification. Springer, New York
Mudigonda NR, Rangayyan RM, Desautels JEL (2001) Detection of breast masses in mammograms by density slicing and texture flow-field analysis. IEEE Trans Med Imaging 20(12): 1215–1227
Haralick RM, Shanmugam K, Dinstein I (1973) Textural features for image classification. IEEE Trans Syst Man Cybern 3(6): 610–622
Shannon CE (1948) A mathematical theory of communication. Bell Syst Tech J 27:379–423, 623–656
Rényi A (1961) On measures of entropy and information. In: Proceedings of the 4th Berkeley symposium on mathematics, statistics and probability, vol 1. University of California Press, Berkeley, CA, pp 547–561
Zhang D, Jia X, Ding H, Ye D, Thakor NV (2010) Application of Tsallis entropy to EEG: quantifying the presence of burst suppression after asphyxial cardiac arrest in rats. IEEE Trans Biomed Engg 57(4): 867–874
Masi M (2005) A step beyond Tsallis and R ényi entropies. Phys Lett A 338(3–5): 217–224
Tsallis C (1988) Possible generalization of Boltzmann-Gibbs statistics. J Stat Phys 52(1): 479–487
Johal RS, Tirnakli U (2004) Tsallis versus Renyi entropic form for systems with q-exponential behaviour: the case of dissipative maps. Physica A 331(3–4): 487–496
Rodrigues PS, Giraldi GA, Chang R-F, Suri JS (2006) Non-extensive entropy for CAD systems of breast cancer images. In: The 19th Brazilian symposium on computer graphics and image processing, pp 121–128
Rodrigues PS, Giraldi GA (2009) Computing the q-index for Tsallis nonextensive image segmentation. In: Brazilian symposium on computer graphics and image processing, pp 232–237
Kinsner W (2005) A unified approach to fractal dimensions. In: Proceedings of the fourth IEEE international conference on cognitive informatics (ICCI). IEEE Computer Society, Irvine, CA, pp 58–72
Gabarda S, Cristóbal G (2008) Discrimination of isotrigon textures using the Rényi entropy of Allan variances. J Opt Soc Am A 25(9): 2309–2319
Li Y, Fan X, Li G (2006) Image segmentation based on Tsallis-entropy and Renyi-entropy and their comparison. In: IEEE international conference on industrial informatics, Singapore, pp 943–948
Sahoo P, Wilkins C, Yeager J (1997) Threshold selection using Renyi’s entropy. Pattern Recogn 30(1): 71–84
Mohanalin J, Kalra PK, Kumar N (2009) Extraction of micro calcification using non extensive property of mammograms. In: Proceedings of the 2009 IEEE international advance computing conference (IACC 2009) Patiala, India, pp 636–641
Duda RO, Hart PE, Stork DG (2001) Pattern classification, 2nd edn. Wiley-Interscience, New York
Haykin S (1999) Neural networks: a comprehensive foundation, 2nd edn. Prentice Hall, Englewood Cliffs, NJ
ROCKIT (2010) Kurt Rossmann Laboratories for Radiologic Image Research. ROC Software. http://www-radiology.uchicago.edu/krl/roc_soft6.htm. Accessed 20 Feb 2010
Chakraborty DP (2002) Statistical power in observer-performance studies: Comparison of the receiver operating characteristic and free-response methods in tasks involving localization. Acad Radiol 9(2): 147–156
Chakraborty DP (2008) Validation and statistical power comparison of methods for analyzing free-response observer performance studies. Acad Radiol 15(12): 1554–1566
Matsubara T, Ichikawa T, Hara T, Fujita H, Kasai S, Endo T, Iwase T (2003) Automated detection methods for architectural distortions around skinline and within mammary gland on mammograms. In: Lemke HU, Vannier MW, Inamura K, Farman AG, Doi K, Reiber JHC (eds) International congress series: proceedings of the 17th international congress and exhibition on computer assisted radiology and surgery. Elsevier, London, UK, pp 950–955
Heath M, Bowyer K, Kopans D, Moore R, Kegelmeyer WP (2001) The Digital Database for Screening Mammography. In: Yaffe MJ (eds) Proceedings of the fifth international workshop on digital mammography. Medical Physics Publishing, Toronto, pp 212–218
Suckling J, Parker J, Dance DR, Astley S, Hutt I, Boggis CRM, Ricketts I, Stamakis E, Cerneaz N, Kok S-L, Taylor P, Betal D, Savage J (1994) The Mammographic Image Analysis Society digital mammogram database. In: Gale AG, Astley SM, Dance DD, Cairns AY (eds) Digital mammography: proceedings of the 2nd international workshop on digital mammography. Elsevier, New York, UK, pp 375–378
Burhenne LJW, Wood SA, D’Orsi CJ, Feig SA, Kopans DB, O’Shaughnessy KF, Sickles EA, Tabar L, Vyborny CJ, Castellino RA (2000) Potential contribution of computer-aided detection to the sensitivity of screening mammography. Radiology 215(2): 554–562
Birdwell RL, Ikeda DM, O’Shaughnessy KF, Sickles EA (2001) Mammographic characteristics of 115 missed cancers later detected with screening mammography and the potential utility of computer-aided detection. Radiology 219(1): 192–202
Samuelson FW, Petrick N, Paquerault S (2007) Advantages and examples of resampling for CAD evaluation. In: 4th IEEE international symposium on biomedical imaging: from nano to macro (ISBI 2007). Arlington, VA, pp 492–495
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Banik, S., Rangayyan, R.M. & Desautels, J.E.L. Measures of angular spread and entropy for the detection of architectural distortion in prior mammograms. Int J CARS 8, 121–134 (2013). https://doi.org/10.1007/s11548-012-0681-x
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DOI: https://doi.org/10.1007/s11548-012-0681-x