Structural analysis of tissue in contiguous micro calcifications inmammograms for breast cancer identification

Bhagwati Charan Patel*, G. R. Sinha**
* Associate Professor (IT), Shri Shankaracharya College of Engg. & Tech, Bhilai, India.
** Professor & Head (IT), Shri Shankaracharya College of Engg. & Tech, Bhilai, India.
Periodicity:November - January'2011
DOI : https://doi.org/10.26634/jfet.6.2.1322

Abstract

Identification of micro-calcifications (MCs) is challenged by the presence of dense breast tissue, resulting in low specificity values and thus in unnecessary biopsies. The current study investigates whether structural properties of the tissue in contiguous MCs can contribute to breast cancer identification.  A sample of 75 dense mammographic images affected with malignant and benign were  collected from BSR APPOLO for cancer research and diagnosis and included in the digital Database. Regions of interest (ROIs) containing the MCs were pre-processed using a wavelet based contrast enhancement method, followed by local thresholding to segment MCs; the segmented MCs were excluded from original image ROIs, and the remaining area (in contiguous tissue) was subjected to structural analysis. Four categories of structural features (first order statistics, co-occurrence matrices features, run length matrices features and Laws’ structural energy measures) were extracted from the contiguous tissue. The ability of each feature category in discriminating malignant from benign tissue was investigated using a k-closest neighbor (kCN) classifier. Receiver Structural Characteristic (RSC) analysis was conducted for classifier performance evaluation of the individual structural feature categories and of the combined classification scheme. The best performance was achieved by the combined classification scheme yielding an area under the RSC curve of 0.96 (sensitivity 94.4%, specificity 80.0%). Structural analysis of tissue in contiguous MCs shows promising results in computer-aided diagnosis of breast cancer and may contribute to the reduction of unnecessary biopsies.

Keywords

Microcalcification,Structural analysis,Tissue,Receiver structural characteristic.

How to Cite this Article?

Patel , B. C., and Sinha, G. R. (2011). Structural Analysis Of Tissue In Contiguous Micro Calcifications In Mammograms For Breast Cancer Identification. i-manager’s Journal on Future Engineering and Technology, 6(2), 20-27. https://doi.org/10.26634/jfet.6.2.1322

References

[1]. Majid, A.S., de Paredes, E.S., Doherty, R.D., and Sharma, N.R. (2003). “Missed breast carcinoma: pitfalls and pearls”, Radiographics, 23: 881-95.
[2]. Kopans, D.B. (2002). “The positive predictive value of mammography”, AJR Am J Roentgenol, 158: 521-6.
[3]. Kolb, T.M., Lichy J., and Newhouse, J.H. (2002). “Comparison of the performance of screening mammography, physical examination, and breast US and evaluation of factors that influence them: an analysis of 27,825 patient evaluations”, Radiology, 225:165-75.
[4]. Bird, R.E., Wallace, T.W., and Yankaskas, B.C. (1992). “Analysis of cancers missed at screening mammography”, Radiology, 184: 613-17.
[5]. Van Gils CH, Otten J.D., Verbeek A.L., Hendriks, J.H., and Holland, R. (1998). “Effect of mammographic breast density on breast cancer screening performance: a study in Nijmegen, the Netherlands”, J Epidemiol Commun Health, 52:267-71.
[6]. Cole, E.B., Pisano, E.D., Kistner, E.O., Muller, K.E., Brown, M.E., Feig, S.A., et.al (2003). “Diagnostic accuracy of digital mammography in patients with dense breasts who underwent problem solving mammography: effects of image processing and lesion type”. Radiology, 226:153-60.
[7]. American Cancer Society. (1998). ”Cancer facts and figures“, Atlanta, GA: American Cancer Society.
[8]. Sampat, P.M., Markey, M.K., and Bovik, A.C. (2005). “Computer-aided detection and diagnosis in mammography”, In: Bovik AC, editor. Academic Press, 1195-217.
[9]. Markey, M.K., Lo, J.Y., and Floyd, C.E., Jr. (2002). “Differences between computer-aided diagnosis of breast masses and that of calcifications”. Radiology 223:469-93.
[10]. Cheng, H.D., Cai, X., Chen, X., Hu, L., and Lou, X. (2003). “Computer-aided detection and classification of mic RS Calcifications in mammograms: a survey”. Pattern Recognition 36:2967-91.
[11]. Wu, Y., Giger, M.L., Doi, K., Vyborny, C.J., Schmidt, R.A., and Metz, C.E. (1993). “Artificial neural networks in mammography: application to decision making in the diagnosis of breast cancer”. Radiology 187:81-7.
[12]. Baker, J.A., Kornguth, P.J., Lo, J.Y., and Floyd, C.E. (1996). “Artificial neural network: improving the quality of breast biopsy recommendations”. Radiology 198:131-5.
[13]. Lo, J.Y., Baker, J.A., Kornguth, P.J., Iglehart, J.D., Floyd, C.E. (1997). “Predicting breast cancer invasion with artificial neural networks on the basis of mammographic features”, Radiology 203:159-63.
[14]. Shen, L., Rangayyan, R.M., Desautels, J.E.L. (1994). “Application of shape analysis to mammographic calcifications”, IEEE Trans Med Imaging, 13:263-74.
[15]. Jiang, Y., Nishikawa, R.M., Wolverton, D.E., Metz, C.E., Giger, M.L., Schmidt, R.A., et.al (1996). “Malignant and benign clustered micRSCalcifications: automated feature analysis and classification”, 198:671-8.
[16]. Chan, H.P., Sahiner, B., Lam, K.L., Helvie, M.A., and Goodsitt (1998). 'Computerized analysis of mammographic micRSCalcifications in morphological and structural feature spaces'. Med Phys 25:2007–19.
[17]. Tsujii, O., Freedman, M.T., and Mun, S.K. (1999). “ Classification of microcalcifications in digital mammograms using trend-oriented radial basis function neural network”, Pattern Recognition 32:891-903.
[18]. Veldkamp, W.J.H., Karssemeijer, N., Otten, J.D.M., and Hendriks, J.H.C.L. (2000). “Automated classification of clustered micRSCalcifications into malignant and benign types”. Med Phys 27:2600-8.
[19]. Markopoulos, C., Kouskos, E., Koufopoulos, K., Kyriakou, V., and Gogas, J. (2001). “Use of artificial neural networks (computer analysis) in the diagnosis of micRSCalcifications on mammography”, 37:60-5.
[20]. Verma, B., and Zakos, J.A. (2001). “Computer-aided diagnosis system for digital mammograms based on fuzzy-neural and feature extraction techniques”. IEEE Trans Inform Technol Biomed 5:44-54.
[21]. De Santo, M., Molinara, M., Tortorella, F., and Vento, M. (2003). “Automatic classification of clustered micRSCalcifications by a multiple expert system”. Pattern Recognition 36:1445-77.
[22]. Lee, S.K., Chung, P., Chang, C.I., Lo, C.S., Lee, T., and Hsu, G.C. (2003). “ Classification of clustered micRSCalcifications using a shape cognition neural network”, Neural Netw 16:121-32.
[23]. Kallergi, M. (2004). “Computer-aided diagnosis of mammographic microcalcification clusters”. Med Phys 31:314-26.
[24]. Wei, L., Yang, Y., Nishikawa, R.M., and Jiang, Y. (2005). “A study on several machine-learning methods for classification of malignant and benign clustered microcalcifications”. IEEE Trans Med Imaging, 24:349-80.
[25]. Papadopoulos, A., Fotiadis, D.I., and Likas, A. (2005). “Characterization of clustered microcalcifications in digitized mammograms using neural networks and support vector machines”. Artif Intell Med, 34:139-50.
[26]. Dhawan, A.P., Chitre, Y., and Kaiser-Bonasso, C. (1996). “Analysis of mammographic micRSCalcifications using gray-level image structure features”. IEEE Trans Med Imaging, 15:244-59.
[27]. Soltanian, H., Rafee, F., and Pourabdollah, D. (2004). ”Comparison of multiwavelet, wavelet, Haralick, and shape features for microcalcification classification in mammograms”. Pattern Recognition, 35:1973-86.
[28]. Kramer, D., and Aghdasi, F. (1999). “Structural analysis techniques for the classification of microcalcifications in digitized mammograms”.
[29]. Sakka, E., Prentza, A., and Koutsouris, D. (2006). ”Classification algorithms for microcalcifications in mammograms“, (review). Oncol Rep1049-56.
[30]. Veldkamp, W.J.H., and Karssemeijer, N. (1996). ”Influence of segmentation on classification of microcalcifications in digital mammography ”. In: th Proceedings of the 18 Annual International Conference of the IEEE Engineering in Medicine and Biology Society; 1996 October 31 – November 3. Amsterdam, Netherlands: Institute of Electrical and Electronic Engineers, Inc.
[31]. Paquerault, S., Yarusso, L.M., Papaioannou, J., Jiang, Y., and Nishikawa, R.M. (2004). “Radial gradient b Ased segmentation of mammographic microcalcifications” Observer evaluation and effect on CAD performance. Med Phys 31:2644-57.
[32]. Thiele, D.L., Kimme-Smith, C., Johnson, T.D., McCombs, M., and Bassett, L.W. (1996). “Using tissue structural in contiguous calcification clusters to predict benign vs malignant outcomes”. Med Phys 23:549-55.
[33]. Sakellaropoulos, P., Costaridou, L., and Panayiotakis, G. (1999). “An image visualization tool in mammography”. Med Inform 24: 53-73.
[34]. Sakellaropoulos, P., Costaridou, L., and Panayiotakis, G. (2000). “Using component technologies for web based wavelet enhanced mammographic image visualization”. Med Inform 25:171-81.
[35]. Sakellaropoulos, P., Costaridou, L., and Panayiotakis, G. (2003). A” wavelet- based spatially adaptive method for mammographic contrast enhancement”. Phys Med Biol 44:787-803.
[36]. Costaridou, L., Sakellaropoulos, P., Skiadopoulos, S., and Panayiotakis, G. (2005). “Locally adaptive wavelet contrast enhancement”. In: Costaridou L, editor. Medical image analysis methods. Boca Raton, FL: Taylor & Francis Group LCC,MCRC Press, 225-70.
[37]. Costaridou, L., Skiadopoulos, S., Karahaliou, A., Sakellaropoulos, P, and Panayiotakis G. (2005). ”On the lesion specific enhancement hypothesis in mammography”. In: Proceedings of 14th International Conference of Medical Physics, ICMP; 2005 September 14-17; Nuremberg, Germany. Berlin, Germany: Fachverlag Schiele & Schon GmbH.
[38]. Gonzalez, R.C., and Woods, R.E. editors. (2002). “Digital image Processing”. Upper Saddle River, NJ: Prentice-Hall, Inc.
[39]. Dudani, S.A (1976). The distance weighted nearest neighbour rule. IEEE Trans Systems Man Cybern SMC- 6:325-7.
[40]. Ursin, G., Hovanessian-Larsen L., Parisky, Y.R., Pike, M.C., Wu, A.H. (2005). Greatly increased occurrence of breast cancers in areas of mammographically dense tissue. Breast Cancer Res 2005; 7: R605-8.
If you have access to this article please login to view the article or kindly login to purchase the article

Purchase Instant Access

Single Article

North Americas,UK,
Middle East,Europe
India Rest of world
USD EUR INR USD-ROW
Online 15 15

Options for accessing this content:
  • If you would like institutional access to this content, please recommend the title to your librarian.
    Library Recommendation Form
  • If you already have i-manager's user account: Login above and proceed to purchase the article.
  • New Users: Please register, then proceed to purchase the article.