IPES: An Image Processing-Enabled Expert System for the Detection of Breast Malignant Tumors
DOI:
https://doi.org/10.14738/jbemi.36.2346Keywords:
Image processing, expert systems, diagnosis of breast masses, mammographyAbstract
Mammography is an influential screening tool for preliminary detection of breast carcinoma. However, Interpreting mammograms is exhausting, particularly in the screening context. Moreover, sensitivity of mammography based screening is influenced by image quality and the experience level of radiologist. Thus, computer-aided diagnosis (CAD) programs can be utilized as a second-opinion tools that enhance the performance of radiologists, by consolidating sensitivity rates in contrast with those taken by double readings. The current paper is directed towards the integration of image processing and the rule-based reasoning into a diagnostic expert system for breast tumors. A proposed system termed IPES (Image Processing-enabled Expert System) is developed for the detection of breast malignant tumors, appear in mammography in three steps: (1) segmentation of mammographic masses from both pectoral muscle region and breast tissues, (2) Characterization of segmented masses upon the standards of Breast Imaging Reporting and Data System (BI-RADS) by: (i) shape-relevant features; (ii) margin characteristics; and (iii) density features, and (3) diagnosis of mass type upon some inference rules. The data set used for testing IPES contained 540 samples obtained from the standard Digital Database for Screening Mammography (DDSM). The Receiver Operator Characteristic (ROC) curves have been employed to evaluate the sensitivities and specificities of the system. Finally, the results reveal the efficacy of IBES in discriminating both malignant and benign breast masses.
References
(1) Niwas, S.I., Palanisamy, P., Chibbar, R., Zhang, W, An expert support system for breast cancer diagnosis using color wavelet features. J Med Syst., 2012. 36(5): p. 3091-3102.
(2) Hayat, M. A., Cancer imaging: lung and breast carcinomas, 2008, Elsevier Academic Press: USA.
(3) Amir, E., Freedman, O.C., Seruga, B., Evans, D.G., Assessing women at high risk of breast cancer: a review of risk assessment models. J Natl Cancer Inst., 2010. 102(10): p. 680–691.
(4) Wang, X., Li, L., Liu, W., Xu, W., Lederman, D., Zheng, B., An interactive system for computer-aided diagnosis of breast masses. J Digit Imaging, 2012. 25(5): p. 570–579.
(5) Shi, X., Cheng, H.D., Hu, L., Ju, W., Tian, J., Detection and classification of masses in breast ultrasound images. Digit Signal Process., 2010. 20(3): p. 824-836.
(6) L. de Oliveira Martins, Silva, A.C., A.C. de Paiva, Gattass, M., Detection of breast masses in mammogram images using growing neural gas algorithm and Ripley’s K function. J Sign Process Syst., 2009. 55(1): p. 77–90.
(7) Jackson, V., Hendrick, R., Feig, S., Kopans, D., Imaging of the radiographically dense breast. Radiology, 1993. 188(2): p. 297–301.
(8) Dennis, M.A., Parker, S.H., Klaus, A.J., Stavros, A.T., Kaske, T., Clark, S.B., Breast biopsy avoidance: The value of normal mammograms and normal sonograms in the setting of a palpable lump. Radiology, 2001. 219(1): p. 186–191.
(9) Sivaramakrishna, R., Powell, K.A., Lieber, M.L., Chilcote, W.A., Shekhar, R., Texture analysis of lesions in breast ultrasound images. Comput. Med. Imaging Graph., 2002. 26(5): p. 303–307.
(10) Buist, D.S., Anderson, M.L., Haneuse, S.J., Sickles, E.A., Smith, R.A., Carney, P.A., Taplin, S.H., Rosenberg, R.D., Geller, B.M., Onega, T.L., Monsees, B.S., Bassett, L.W., Yankaskas, B.C., Elmore, J.G., Kerlikowske, K., Miglioretti, D.L., Influence of annual interpretive volume on screening mammography performance in the United States. Radiology, 2011. 259(1): p. 72–84.
(11) Brown, J., Bryan, S., Warren, R., Mammography screening: An incremental cost effectiveness analysis of double versus single reading of mammograms. BMJ, 1996. 312(7034): p. 809–812.
(12) Ramos-Pollán, R., Guevara-López, M.A., Suárez-Ortega, Díaz-Herrero, G., Franco-Valiente, J.M., Rubio-Del-Solar, M., González-de-Posada, N., Vaz, M.A., Loureiro, J., Ramos, I., Discovering mammography-based machine learning classifiers for breast cancer diagnosis. J Med Syst., 2012. 36(4): p.
-2269.
(13) Markey, M.K., Bovik. A.C., Computer-Aided Detection and Diagnosis in Mammography, in Handbook of Image and Video Processing, A.C. Bovik, Editor 2005, New York: Academic, p. 1195-1217.
(14) Dhaliwal, J.S., Benbasat, I., The Use and Effects of Knowledge-Based System Explanations: Theoretical Foundations and a Framework for Empirical Evaluation. Information Systems Research, 1996. 7(3): p. 342-366.
(15) Karabatak, M., Cevdet Ince, M., An expert system for detection of breast cancer based on association rules and neural network. Expert Systems with Applications, 2009. 36(2): p. 3465–3469.
(16) Garibaldi J.M., et al., Incorporation of expert variability into breast cancer treatment recommendation in designing clinical protocol guided fuzzy rule system models. Journal of Biomedical Informatics, 2012. 45(3): p. 447–459.
(17) Saraiva, R., et al., Early diagnosis of gastrointestinal cancer by using case-based and rule-based reasoning. Expert Systems with Applications, 2016. 61(1): p. 192–202.
(18) American College of Radiology, The ACR Breast Imaging Reporting and Data System (BI-RADS), 4th ed., American College of Radiology, Reston,VA, 2003.
(19) Wei, C.-H., Chen, S.Y., Liu, X., Mammogram retrieval on similar mass lesions. Computer Methods and Programs in Biomedicine, 2012. 106(3): p. 234–248.
(20) Zhu, L., Sun, P.-C., Bartsch, D.-U., Freeman, W.R., Fainman, Y., Wave-front generation of Zernike polynomial modes with a micromachined membrane deformable mirror for Optical Metrology. Applied Optics, 1999. 38(28): p. 6019-6026.
(21) Walia, E., Goyal, A., Brar, Y.S., Zernike moments and
LDP weighted patches for content based image retrieval. Signal, Image and Video Processing, 2014. 8(3): p. 577-594.
(22) Goyal, A., Walia, E., Variants of dense descriptors and Zernike moments as features for accurate shape-based image retrieval. Signal, Image and Video Processing, 2014. 8(7): p. 1273-1289.
(23) Yang, M., Kpalma, K., Ronsin, J., Shape-based invariant feature extraction for object recognition, In Advances in Reasoning-Based Image Processing, Kountchev et al., Editor 2012, Verlag Berlin Heidelberg: Springer, p. 255–314.
(24) Ma, Z.M., Zhang, G., Yan, L., Shape feature descriptor using modified Zernike moments. Pattern Anal Applic., 2011. 14(1): p. 9–22.
(25) Smith, S.M., Brady, J.M., SUSAN-a new approach to low level image processing. International Journal of Computer Vision, 1997. 23(1): 45-78.
(26) Wei, C-H, Li, C-T. Content-Based Retrieval for Mammograms, In Artificial Intelligence for Maximizing Content Based Image Retrieval, Z. Ma, Editor 2008, Hershey (PA, USA): Idea Group Publishing, p. 313-339.
(27) Heikkilä, M., Pietikäinen, M., Schmid, C., Description of Interest Regions with Center Symmetric Local Binary Patterns, In 5th Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP '06), P. Kalra, S. Peleg, Editor 2006, Lecture Notes in Computer Science (LNCS), volume 4338, Springer-Verlag, p.58-69.
(28) Digital Database for Screening Mammography
a. http://marathon.csee.usf.edu/Mammography/Database.html. Accessed 21 May 2015.
. Bahoura, M., Pattern recognition methods applied to respiratory sounds classification into normal and wheeze classes. Computers in Biology and Medicine, 2009. 39(9): p. 824–843.
