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Erschienen in:
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2020 | OriginalPaper | Buchkapitel

13. Advanced Medical Imaging Analytics in Breast Cancer Diagnosis

verfasst von : Yinlin Fu, Bhavika K. Patel, Teresa Wu, Jing Li, Fei Gao

Erschienen in: Women in Industrial and Systems Engineering

Verlag: Springer International Publishing

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Abstract

Modern imaging technique provides a fast, noninvasive means to study physiologic, metabolic, and molecular processes in the body. Imaging is the primary means in clinical cancer practice to facilitate diagnosis, prognosis, and treatment evaluation. While breast cancer contributes to 25% of morbidity in all cancer and is the second leading cause of cancer death in women, it is also one of the most treatable malignancies if detected early. In this chapter, we present an overview of research using advanced imaging analytics tools on Digital Mammography (DM) to improve the sensitivity and specificity of breast cancer detection. Currently, there are two dominating trends in the advanced imaging analytics field: texture analysis and deep learning. We implement three texture analysis algorithms: Gray Level Co-Occurrence Matrix (GLCM), Local binary patterns (LBP), and Gabor Filter and one deep learning network: ResNet. Gradient Boosted Tree (GBT) classifier is then developed on the features to diagnose the lesion as malignant vs. benign. The classifier using texture features from each texture analysis algorithm has an accuracy of 0.82, 0.72, and 0.72 for GLCM, LBP, and Gabor, respectively. If the texture features from different texture analysis algorithms are pooled together, the classifier has an accuracy of 0.81. The same classifier using features extracted from ResNet has an accuracy of 0.89 indicating the potentials of deep learning in medical imaging for disease diagnosis.

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Vorheriges Kapitel Improving Patient Safety in the Patient Journey: Contributions from Human Factors Engineering
Nächstes Kapitel Decision-Making in Sequential Adaptive Clinical Trials, with Implications for Drug Misclassification and Resource Allocation
Literatur
Abbey CK, Nosrateih A, Sohl-Dickstein J, Yang K, Boone JM (2012) Non-Gaussian statistical properties of breast images. Med Phys 39(11):7121–7130CrossRef
American College of Radiology (1998) BI-RADS Committee, “breast imaging reporting and data system”. Radiol Clin North Am 40:409–430
Antropova N, Huynh BQ, Giger ML (2017) A deep feature fusion methodology for breast cancer diagnosis demonstrated on three imaging modality datasets. Med Phys 44(10):5162–5171CrossRef
Arevalo J, González FA, Ramos-Pollán R, Oliveira JL, Guevara Lopez MA (2016) Representation learning for mammography mass lesion classification with convolutional neural networks. Comput Methods Prog Biomed 127:248–257CrossRef
Bar Y, Diamant I, Wolf L, Greenspan H (2015) Deep learning with non-medical training used for chest pathology identification. In: Medical imaging 2015: computer-aided diagnosis, vol 9414, p 94140V. International Society for Optics and Photonics
Becker AS, Marcon M, Ghafoor S, Wurnig MC, Frauenfelder T, Boss A (2017) Deep learning in mammography diagnostic accuracy of a multipurpose image analysis software in the detection of breast cancer. Investig Radiol 52(7):434–440CrossRef
Carneiro G, Nascimento J, Bradley AP (2017) Automated analysis of unregistered multi-view mammograms with deep learning. IEEE Trans Med Imaging 36(11):2355–2365CrossRef
Cha KH, Hadjiiski L, Samala RK, Chan H-P, Caoili EM, Cohan RH (2016) Urinary bladder segmentation in CT urography using deep-learning convolutional neural network and level sets. Med Phys 43(4):1882–1896CrossRef
Choi JY, Ro YM (2012) Multiresolution local binary pattern texture analysis combined with variable selection for application to false-positive reduction in computer-aided detection of breast masses on mammograms. Phys Med Biol 57(21):7029–7052CrossRef
Davnall F et al (2012) Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice? Insights Imaging 3(6):573–589CrossRef
do Nascimento MZ, Martins AS, Neves LA, Ramos RP, Flores EL, Carrijo GA (2013) Classification of masses in mammographic image using wavelet domain features and polynomial classifier. Expert Syst Appl 40(15):6213–6221CrossRef
Ferrari RJ, Rangayyan RM, Desautels JEL, Frère AF (2001) Analysis of asymmetry in mammograms via directional filtering with Gabor wavelets. IEEE Trans Med Imaging 20(9):953–964CrossRef
Ferrari RJ, Rangayyan RM, Desautels JEL, Borges RA, Frère AF (2004) Automatic identification of the pectoral muscle in mammograms. IEEE Trans Med Imaging 23(2):232–245CrossRef
Gao F, Zhang M, Wu T, Bennett KM (2016) 3D small structure detection in medical image using texture analysis. In: 2016 38th annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp 6433–6436
Gargouri N, Dammak Masmoudi A, Sellami Masmoudi D, Abid R (2012) A new GLLD operator for mass detection in digital mammograms. Int J Biomed Imaging 2012:765649CrossRef
Haralick RM, Shanmugam K, Dinstein I (1973) Textural features for image classification. IEEE Trans Syst Man Cybernet SMC-3(6):610–621CrossRef
Hubbard RA, Kerlikowske K, Flowers CI, Yankaskas BC, Zhu W, Miglioretti DL (2011) Cumulative probability of false-positive recall or biopsy recommendation after 10 years of screening mammography. Ann Intern Med 155(8):481CrossRef
Huynh BQ, Li H, Giger ML (2016) Digital mammographic tumor classification using transfer learning from deep convolutional neural networks. J Med Imaging 3(3):34501CrossRef
Jadoon MM, Zhang Q, Haq IU, Butt S, Jadoon A (2017) Three-class mammogram classification based on descriptive CNN features. Biomed Res Int 2017:3640901CrossRef
Jain AK, Ratha NK, Lakshmanan S (1997) Object detection using gabor filters. Pattern Recogn 30(2):295–309CrossRef
Jona JB, Nagaveni N (2014) Ant-cuckoo colony optimization for feature selection in digital mammogram. Pak J Biol Sci 17(2):266–271CrossRef
Kallenberg M et al (2016) Unsupervised deep learning applied to breast density segmentation and mammographic risk scoring. IEEE Trans Med Imaging 35(5):1322–1331CrossRef
Kashyap KL, Bajpai MK, Khanna P (2017) Globally supported radial basis function based collocation method for evolution of level set in mass segmentation using mammograms. Comput Biol Med 87:22–37CrossRef
Kashyap KL, Bajpai MK, Khanna P, Giakos G (2018) Mesh‐free based variational level set evolution for breast region segmentation and abnormality detection using mammograms. Int J Numer Method Biomed Eng 34(1):e2907CrossRef
Kassner A, Thornhill RE (2010) Texture analysis: a review of neurologic MR imaging applications. Am J Neuroradiol 31(5):809–816CrossRef
Kooi T, Karssemeijer N (2017) Classifying symmetrical differences and temporal change in mammography using deep neural networks. J Med Imaging (Bellingham) 4(4):044501
Kooi T, van Ginneken B, Karssemeijer N, den Heeten A (2017a) Discriminating solitary cysts from soft tissue lesions in mammography using a pretrained deep convolutional neural network. Med Phys 44(3):1017–1027CrossRef
Kooi T et al (2017b) Large scale deep learning for computer aided detection of mammographic lesions. Med Image Anal 35:303–312CrossRef
Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. In: Proceedings of the 25th international conference on neural information processing systems, vol 1. Curran Associates Inc., pp 1097–1105
Larroza A, Bodí V, Moratal D (2016) Texture analysis in magnetic resonance imaging: review and considerations for future applications. In: Assessment of cellular and organ function and dysfunction using direct and derived mri methodologies. InTech
Lecun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324CrossRef
Lecun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444CrossRef
Li Z et al (2017) Diagnostic performance of mammographic texture analysis in the differential diagnosis of benign and malignant breast tumors. Clin Breast Cancer 18(4):e621–e627CrossRef
Li S et al (2018) Computer-aided assessment of breast density: comparison of supervised deep learning and feature-based statistical learning. Phys Med Biol 63(2):25005MathSciNetCrossRef
Lladó X, Oliver A, Freixenet J, Martí R, Martí J (2009) A textural approach for mass false positive reduction in mammography. Comput Med Imaging Graph 33(6):415–422CrossRef
Malar E, Kandaswamy A, Chakravarthy D, Giri Dharan A (2012) A novel approach for detection and classification of mammographic microcalcifications using wavelet analysis and extreme learning machine. Comput Biol Med 42(9):898–905CrossRef
Mascaro AA, Mello CAB, Santos WP, Cavalcanti GDC (2009) Mammographic images segmentation using texture descriptors. In: Proceedings of the 31st annual international conference of the IEEE engineering in medicine and biology society: engineering the future of biomedicine, EMBC 2009, pp 3653–3656
Materka A (2004) Texture analysis methodologies for magnetic resonance imaging. Dialog Clin Neurosci 6(2):243–250
Mohamed AA, Berg WA, Peng H, Luo Y, Jankowitz RC, Wu S (2018a) A deep learning method for classifying mammographic breast density categories. Med Phys 45(1):314–321CrossRef
Mohamed AA, Luo Y, Peng H, Jankowitz RC, Wu S (2018b) Understanding clinical mammographic breast density assessment: a deep learning perspective. J Digit Imaging 31(4):387–392CrossRef
Moreira IC, Amaral I, Domingues I, Cardoso A, Cardoso MJ, Cardoso JS (2012) INbreast: toward a full-field digital mammographic database. Acad Radiol 19(2):236–248CrossRef
Muramatsu C, Hara T, Endo T, Fujita H (2016) Breast mass classification on mammograms using radial local ternary patterns. Comput Biol Med 72:43–53CrossRef
Ojala T, Pietikäinen M, Mäenpää T (2001) A generalized local binary pattern operator for multiresolution gray scale and rotation invariant texture classification. Springer, Berlin, pp 399–408MATH
Oliver A, Lladó X, Freixenet J, Martí J (2007) False positive reduction in mammographic mass detection using local binary patterns. In: Medical image computing and computer-assisted intervention : MICCAI ... international conference on medical image computing and computer-assisted intervention, vol 10, no. Pt 1, pp 286–93
Purwadi NS, Atay HT, Kurt KK, Turkeli S (2016) Assessment of content-based image retrieval approaches for mammography based on breast density patterns. Stud Health Technol Inf 228:727–731
Rangayyan RM, Nguyen TM, Ayres FJ, Nandi AK (2010) Effect of pixel resolution on texture features of breast masses in mammograms. J Digit Imaging 23(5):547–553CrossRef
Reyad YA, Berbar MA, Hussain M (2014) Comparison of statistical, LBP, and multi-resolution analysis features for breast mass classification. J Med Syst 38(9):100CrossRef
Russakovsky O et al (2014) International journal of computer vision. Kluwer Academic, Boston
Samala RK, Chan H-P, Hadjiiski L, Helvie MA, Wei J, Cha K (2016) Mass detection in digital breast tomosynthesis: deep convolutional neural network with transfer learning from mammography. Med Phys 43(12):6654–6666CrossRef
Samala RK, Chan H-P, Hadjiiski LM, Helvie MA, Cha K, Richter C (2017) Multi-task transfer learning deep convolutional neural network: application to computer-aided diagnosis of breast cancer on mammograms. Phys Med Biol 62(23):8894–8908CrossRef
Sharma S, Khanna P (2014) Computer-aided diagnosis of malignant mammograms using Zernike moments and SVM. J Digit Imaging 28(1):77–90CrossRef
Siegel RL, Miller KD, Jemal A (2016) Cancer statistics, 2016. CA Cancer J Clin 66(1):7–30CrossRef
Skogen K, Ganeshan B, Good C, Critchley G, Miles K (2013) Measurements of heterogeneity in gliomas on computed tomography relationship to tumour grade. J Neurooncol 111(2):213–219CrossRef
Tajbakhsh N et al (2016) Convolutional neural networks for medical image analysis: full training or fine tuning? IEEE Trans Med Imaging 35(5):1299–1312CrossRef
Tan M, Pu J, Zheng B (2014) Reduction of false-positive recalls using a computerized mammographic image feature analysis scheme. Phys Med Biol 59(15):4357–4373CrossRef
Teare P, Fishman M, Benzaquen O, Toledano E, Elnekave E (2017) Malignancy detection on mammography using dual deep convolutional neural networks and genetically discovered false color input enhancement. J Digit Imaging 30(4):499–505CrossRef
Törnberg S et al (2006) Breast cancer incidence and mortality in the Nordic capitals, 1970-1998. Trends related to mammography screening programmes. Acta Oncol 45(5):528–535CrossRef
Tosteson ANA et al (2014) Consequences of false-positive screening mammograms. JAMA Intern Med 174(6):954–961CrossRef
Wang J, Yang X, Cai H, Tan W, Jin C, Li L (2016) Discrimination of breast cancer with microcalcifications on mammography by deep learning. Sci Rep 6:1–9CrossRef
Zacharaki EI et al (2009) Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme. Magn Reson Med 62(6):1609–1618CrossRef
Metadaten
Titel
Advanced Medical Imaging Analytics in Breast Cancer Diagnosis
verfasst von
Yinlin Fu
Bhavika K. Patel
Teresa Wu
Jing Li
Fei Gao
Copyright-Jahr
2020
Buch
Women in Industrial and Systems Engineering

Print ISBN: 978-3-030-11865-5

Electronic ISBN: 978-3-030-11866-2

Copyright-Jahr: 2020

DOI
https://doi.org/10.1007/978-3-030-11866-2_13

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