Recent Trends in Computer Assisted Diagnosis (CAD) System for Breast Cancer Diagnosis Using Histopathological Images

IRBM - Tập 40 - Trang 211-227 - 2019
C. Kaushal1, S. Bhat2, D. Koundal3, A. Singla1
1Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India
2Department of Electronics and Communication, Chitkara University School of Engineering and Technology, Chitkara University, Baddi, India
3Department of Computer Science and Engineering, Chitkara University School of Engineering and Technology, Chitkara University, Baddi, India

Tài liệu tham khảo

Abreu, 2016, Predicting breast cancer recurrence using machine learning techniques, ACM Comput Surv, 49, 1, 10.1145/2988544 Aswathy, 2017, Detection of breast cancer on digital histopathology images: present status and future possibilities, Inform Med Unlocked, 8, 74, 10.1016/j.imu.2016.11.001 Demir, 2005, 1 Robertson, 2018, Digital image analysis in breast pathology-from image processing techniques to artificial intelligence, Transl Res, 2018, 19, 10.1016/j.trsl.2017.10.010 Irshad, 2014, Methods for nuclei detection, segmentation, and classification in digital histopathology: a review-current status and future potential, IEEE Rev Biomed Eng, 7, 97, 10.1109/RBME.2013.2295804 2002 Pal, 1993, A review on image segmentation techniques, Pattern Recognit, 26, 1277, 10.1016/0031-3203(93)90135-J Guyon, 2006, An introduction to feature extraction, 1 Nasrabadi, 2007, Pattern recognition and machine learning, J Electron Imaging, 16 Nahid, 2017, Involvement of machine learning for breast cancer image classification: a survey, Comput Math Models Med, 1 Belsare, 2012, Histopathological image analysis using image processing techniques: an overview, Signal Image Process, 3, 23 Vahadane, 2013, Towards generalized nuclear segmentation in histological images, 1 Hamilton, 1997, Automated location of dysplastic fields in colorectal histology using image texture analysis, J Pathol, 182, 68, 10.1002/(SICI)1096-9896(199705)182:1<68::AID-PATH811>3.0.CO;2-N Khan, 2013, HyMaP: a hybrid magnitude-phase approach to unsupervised segmentation of tumor areas in breast cancer histology images, J Pathol Inform, 4 Khan, 2012, A gamma-gaussian mixture model for detection of mitotic cells in breast cancer histopathology images, 149 Cheng, 2001, Color image segmentation: advances and prospects, Pattern Recognit, 34, 2259, 10.1016/S0031-3203(00)00149-7 Veta, 2011, Marker-controlled watershed segmentation of nuclei in H&E stained breast cancer biopsy images, 618 Kowal, 2011, Computer-aided diagnosis of breast cancer using Gaussian mixture cytology image segmentation, J Med Inform Technol, 17, 257 Ali, 2011, Segmenting multiple overlapping objects via a hybrid active contour model incorporating shape priors: applications to digital pathology, vol. 7962, 1 Mouelhi, 2011, Automatic segmentation of clustered breast cancer cells using watershed and concave vertex graph, 1 Roullier, 2011, Multi-resolution graph-based analysis of histopathological whole slide images: application to mitotic cell extraction and visualization, Comput Med Imaging Graph, 35, 603, 10.1016/j.compmedimag.2011.02.005 Qi, 2012, Robust segmentation of overlapping cells in histopathology specimens using parallel seed detection and repulsive level set, IEEE Trans Biomed Eng, 59, 754, 10.1109/TBME.2011.2179298 Ali, 2011, Active contour for overlap resolution using watershed based initialization (ACOReW): applications to histopathology, 614 Singh, 2012, Cancer cells detection and classification in biopsy image, Int J Eng Sci Technol, 3, 15 Kulikova, 2012, Nuclei extraction from histopathological images using a marked point process approach, vol. 8314, 1 Filipczuk, 2012, Breast fibroadenoma automatic detection using k-means based hybrid segmentation method, 1623 Khan, 2012, RanPEC: random projections with ensemble clustering for segmentation of tumor areas in breast histology images, 17 Prasath, 2012, Segmentation of breast cancer tissue microarrays for computer-aided diagnosis in pathology, 40 Ali, 2012, An integrated region-, boundary-, shape-based active contour for multiple object overlap resolution in histological imagery, IEEE Trans Med Imaging, 31, 1448, 10.1109/TMI.2012.2190089 Xu, 2012, Context-constrained multiple instance learning for histopathology image analysis, 623 Irshad, 2013, Multi-channels statistical and morphological features based mitosis detection in breast cancer histopathology, 6091 Mouelhi, 2013, Hybrid segmentation of breast cancer cell images using a new fuzzy active contour model and an enhanced watershed method, 382 Veta, 2013, Detecting mitotic figures in breast cancer histopathology images, vol. 8676, 1 Tashk, 2013, An automatic mitosis detection method for breast cancer histopathology slide images based on objective and pixel-wise textural features classification, 406 Mouelhi, 2013, Automatic image segmentation of nuclear stained breast tissue sections using color active contour model and an improved watershed method, Biomed Signal Process Control, 8, 421, 10.1016/j.bspc.2013.04.003 Veta, 2013, Automatic nuclei segmentation in H&E stained breast cancer histopathology images, PLoS ONE, 8, 1, 10.1371/journal.pone.0070221 Qu, 2014, Two-step segmentation of Hematoxylin-Eosin stained histopathological images for prognosis of breast cancer, 218 Wang, 2014, Mitosis detection in breast cancer pathology images by combining handcrafted and convolutional neural network features, J Med Imag, 1, 1, 10.1117/1.JMI.1.3.034003 Wang, 2014, Cascaded ensemble of convolutional neural networks and handcrafted features for mitosis detection, International Society for Optics and Photonics, 9041, 1 Raut, 2014, Automatic segmentation of cell nuclei in breast histopathology images and classification using feed forward neural network, Int J Eng Res Appl, 29 Beevi, 2014, Automatic segmentation and classification of mitotic cell nuclei in histopathology images based on active contour model, 740 Qu, 2015, Segmentation of Hematoxylin-Eosin stained breast cancer histopathological images based on pixel-wise SVM classifier, Sci China Inf Sci, 58, 1, 10.1007/s11432-014-5277-3 Su, 2015, Region segmentation in histopathological breast cancer images using deep convolutional neural network, 55 Wang, 2016, Automatic cell nuclei segmentation and classification of breast cancer histopathology images, Signal Process, 122, 1, 10.1016/j.sigpro.2015.11.011 Nguyen, 2015, Automatic glandular and tubule region segmentation in histological grading of breast cancer, vol. 9420, 1 Husham, 2016, Automated nuclei segmentation of malignant using level sets, Microsc Res Tech, 79, 993, 10.1002/jemt.22733 Xu, 2016, A deep convolutional neural network for segmenting and classifying epithelial and stromal regions in histopathological images, Neurocomputing, 191, 214, 10.1016/j.neucom.2016.01.034 Xing, 2016, An automatic learning - based framework for robust nucleus segmentation, IEEE Trans Med Imaging, 35, 550, 10.1109/TMI.2015.2481436 Chen, 2016, Mitosis detection in breast cancer histology images via deep cascaded networks, 1160 Zarella, 2017, A template matching model for nuclear segmentation in digital images of h&e stained slides, 11 Paramanandam, 2016, Automated segmentation of nuclei in breast cancer histopathology images, PLoS ONE, 11, 1, 10.1371/journal.pone.0162053 Nguyen, 2017, Spatial statistics for segmenting histological structures in H&E stained tissue images, IEEE Trans Med Imaging, 36, 1522, 10.1109/TMI.2017.2681519 Naylor, 2017, Nuclei segmentation in histopathology images using deep neural networks, 933 Pan, 2017, Accurate segmentation of nuclei in pathological images via sparse reconstruction and deep convolutional networks, Neurocomputing, 229, 88, 10.1016/j.neucom.2016.08.103 Xu, 2017, Automatic nuclei detection based on generalized laplacian of gaussian filters, IEEE J Biomed Health Inform, 21, 826, 10.1109/JBHI.2016.2544245 Kowal, 2013, Computer-aided diagnosis of breast cancer based on fine needle biopsy microscopic images, Comput Biol Med, 43, 1563, 10.1016/j.compbiomed.2013.08.003 Anzai, 2012 Duda, 2012 Cremers, 2007, A review of statistical approaches to level set segmentation: integrating color, texture, motion and shape, Int J Comput Vis, 72, 195, 10.1007/s11263-006-8711-1 Haykin, 1994 Jain, 1997, Feature selection: evaluation, application, and small sample performance, IEEE Trans Pattern Anal Mach Intell, 19, 153, 10.1109/34.574797 Zheng, 2017, Feature extraction from histopathological images based on nucleus-guided convolutional neural network for breast lesion classification, Pattern Recognit, 71, 14, 10.1016/j.patcog.2017.05.010 Sharma, 2015, A review of graph-based methods for image analysis in digital histopathology, Diagn Pathol, 1, 1 Bloom, 1957, Histological grading and prognosis of breast cancer, Br J Cancer, 11, 359, 10.1038/bjc.1957.43 Zwanenburg Doyle, 2008, Automated grading of breast cancer histopathology using spectral clustering with textural and architectural image features, 496 Chekkoury, 2012, Automated malignancy detection in breast histopathological images, vol. 8315, 1 Roa, 2013, A deep learning architecture for image representation, visual interpretability and automated basal-cell carcinoma cancer detection, vol. 2, 403 Caicedo, 2009, Histopathology image classification using bag of features and kernel functions, 126 Kandemir, 2015, Computer-aided diagnosis from weak supervision: a benchmarking study, Comput Med Imaging Graph, 42, 44, 10.1016/j.compmedimag.2014.11.010 Osareh, 2010, Machine learning techniques to diagnose breast cancer, 114 Sommer, 2012, Learning-based mitotic cell detection in histopathological images, 2306 Niwas, 2012, An expert support system for breast cancer diagnosis using color wavelet features, J Med Syst, 36, 3091, 10.1007/s10916-011-9788-9 Ciresan, 2013, Mitosis detection in breast cancer histology images with deep neural networks, vol. 2, 411 Loukas, 2013, Breast cancer characterization based on image classification of tissue sections visualized under low magnification, Comput Math Methods Med, 1, 10.1155/2013/829461 Basavanhally, 2013, Multi-field-of-view framework for distinguishing tumor grade in ER+ breast cancer from entire histopathology slides, IEEE Trans Biomed Eng, 60, 2089, 10.1109/TBME.2013.2245129 Roa, 2014, Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks, vol. 9041, 3 Kumar, 2015, Detection and classification of cancer from microscopic biopsy images using clinically significant and biologically interpretable features, J Med Eng, 1, 10.1155/2015/457906 Vu, 2015, DFDL: discriminative feature-oriented dictionary learning for histopathological image classification, 990 Spanhol, 2016, A dataset for breast cancer histopathological image classification, IEEE Trans Biomed Eng, 63, 1455, 10.1109/TBME.2015.2496264 Bayramoglu, 2016, Deep learning for magnification independent breast cancer histopathology image classification, 2440 Spanhol, 2016, Breast cancer histopathological image classification using convolutional neural networks, 2560 Carrobles, 2016, Bagging tree classifier and texture features for tumor identification in histological images, Proc Comput Sci, 90, 99, 10.1016/j.procs.2016.07.030 Shi, 2016, Histopathological image classification with color pattern random binary hashing based PCANet and matrix-form classifier, IEEE J Biomed Health Inform, 1 Bejnordi, 2017, Deep learning-based assessment of tumor-associated stroma for diagnosing breast cancer in histopathology images, 1 Song, 2017, Adapting fisher vectors for histopathology image classification, 600 Das, 2017, Classifying histopathology whole-slides using fusion of decisions from deep convolutional network on a collection of random multi-views at multi-magnification, 1024 Wei, 2017, Deep learning model based breast cancer histopathological image classification, 348 Cascianelli, 2017, Dimensionality reduction strategies for CNN-based classification of histopathological images, 21 Zejmo, 2017, Classification of breast cancer cytological specimen using convolutional neural network, J Phys Conf Ser, 783, 1 Cheng, 2010, Automated breast cancer detection and classification using ultrasound images: a survey, Pattern Recognit, 43, 299, 10.1016/j.patcog.2009.05.012 Burges, 1998, A tutorial on support vector machines for pattern recognition, Data Min Knowl Discov, 2, 121, 10.1023/A:1009715923555 Chan, 2015, PCANet: a simple deep learning baseline for image classification, IEEE Trans Image Process, 24, 5017, 10.1109/TIP.2015.2475625 Madabhushi, 2011, Computer-aided prognosis: predicting patient and disease outcome via quantitative fusion of multi-scale, multi-modal data, Comput Med Imaging Graph, 35, 506, 10.1016/j.compmedimag.2011.01.008 Mangasarian, 1995, Breast cancer diagnosis and prognosis via linear programming, Oper Res, 43, 570, 10.1287/opre.43.4.570 Chen, 2003, Learning with progressive transductive support vector machine, Pattern Recognit Lett, 24, 1845, 10.1016/S0167-8655(03)00008-4 Wang, 2016, Semi-supervised learning combining transductive support vector machine with active learning, Neurocomputing, 173, 1288, 10.1016/j.neucom.2015.08.087 Singla, 2014, A novel classification technique based on progressive transductive SVM learning, Pattern Recognit Lett, 42, 101, 10.1016/j.patrec.2014.02.003 Lowe, 2004, Distinctive image features from scale-invariant keypoints, Int J Comput Vis, 60, 91, 10.1023/B:VISI.0000029664.99615.94 Bay, 2008, Speeded-up robust features (SURF), Comput Vis Image Underst, 110, 346, 10.1016/j.cviu.2007.09.014 Rublee, 2011, Orb: an efficient alternative to sift or surf, 2564 Gonzalez, 1992 Singla, 2017, A fast automatic optimal threshold selection technique for image segmentation, Signal Image Video Process, 11, 243, 10.1007/s11760-016-0927-0