Nội dung được dịch bởi AI, chỉ mang tính chất tham khảo
Khảo sát về chẩn đoán ung thư tự động từ hình ảnh bệnh lý mô học
Tóm tắt
Việc phát hiện ung thư ở giai đoạn sớm rất hữu ích cho việc dự đoán và lập kế hoạch điều trị bệnh nhân tốt hơn. Mặc dù có nhiều xét nghiệm ban đầu và các thủ tục không xâm lấn được thực hiện để phát hiện ung thư ở các cơ quan khác nhau, nhưng nghiên cứu bệnh lý mô học là điều không thể thiếu và được coi là tiêu chuẩn vàng trong chẩn đoán ung thư. Ngày nay, khi chi phí của các linh kiện điện tử giảm mạnh, máy tính với dung lượng bộ nhớ lớn và khả năng xử lý tốt hơn được xây dựng. Hơn nữa, các phương thức hình ảnh cũng đã được phát triển ở mức độ cao. Thú vị thay, máy tính giúp các bác sĩ trong việc giải thích hình ảnh y tế trong quá trình chẩn đoán, từ đó lĩnh vực Chẩn đoán Hỗ trợ Máy tính (CAD) đã ra đời. Do đó, quy trình chẩn đoán trở nên có thể lặp lại, đáng tin cậy và ít bị ảnh hưởng bởi biến đổi của người quan sát. Khảo sát này khám phá các vật liệu và phương pháp tiên tiến đã được sử dụng cho CAD để phát hiện ung thư từ hình ảnh bệnh lý mô học.
Từ khóa
#chẩn đoán ung thư #bệnh lý mô học #Chẩn đoán Hỗ trợ Máy tính (CAD) #hình ảnh y tế #kỹ thuật sốTài liệu tham khảo
(2014) ICPR 22nd international conference on pattern recognition. http://mitos-atypia-14.grand-challenge.org/home/
(2015) GlaS@MICCAI’2015: Gland segmentation challenge contest. http://www2.warwick.ac.uk/fac/sci/dcs/research/combi/research/bic/glascontest
Al-Kadi OS (2009) A fractal dimension based optimal wavelet packet analysis technique for classification of meningioma brain tumours. In: 16th IEEE international conference on image processing. IEEE, Cairo, Egypt, pp 4177–4180
Al-Kofahi Y, Lassoued W, Lee W, Roysam B (2010) Improved automatic detection and segmentation of cell nuclei in histopathology images. IEEE Trans Biomed Eng 57(4):841–852
Ali S, Madabhushi A (2011) Active contour for overlap resolution using watershed based initialization (acorew): applications to histopathology. In: 8th IEEE international symposium on biomedical imaging: from nano to macro. IEEE, Chicago, U.S.A., pp 614–617
Ali S, Madabhushi A (2012) An integrated region-, boundary-, shape-based active contour for multiple object overlap resolution in histological imagery. IEEE Trans Med Imaging 31(7):1448–1460
Ali S, Veltri R, Epstein JI, Christudass C, Madabhushi A (2015) Selective invocation of shape priors for deformable segmentation and morphologic classification of prostate cancer tissue microarrays. Comput Med Imaging Gr 41:3–13 machine Learning in Medical Imaging
Arif M, Rajpoot N (2007a) Classification of potential nuclei in prostate histology images using shape manifold learning. In: International conference on machine vision, pp 113–118
Arif M, Rajpoot N (2007b) Detection of nuclei by unsupervised manifold learning. In: 11th Medical image understanding and analysis, Aberystwyth, Wales
Arivazhagan S, Ganesan L (2003) Texture segmentation using wavelet transform. Pattern Recognit Lett 24(16):3197–3203
Babu MN, Madasu VK, Hanmandlu M, Vasikarla S (2010) Histo-pathological image analysis using os-fcm and level sets. In: 39th IEEE applied imagery pattern recognition workshop, pp 1–8
Baish JW, Jain RK (2000) Fractals and cancer. Cancer Res 60(14):3683–3688
Baker SG (2003) The central role of receiver operating characteristic (roc) curves in evaluating tests for the early detection of cancer. J Natl Cancer Inst 95(7):511–515
Basavanhally A, Xu J, Madabhushi A, Ganesan S (2009) Computer-aided prognosis of er+ breast cancer histopathology and correlating survival outcome with oncotype dx assay. In: IEEE international symposium on biomedical imaging: from nano to macro, pp 851–854
Basavanhally AN, Ganesan S, Agner S, Monaco JP, Feldman MD, Tomaszewski JE, Bhanot G, Madabhushi A (2010) Computerized image-based detection and grading of lymphocytic infiltration in her2+ breast cancer histopathology. IEEE Trans Biomed Eng 57(3):642–653
Beevi S, Nair MS, R BG (2014) Automatic segmentation and classification of mitotic cell nuclei in histopathology images based on active contour model. In: International conference on contemporary computing and informatics, pp 740–744
Belhomme P, Toralba S, Plancoulaine B, Oger M, Gurcan MN, Gurcan MN, Bor-Angelier C (2015) Heterogeneity assessment of histological tissue sections in whole slide images. Comput Med Imaging Gr 42:51–55 Breakthrough Technologies In Digital Pathology
Belkacem-Boussaid K, Sertel O, Lozanski G, Shana’aah A, Gurcan M (2009) Extraction of color features in the spectral domain to recognize centroblasts in histopathology. In: Annual international conference of the IEEE engineering in medicine and biology society, pp 3685–3688
Bengio Y, Courville A, Vincent P (2013) Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell 35(8):1798–1828. doi:10.1109/TPAMI.2013.50
Bilgin C, Demir C, Nagi C, Yener B (2007) Cell-graph mining for breast tissue modeling and classification. In: 29th annual international conference of the IEEE, pp 5311–5314
Bottou L, Lin CJ (2007) Support vector machine solvers. Large Scale Kernel Machines. MIT Press, Cambridge
Boucheron LE, Manjunath BS, Harvey NR (2010) Use of imperfectly segmented nuclei in the classification of histopathology images of breast cancer. In: IEEE International conference on acoustics speech and signal processing, pp 666–669
Brown HS (2002) Hematoxylin & eosin (The routine stain). Sigma-Aldrich Corporation, St. Louis
Caselles V, Kimmel R, Sapiro G (1997) Geodesic active contours. Int J Comput Vis 22(1):61–79
Chang T, Kuo CCJ (1993) Texture analysis and classification with tree-structured wavelet transform. IEEE Trans Image Process 2(4):429–441
Cheikh BB, Bertheau P, Racoceanu D (2015) Preliminary approach for crypt detection in inflammatory bowel disease. In: RITS, pp 138–139
Chekkoury A, Khurd P, Ni J, Bahlmann C, Kamen A, Patel A, Grady L, Singh M, Groher M, Navab N, Krupinski E, Johnson J, Graham A, Weinstein R (2012) Automated malignancy detection in breast histopathological images. In: Medical Imaging 2012: Computer-Aided Diagnosis, San Diego, California, vol 8315:831505
Chen C, Ozolek JA, Wang W, Rohde GK (2011) A pixel classification system for segmenting biomedical images using intensity neighborhoods and dimension reduction. In: IEEE International symposium on biomedical imaging: from nano to macro, pp 1649–1652
Chen C, Wang W, Ozolek JA, Rohde GK (2013) A flexible and robust approach for segmenting cell nuclei from 2d microscopy images using supervised learning and template matching. J Int Soc Adv Cytom A 83(5):495–507
Cheng Y (1995) Mean shift, mode seeking, and clustering. IEEE Trans Pattern Anal Mach Intell 7(8):790–799
Ciresan D, Giusti A, Gambardella L (2013) Mitosis detection in breast cancer histology images with deep neural networks. In: Mori K, Sakuma I, Sato Y, Barillot C, Navab N (eds) Medical image computing and computer-assisted intervention MICCAI., Lecture notes in computer scienceSpringer, Berlin, pp 411–418
Comaniciu D, Meer P (2002) Mean shift: a robust approach toward feature space analysis. IEEE Trans Pattern Anal Mach Intell 24(5):603–619
Contest I (2012) Mitosis detection from breast cancer histological images, icpr 2012 contest. http://ipal.cnrs.fr/ICPR2012/?q=node/5
Cruz-Roa A, Arévalo J, Madabhushi A, González F (2013) A deep learning architecture for image representation, visual interpretability and automated basal-cell carcinoma cancer detection. In: Mori K, Sakuma I, Sato Y, Barillot C, Navab N (eds) Medical Image computing and computer-assisted intervention MICCAI 2013, vol 8150., Lecture notes in computer scienceSpringer, Berlin Heidelberg, pp 403–410
Cruz-Roa A, Arévalo J, Basavanhally A, Madabhushi A, González F (2015) A comparative evaluation of supervised and unsupervised representation learning approaches for anaplastic medulloblastoma differentiation. In: SPIE Medical Imaging, vol 9287. p 92870G
Dai SK, Wu YC, Jan YJ, Lin SC (2009) The histological grading of hcc using fusion images. In: IEEE international conference on computational intelligence for measurement systems and applications, pp 186–189
Dalle JR, Leow WK, Racoceanu D, Tutac AE, Putti TC (2008) Automatic breast cancer grading of histopathological images. In: 30th annual international IEEE EMBS conference Vancouver. British Columbia, Canada, pp 3052–3055
Daskalakis A, Kostopoulos S, Spyridonos P, Glotsos D, Ravazoula P, Kardari M, Kalatzis I, Cavouras D, Nikiforidis G (2008) Design of a multi-classifier system for discriminating benign from malignant thyroid nodules using routinely h&e-stained cytological images. Comput Biol Med 38(2):196–203
Datar M, Padfield D, Cline H (2008) Color and texture based segmentation of molecular pathology images using hsoms. In: 5th IEEE international symposium on biomedical imaging: from nano to macro, pp 292–295
Demir C, Yener B (2005) Automated cancer diagnosis based on histopathological images: a systematic survey. Technical report, Department of Computer Science, Rensselaer Polytechnic Institute, USA
Demir C, Gultekin SH, Yener B (2005) Learning the topological properties of brain tumors. IEEE/ACM Trans Comput Biol Bioinform 2(4):262–270
Diamond J, Anderson NH, Bartels PH, Montironi R, Hamilton PW (2004) The use of morphological characteristics and texture analysis in the identification of tissue composition in prostatic neoplasia. Human Pathol 35(9):1121–1131
Dong F, Irshad H, Oh EY, Lerwill M, Brachtel E, Jones NC, Knoblauch NW, MontaserKouhsari L, Johnson NB, Rao LKF, Faulkner-Jones B, Wilbur DC, Schnitt SJ, Beck AH (2014) Computational pathology to discriminate benign from malignant intraductal proliferations of the breast. PLoS One 9(12):e114885. doi:10.1371/journal.pone.0114885
Doyle S, Madabhushi A, Feldman MD, Tomaszeweski JE (2006) A boosting cascade for automated detection of prostate cancer from digitized histology. In: 9th International conference on medical image computing and computer-assisted intervention - MICCAI 2006, Copenhagen, Denmark, October 1–6, 2006, proceedings, Part II, Springer, Lecture Notes in Computer Science, vol 4191, pp 504–511
Dundar MM, Badve S, Raykar VC, Jain RK, Sertel O, Gurcan MN (2010) A multiple instance learning approach toward optimal classification of pathology slides. In: 20th International conference on pattern recognition, pp 2732–2735
Dundar MM, Badve S, Bilgin G, Raykar V, Jain R, Sertel O, Gurcan MN (2011) Computerized classification of intraductal breast lesions using histopathological images. IEEE Trans Biomed Eng 58(7):1977–1984
Esgiar AN, Chakravorty PK (2007) Fractal based classification of colon cancer tissue images. In: 9th International symposium on signal processing and its applications, pp 1–4
Fatakdawala H, Xu J, Basavanhally A, Bhanot G, Ganesan S, Feldman M, Tomaszewski JE, Madabhushi A (2010) Expectation-maximization-driven geodesic active contour with overlap resolution (emagacor): application to lymphocyte segmentation on breast cancer histopathology. IEEE Trans Biomed Eng 57(7):1676–1689
Fernández-Carrobles MM, Bueno G, Déniz O, Salido J, García-Rojo M, Gonzández-López L (2015) Frequential versus spatial colour textons for breast TMA classification. Comput Med Imaging Gr 42:25–37 Breakthrough Technologies In Digital Pathology
Fuchs TJ, Buhmann JM (2011) Computational pathology:challenges and promises for tissue analysis. Comput Med Imaging Gr 35:515–530
Fujita H, Uchiyama Y, Nakagawa T, Fukuoka D, Hatanaka Y, Hara T, Lee GN, Hayashi Y, Ikedo Y, Gao X, Zhou X (2008) Computer-aided diagnosis: the emerging of three cad systems induced by japanese health care needs. Comput Methods Progr Biomed 92(3):238–248
Gelasca ED, Obara B, Fedorov DG, Kvilekval K, Manjunath BS (2009) A biosegmentation benchmark for evaluation of bioimage analysis methods. Bioinformatics 10:1–12
George LE, Sager KH (2007) Breast cancer diagnosis using multi-fractal dimension spectra. In: IEEE international conference on signal processing and communications, pp 592–595
Glotsos D, Kalatzis I, Spyridonos P, Kostopoulos S, Daskalakis A, Athanasiadis E, Ravazoula P, Nikiforidis G, Cavouras D (2008) Improving accuracy in astrocytomas grading by integrating a robust least squares mapping driven support vector machine classifier into a two level grade classification scheme. Comput Methods Progr Biomed 90(3):251–261
GLoukas C, Linney A (2004) A survey on histological image analysis-based assessment of three major biological factors influencing radiotherapy: proliferation, hypoxia and vasculature. Comput Methods Progr Biomed 74(3):183–199
Gonzalez RC, Woods RE (2008) Digital image processing. Pearson Education, Upper Saddle River
Gopinath B, Gupta BR (2010) Majority voting based classification of thyroid carcinoma. Procedia Comput Sci 2:265–271
Grady L (2005) Multilabel random walker image segmentation using prior models. In: IEEE computer society conference on computer vision and pattern recognition, pp 763–770
Grady L (2006) Random walks for image segmentation. IEEE Trans Pattern Anal Mach Intell 28(11):1768–1783
Gurcan M, Boucheron L, Can A, Madabhushi A, Rajpoot N, Yener B (2009) Histopathological image analysis: a review. IEEE Rev Biomed Eng 2:147–171
Han J, Kamber M (2006) Data mining concepts and techniques. Elsevier, Amsterdam
Han J, Chang H, Loss L, Zhang K, Baehner FL, Gray JW, Spellman P, Parvin B (2011) Comparison of sparse coding and kernel methods for histopathological classification of gliobastoma multiforme. In: IEEE international symposium on biomedical imaging: from nano to macro, pp 711–714
Haralick RM, Shanmugam K, Dinstein I (1973) Textural features for image classification. IEEE Trans Syst Man Cybern SMC 3(6):610–621
He L, Long LR, Antani S, Thoma GR (2012) Histology image analysis for carcinoma detection and grading. Comput Methods Progr Biomed 107(3):538–556
Hipp J, Flotte T, Monaco J, Cheng J, Madabhushi A, Yagi Y, Rodriguez-Canales J, Emmert-Buck M, Dugan MC, Hewitt S, Toner M, Tompkins RG, Lucas D, Gilbertson JR, Balis UJ (2011) Computer aided diagnostic tools aim to empower rather than replace pathologists: lessons learned from computational chess. J Pathol Inform 2(1):25
Horstmeyer R, Ou X, Zheng G, Willems P, Yang C (2015) Digital pathology with fourier ptychography. Comput Med Imaging Gr 42:38–43 Breakthrough Technologies In Digital Pathology
Irshad H, Jalali S, Roux L, Racoceanu D, Hwee LJ, Naour GL, Capron F (2013a) Automated mitosis detection using texture, sift features and hmax biologically inspired approach. J Pathol Inform 4(2):12
Irshad H, Roux L, Racoceanu D (2013b) Multi-channels statistical and morphological features based mitosis detection in breast cancer histopathology. In: 35th Annual international conference of the IEEE EMBS, pp 6091–6094
Irshad H, Guaillard A, Roux L, Racoceanu D (2014a) Multispectral band selection and spatial characterization: application to mitosis detection in breast cancer histopathology. Comput Med Imaging Gr 38(5):390–402
Irshad H, Veillard A, Roux L, Racoceanu D (2014b) Methods for nuclei detection, segmentation, and classification in digital histopathology: a review - current status and future potential. IEEE Rev Biomed Eng 7:97–114
Jadhav AS, Banerjee S, Dutta PK, Paul RR, Pal M, Banerjee P, Chaudhuri K, Chatterjee J (2006) Quantitative analysis of histopathological features of precancerous lesion and condition using image processing technique. In: 19th IEEE International symposium on computer-based medical systems, pp 231–236
Jafari-Khouzani K, Soltanian-Zadeh H (2003) Multiwavelet grading of pathological images of prostate. IEEE Trans Biomed Eng 50(6):697–704
Jain AK, Zongker D (1997) Feature selection: evaluation, application, and small sample performance. IEEE Trans Pattern Anal Mach Intell 19(2):153–158
Janowczyk A, Chandran S, Singh R, Sasaroli D, Coukos G, Feldman MD, Madabhushi A (2009) Hierarchical normalized cuts: Unsupervised segmentation of vascular biomarkers from ovarian cancer tissue microarrays. In: MICCAI-2009, Springer, Lecture Notes in Computer Science, vol 5761, pp 230–238
Jothi AA, Rajam MA (2014) Segmentation of nuclei from breast histopathology images using pso-based otsus multilevel thresholding. In: L Padma Suresh BKP Subhransu Sekhar Dash (ed) Artificial Intelligence and Evolutionary Algorithms in Engineering Systems, Springer, Advances in Intelligent and Soft Computing, vol 325, pp 835–843
Kandemir M, Hamprecht FA (2015) Computer-aided diagnosis from weak supervision: a benchmarking study. Comput Med Imaging Gr 42:44–50 Breakthrough Technologies In Digital Pathology
Kass M, Witkin A, Terzopoulos D (1988) Snakes : active contour models. Int J Comput Vis 1(4):321–331
Khan AM, El-Daly H, Rajpoot NM (2012) A gamma-gaussian mixture model for detection of mitotic cells in breast cancer histopathology images. In: 21st International conference on pattern recognition, pp 149–152
Khan AM, Rajpoot N, Treanor D, Magee D (2014) Non-linear mapping approach to stain normalisation in digital histopathology images using image-specific colour deconvolutionieee transactions on biomedical engineering. IEEE Trans Biomed Eng 61(6):1729–1738
Khan AM, Sirinukunwattana K, Rajpoot N (2015) A global covariance descriptor for nuclear atypia scoring in breast histopathology images. IEEE J Biomed Health Inform 19:1637–1647
Khurd P, Bahlmann C, Maday P, Kamen A, Gibbs-Strauss S, Genega EM, Frangioni JV (2010) Computer-aided gleason grading of prostate cancer histopathological images using texton forests. In: IEEE international symposium on biomedical imaging: from nano to macro, pp 636–639
Khurd P, Grady L, Kamen A, Gibbs-Strauss S, Genega EM, Frangioni JV (2011) Network cycle features: application to computer-aided gleason grading of prostate cancer histopathological images. In: IEEE international symposium on biomedical imaging: from nano to macro, pp 1632–1636
Kong H, Gurcan M, Belkacem-Boussaid K (2011a) Partitioning histopathological images: an integrated framework for supervised color-texture segmentation and cell splitting. IEEE Trans Med Imaging 30(9):1661–1677
Kong H, Gurcan M, Belkacem-Boussaid K (2011b) Splitting touching-cell clusters on histopathological images. In: IEEE international symposium on biomedical imaging: from nano to macro, pp 208–211
Kothari S, Phan JH, Moffitt RA, Stokes TH, Hassberger SE, Chaudry Q, Young AN, Wang MD (2011) Automatic batch-invariant color segmentation of histological cancer images. In: IEEE International symposium on biomedical imaging: from nano to macro, IEEE, pp 657–660
Li G, Sanchez V, Patel G, Quenby S, Rajpoot N (2015) Localisation of luminal epithelium edge in digital histopathology images of IHC stained slides of endometrial biopsies. Comput Med Imaging Gr 42:56–64 Breakthrough Technologies In Digital Pathology
Li X, Plataniotis KN (2015) A complete color normalization approach to histopathology images using color cues computed from saturation-weighted statistics. IEEE Trans Biomed Eng 62(7):1862–1873
Loménie N, Racoceanu D (2012) Point set morphological filtering and semantic spatial configuration modeling: application to microscopic image and bio-structure analysis. Pattern Recognit 45(8):2894–2911
Lu C, Mahmood M, Jha N, Mandal M (2012) A robust automatic nuclei segmentation technique for quantitative histopathological image analysis. Anal Quant Cytol Histol 34(6):296–308
Luts J, Ojeda F, de Plas RV, Moor BD, Suykens SVHJA (2010) A tutorial on support vector machine-based methods for classification problems in chemometrics. Anal Chimica Acta 665(2):129–145
Madabhushi A, Basavanhally A, Doyle S, Agner S, Lee G (2010) Computer-aided prognosis: Predicting patient and disease outcome via multi-modal image analysis. In: IEEE International symposium on biomedical imaging: from nano to macro, pp 1415–1418
Masood K, Rajpoot N (2009) Texture based classification of hyperspectral colon biopsy samples using clbp. In: IEEE international symposium on biomedical imaging: from nano to macro, pp 1011–1014
Masood K, Rajpoot N, Qureshi H, Rajpoot K (2006) Co-occurrence and morphological analysis for colon tissue biopsy classification. In: 4th international workshop on frontiers of information technology
Mete M, Xu X, Fan CY, Shafirstein G (2006) Head and neck cancer detection in histopathological slides. In: 6th IEEE international conference on data mining—Workshops
MICCAI (2013) Assessment of mitosis detection algorithms 2013 (AMID13), MICCAI grand challenge. http://amida13.isi.uu.nl/
Naik S, Doyle S, Agner S, Madabhushi A, Feldman M, Tomaszewski J (2008) Automated gland and nuclei segmentation for grading of prostate and breast cancer histopathology. In: 5th IEEE international symposium on biomedical imaging: from nano to macro, pp 284–287
Nandy K, Gudla PR, Amundsen R, Meaburn KJ, Misteli T, Lockett SJ (2012) Automatic segmentation and supervised learning-based selection of nuclei in cancer tissue images. J Int Soc Adv Cytom A 81(9):743–754
Nateghi R, Danyali H, SadeghHelfroush M, Pour F (2014) Automatic detection of mitosis cell in breast cancer histopathology images using genetic algorithm. In: 21th Iranian conference on biomedical engineering, pp 1–6
Nayak N, Chang H, Borowsky A, Spellman P, Parvin B (2013) Classification of tumor histopathology via sparse feature learning. In: 10th International symposium on biomedical imaging, pp 410–413
Nguyen K, Jain AK, Allen RL (2010) Automated gland segmentation and classification for gleason grading of prostate tissue images. In: International conference on pattern recognition, pp 1497–1500
Nguyen K, Sabata B, Jain AK (2012) Prostate cancer grading: gland segmentation and structural features. Pattern Recognit Lett 33(7):951–961
Pang B, Zhang Y, Chen Q, Gao Z, Peng Q, You X (2010) Cell nucleus segmentation in color histopathological imagery using convolutional networks. In: Chinese conference on pattern recognition, pp 1–5
Park SY, Sargent D, Lieberman R, Gustafsson U (2011) Domain-specific image analysis for cervical neoplasia detection based on conditional random fields. IEEE Trans Med Imaging 30(3):867–878
Peng H (2008) Bioimage informatics: a new area of engineering biology. Bioinformatics 24(17):1827–1836
Perkins S, Lacker K, Theiler J (2003) Grafting: fast, incremental feature selection by gradient descent in function space. J Mach Learn Res 3:1333–1356
Petushi S, Katsinis C, Coward C, Garcia F, Tozeren A (2004) Automated identification of microstructures on histology slides. IEEE International symposium on biomedical imaging: from nano to macro 1:424–427
Petushi S, Garcia FU, Haber MM, Katsinis C, Tozeren A (2006) Large-scale computations on histology images reveal grade-differentiating parameters for breast cancer. BMC Med Imaging 6:14
Po-Whei H, Cheng-Hsiung L (2009) Automatic classification for pathological prostate images based on fractal analysis. IEEE Trans Med Imaging 28(7):1037–1050
Po-Whei H, Yan-Hao L (2010) Effective segmentation and classification for hcc biopsy images. Pattern Recognit 43(4):1550–1563
Pudil P, Novovicova J, Kittler J (1994) Floating search methods in feature selection. Pattern Recognit Lett 15:1119–1125
Racoceanu D, Capron F (2015) Towards semantic-driven high-content image analysis: an operational instantiation for mitosis detection in digital histopathology. Comput Med Imaging Gr 42:2–15
Rahmadwati, Naghdy G, Ross M, Todd C, Norachmawati E (2010) Classification cervical cancer using histology images. In: 2nd international conference on computer engineering and applications, vol 1, pp 515–519
Reinhard E, Adhikhmin M, Gooch B, Shirley P (2001) Color transfer between images. IEEE Comput Gr Appl 21(5):34–41
Roula MA, Bouridane A, Kurugollu F (2004) An evolutionary snake algorithm for the segmentation of nuclei in histopathological images. Int Conf Image Process 1:127–130
Roullier V, Ta VT, Léoray O, Elmoataz A (2010) Graph-based multi-resolution segmentation of histological whole slide images. In: IEEE international symposium on biomedical imaging: from nano to macro, pp 153–156
Roux L, Racoceanu D, Loménie N, Kulikova M, Irshad H, Klossa J, Capron F, Genestie C, Metin N, Gurcan GLN (2013) Mitosis detection in breast cancer histological images: an icpr 2012 contest. J Pathol Inform 4:8
Ruiz A, Kong J, Ujaldon M, Boyer K, Saltz J, Gurcan M (2008) Pathological image segmentation for neuroblastoma using the gpu. In: 5th IEEE international symposium on biomedical imaging: from nano to macro, pp 296–299
Seminowich S, Sar A, Yilmaz S, Rangayyan RM (2009) Segmentation of the effective area of images of renal biopsy samples. In: Canadian conference on electrical and computer engineering, pp 108–111
Seminowich S, Sar A, Yilmaz S, Rangayyan RM (2010) Segmentation of cell nuclei in images of renal biopsy samples. In: 23rd Canadian conference on electrical and computer engineering, pp 1–4
Sertel O, Kong J, Lozanski G, Shana’ah A, Catalyurek UV, Saltz JH, Gurcan MN (2008) Texture classification using nonlinear color quantization: application to histopathological image analysis. In: IEEE International conference on acoustics, speech and signal processing, pp 597–600
Sertel O, Catalyurek UV, Shimada H, Gurcan MN (2009a) Computer-aided prognosis of neuroblastoma: Detection of mitosis and karyorrhexis cells in digitized histological images. In: Annual international conference of the IEEE engineering in medicine and biology society, pp 1433–1436
Sertel O, Kong J, Catalyurek UV, Gerard L, Saltz JH, Gurcan MN (2009b) Histopathological image analysis using model-based intermediate representations and color texture: Follicular lymphoma grading. J Signal Process Syst 55(1–3):169–183
Sertel O, Catalyurek UV, Lozanski G, Shanaah A, Gurcan MN (2010a) An image analysis approach for detecting malignant cells in digitized h&e-stained histology images of follicular lymphoma. In: 20th International conference on pattern recognition, pp 273–276
Sertel O, Lozanski G, Shana’ah A, Gurcan MN (2010b) Computer-aided detection of centroblasts for follicular lymphoma grading using adaptive likelihood-based cell segmentation. IEEE Trans Biomed Eng 57(10):2613–2616
Sezgin M, Sankur B (2004) Survey over image thresholding techniques and quantitative performance evaluation. J Electron Imaging 13(1):146–165
Shi J, Malik J (2000) Normalized cuts and image segmentation. IEEE Trans Pattern Anal Mach Intell 22(8):888–905
Sieren JC, Weydert J, Bell A, Young BD, Smith AR, Thiesse J, Namati E, McLennan G (2010) An automated segmentation approach for highlighting the histological complexity of human lung cancer. Annal Biomed Eng 38(12):3581–3591
Sirinukunwattana K, Khan AM, Rajpoot NM (2015) Cell words: modelling the visual appearance of cells in histopathology images. Comput Med Imaging Gr 42:16–24 Breakthrough Technologies In Digital Pathology
Somol P, Pudil P, Novovicova J, Paclik P (1999) Adaptive foating search methods in feature selection. Pattern Recognit Lett 20(11–13):1157–1163
Song Y, Zhang L, Chen S, Ni D, Li B, Zhou Y, Lei B, Wang T (2014) A deep learning based framework for accurate segmentation of cervical cytoplasm and nuclei. In: Engineering in Medicine and Biology Society (EMBC), 2014 36th annual international conference of the IEEE, pp 2903–2906
Song Y, Zhang L, Chen S, Ni D, Lei B, Wang T (2015) Accurate segmentation of cervical cytoplasm and nuclei based on multi-scale convolutional network and graph partitioning. IEEE transactions on biomedical engineering PP(99):1–1
Spyridonos P, Glotsos D, Cavouras D, Ravazoula P, Zolota V, Nikiforidis G (2002) Pattern recognition based segmentation method of cell nuclei in tissue section analysis. In: 14th International conference on digital signal processing, pp 1121–1124
Sridhar S (2011) Digital image processing. Oxford University Press, Oxford
Tabesh A, Teverovskiy M, Pang HY, Kumar VP, Verbel D, Kotsianti A, Saidi O (2007) Multifeature prostate cancer diagnosis and gleason grading of histological images. IEEE Trans Med Imaging 26(10):1366–1378
Tashk A, Helfroush MS, Danyali H, Akbarzadeh-Jahromi M (2013) An automatic mitosis detection method for breast cancer histopathology slide images based on objective and pixel-wise textural features classification. In: 5th Conference on information and knowledge technology, pp 406–410
Tashk A, Helfroush MS, Danyali H, Akbarzadeh-Jahromi M (2014) A cad mitosis detection system from breast cancer histology images based on fused features. In: 22nd Iranian conference on electrical engineering, pp 1924–1927
Tashk A, Helfroush MS, Danyali H, Akbarzadeh-jahromi M (2015) Automatic detection of breast cancer mitotic cells based on the combination of textural, statistical and innovative mathematical features. Appl Math Model 39(20):6165–6182
Tchikindas L, Sparks R, Baccon J, Ellison D, Judkins AR, Madabhushi A (2011) Segmentation of nodular medulloblastoma using random walker and hierarchical normalized cuts. In: IEEE 37th annual northeast bioengineering conference, pp 1–2
Tomasi C (2004) Estimating gaussian mixture densities with em - a tutorial. http://www.cse.psu.edu/~rtc12/CSE586/papers/emTomasiTutorial.pdf
Tosun AB, Gunduz-Demir C (2011) Graph run-length matrices for histopathological image segmentation. IEEE Trans Med Imaging 30(3):721–732
Tosun AB, Kandemir M, Sokmensuer C, Gunduz-Demir C (2009) Object-oriented texture analysis for the unsupervised segmentation of biopsy images for cancer detection. Pattern Recognit 42(6):1104–1112
Veillard A, Bressan S, Racoceanu D (2012) SVM-based framework for the robust extraction of objects from histopathological images using color, texture, scale and geometry. In: 11th International conference on machine learning and applications, pp 70–75
Veta M, van Diest PJ, Kornegoor R, Huisman A, Viergever MA, Pluim JPW (2013) Automatic nuclei segmentation in h&e stained breast cancer histopathology images. PLoS One 8(7):e70,221. doi:10.1371/journal.pone.0070221
Veta M, Pluim JPW, van Diest PJ, Viergever MA (2014) Breast cancer histopathology image analysis: a review. IEEE Trans Biomed Eng 61(5):1400–1411
Veta M, van Diesta PJ, Willems SM, Wang H, Madabhushi A, Cruz-Roa A, Gonzalez F, Larsen AB, Vestergaarda JS, Dahl AB, Ciresan DC, Schmidhuber J, Giusti A, Gambardella LM, Tek FB, Walter T, Wang CW, Kondo S, Matuszewski BJ, Precioso F, Snell V, Kittler J, de Campos TE, Khan AM, Rajpoot NM, Arkoumani E, Lacle MM, Viergever MA, Pluim JP (2015) Assessment of algorithms for mitosis detection in breast cancer histopathology images. Med Image Anal 20(1):237–248
Waheed S, Moffitt RA, Chaudryl Q, Young AN, Wang MD (2007) Computer aided histopathological classification of cancer subtypes. In: 7th IEEE International conference on bioinformatics and bioengineering, pp 503–508
Wan T, Liu X, Chen J, Qin Z (2014) Wavelet-based statistical features for distinguishing mitotic and non-mitotic cells in breast cancer histopathology. In: 2014 IEEE International conference on image processing (ICIP), pp 2290–2294
Wang W, Ozolek JA, Rohde GK (2010) Detection and classification of thyroid follicular lesions based on nuclear structure from histopathology images. J Int Soc Adv Cytom A 77A(5):485–494
Wang W, Ozolek JA, Slepcev D, Lee AB, Chen C, Rohde GK (2011) An optimal transportation approach for nuclear structure-based pathology. IEEE Trans Med Imaging 30(3):621–631
Wang Y, Crookes D, Eldin OS, Wang S, Hamilton P, Diamond J (2009) Assisted diagnosis of cervical intraepithelial neoplasia (cin). IEEE J Sel Top Signal Process 3(1):112–121
Westin LK (2001) Receiver operating characteristic (roc) analysis. evaluating discriminance effects among descision support systems. Technical report, Department of Computing Science, Umea University, Sweden
Wittke C, Mayer J, Schweiggert F (2007) On the classification of prostate carcinoma with methods from spatial statistics. IEEE Trans Inform Technol Biomed 11(4):406–414
Xu C, Prince JL (1998) Snakes, shapes, and gradient vector flow. IEEE Trans Image Process 7(3):359–369
Xu J, Sparks R, Janowcyzk A, Tomaszewski JE, Feldman MD, Madabhushi1 A (2010) High-throughput prostate cancer gland detection, segmentation, and classification from digitized needle core biopsies. In: Prostate cancer imaging, Springer, Lecture Notes in Computer Science, vol 6367, pp 77–88
Xu Y, Mo T, Feng Q, Zhong P, Lai M, Chang EIC (2014) Deep learning of feature representation with multiple instance learning for medical image analysis. In: IEEE international conference on acoustics, speech and signal processing, pp 1626–1630
Yang L, Tuzel O, Meer P, Foran DJ (2008) Automatic image analysis of histopathology specimens using concave vertex graph. In: Medical Image Computing and Computer-Assisted Intervention, Springer, Lecture Notes in Computer Science, vol 5241, pp 833–841
Zampirolli FDA, Stransky B, Lorena AC, de Melo Paulon FL (2010) Segmentation and classification of histological images - application of graph analysis and machine learning methods. In: 23rd SIBGRAPI conference on graphics, patterns and images, pp 331–338
Zhang M, Wu T, Bennett KM (2015) Small blob identification in medical images using regional features from optimum scale. IEEE Trans Biomed Eng 62(4):1051–1062
Zhou ZH (2004) Multi-instance learning: a survey. Technical report, AI Lab, Department of Computer Science & Technology,Nanjing University, Nanjing, China
