Multi-resolution graph-based analysis of histopathological whole slide images: Application to mitotic cell extraction and visualization

Tavassoli, 2003

Gillett CE. Assessment of cellular proliferation by calculation of mitotic index and by immunohistochemistry. In: Metastasis research protocols, volume 57 of methods in molecular medicine. Humana Press; 2001. p. 123–31.

Demir C, Yener B. Automated cancer diagnosis based on histopathological images: a systematic survey. Technical Report, Rensselaer Polytechnic Institute; 2005.

Petushi, 2006, Large-scale computations on histology images reveal grade-differentiating parameters for breast cancer, BMC Medical Imaging, 6, 1, 10.1186/1471-2342-6-14

Naik S, Doyle S, Agner S, Madabhushi A, Feldman MD, Tomaszewski J. Automated gland and nuclei segmentation for grading of prostate and breast cancer histopathology. In: International symposium on biomedical imaging; 2008. p. 284–7.

Doyle S, Agner S, Madabhushi A, Feldman MD, Tomaszewski J. Automated grading of breast cancer histopathology using spectral clustering with textural and architectural image features. In: International symposium on biomedical imaging; 2008. p. 496–9.

Ruiz A, Kong J, Ujaldon M, Boyer K, Saltz J, Gurcan M. Pathological image segmentation for neuroblasma using the gpu. In: International symposium on biomedical imaging; 2008. p. 296–9.

Doyle S, Madabhushi A, Feldman M, Tomaszeweski J. A boosting cascade for automated detection of prostate cancer from digitized histology. In: International conference on medical image computing and computer assisted intervention; 2006. p. 504–11.

Fuchs TJ, Wild PJ, Moch H, Buhmann JM. Computational pathology analysis of tissue microarrays predicts survival of renal clear cell carcinoma patients. In: International conference on medical image computing and computer assisted intervention, vol. 2; 2008. p. 1–8.

Signolle N, Plancoulaine B, Herlin P, Revenu M. Texture-based multiscale segmentation: Application to stromal compartment characterization on ovarian carcinoma virtual slides. In: International conference on image and signal processing, vol. LNCS 5099; 2008. p. 173–82.

Elston, 1991, Pathological prognostic factors in breast cancer. I. The value of histological grade in breast cancer: experience from a large study with long-term follow-up, Histopathology, 19, 403, 10.1111/j.1365-2559.1991.tb00229.x

Dalle J, Leow W, Racoceanu D, Tutac A, Putti T. Automatic breast cancer grading of histopathological images. In: International conference of the IEEE engineering in medicine and biology society; 2008. p. 3052–5.

Kong, 2009, Computer-aided evaluation of neuroblastoma on whole-slide histology images: classifying grade of neuroblastic differentiation, Pattern Recognition, 42, 1080, 10.1016/j.patcog.2008.10.035

Elmoataz, 2008, Nonlocal discrete regularization an weighted graphs: a framework for image and manifolds processing, IEEE transactions on Image Processing, 17, 1047, 10.1109/TIP.2008.924284

Ta, 2009, Graph-based tools for microscopic cellular image segmentation, Pattern Recognition, 42, 1113, 10.1016/j.patcog.2008.10.029

Diestel, 2005

Chung, 1997, Spectral graph theory, CBMS Regional Conference Series in Mathematics, 92, 1, 10.1090/cbms/092/01

Chan, 2001, The digital TV filter and nonlinear denoising, IEEE Transactions on Image Processing, 10, 231, 10.1109/83.902288

Wang, 2006, Semi-supervised classification using linear neighborhood propagation, 1, 160

Grady, 2006, Random walks for image segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 28, 1768, 10.1109/TPAMI.2006.233

Sinop AK, Grady L. A seeded image segmentation framework unifying graph cuts and random walker which yields a new algorithm. In: International conference on computer vision; 2007. p. 1–8.

Zhou D, Schölkopf B. Regularization on discrete spaces. In: German association for pattern recognition DAGM, vol. LNCS 3663; 2005. p. 361–8.

Belkin, 2006, Manifold regularization: a geometric framework for learning from labeled and unlabeled examples, Journal of Machine Learning Research, 7, 2399

Roullier V, Ta V-T, Lézoray O, Elmoataz A. Graph-based multi-resolution segmentation of histological whole slide images. In: International symposium on biomedical imaging; 2010. p. 153–6.

Kanungo, 2002, An efficient k-means clustering algorithm: Analysis and implementation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 24, 881, 10.1109/TPAMI.2002.1017616

Elie, 2003, A simple way of quantifying immunostained cell nuclei on the whole histologic section, Cytometry Part A, 56A, 37, 10.1002/cyto.a.10075

Ojala, 1996, A comparative study of texture measures with classification based on feature distributions, Pattern Recognition, 29, 51, 10.1016/0031-3203(95)00067-4

Ojala, 2002, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns, IEEE Transactions on Pattern Analysis and Machine Intelligence, 24, 971, 10.1109/TPAMI.2002.1017623

Belkin, 2003, Laplacian eigenmaps for dimensionality reduction and data representation, Neural Computation, 15, 1373, 10.1162/089976603321780317

Shi, 1997, Normalized cuts and image segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 22, 888

Howard C, Reed M. Unbiased stereology three-dimensional measurement in microscopy, 2nd ed. QTP Publications; 2010.

Herlin P, Lay B, Bouchard H, Pezeril H, Lange H, Plancoulaine B. Benefits of stereology for quality control of fully automated virtual slide analysis. In: 10th European congress on telepathology and 4th international congress on virtual microscopy; 2010.