References

    1. Randell DA, Landini G. Discrete mereotopology in automated histological image analysis. Proceedings of the Second ImageJ user and developer Conference, Luxembourg, 6-7 November, 2008, p 151-156. ISBN 2-919941-06-2
    2. Landini G, Randell DA, Galton. Intelligent imaging using Discrete Mereotopology. Proceedings of the Fourth ImageJ user and developer Conference, Luxembourg, 24-26 October, 2012, p 99-103. ISBN:2-919941-18-6.
    3. Landini G, Randell DA, Galton.Discrete Mereotopology in Histological Imaging. Medical Image Understanding and Analysis, In: Ela Claridge, Andrew D. Palmer and William T. E. Pitkeathly (editors), Proceedings of the 17th Conference on Medical Image Understanding and Analysis, 17th-19th July 2013, Birmingham, UK, pages 101-106. PDF
    4. Sioutis, M.; Condotta, J-F.; Salhi, Y.; Mazure, B.; Randell, D.A. (2015): ‘On Ordering SpatioTemporal Sequences to meet Transition Constraints: Complexity and Framework’, In: Proc AIAI-2015: 130-150. PDF
    5. Flight R. Landini G, Styles I, Shelton R, Milward M, Cooper P. Automated optimisation of cell segmentation parameters in phase contrast microscopy using discrete mereotopology. Proceedings of the 19th Conference on Medical Image Understanding and Analysis. Lincoln, Jul 15-17, 2015. PDF
    6. Randell DA, Landini, G, Galton A. Discrete Mereotopology for spatial reasoning in automated histological image analysis. IEEE Trans Patt Rec Mach Intell 35(3): 568-581, 2013. DOI: 10.1109/TPAMI.2012.128 PDF
    7. Galton A, Landini G, Randell D, Fouad S. Ontological levels in histological imaging. 9th International Conference on Formal Ontology in Information Systems, FOIS 2016, Annecy, Jul 4-6, 2016. PDF
    8. Fouad S, Landini G, Randell D, Galton A. Morphological separation of clustered nuclei in histological images. ICIAR 2016, Lecture Notes in Computer Science, 9730, pp. 599–607, 2016. Presented at 13th International Conference on Image Analysis and Recognition, ICIAR 2016, Póvoa de Varzim, Portugal, July 13-15, 2016. PDF
    9. Landini G, Randell D, Fouad S, Galton A. Automatic thresholding from the gradients of region boundaries. Journal of Microscopy 2016, Vol 256 (2): 185-195. PDF
    10. Florindo JB, Landini G, Bruno OM. Three-dimensional connectivity index for texture recognition. Pattern Review Letters 2016, Vol 84: 239-244. PDF
    11. Randell DA, Galton A, Fouad S, Mehanna A, Landini G. Model-based correction of segmentation errors in digitised histological images. Medical Image Understanding and Analysis, 21st Annual Conference, MIUA 2017, Edinburgh, UK, July 11–13, 2017, Proceedings, Communications in Computer and Information Science book series (CCIS, volume 723), p. 718-730. PDF
    12. Fouad S, Randell D, Galton A, Mehanna A, Landini G. Unsupervised superpixel-based segmentation of histopathological images with Consensus Clustering. Medical Image Understanding and Analysis. 21st Annual Conference, MIUA 2017, Edinburgh, UK, July 11–13, 2017, Proceedings, Communications in Computer and Information Science book series (CCIS, volume 723), p. 767-779. PDF
    13. Galton A, Fouad S, Landini G, Randell D. Errors and Artefacts in Histopathological Imaging. 8th International Conference on Biomedical Ontology (ICBO), Newcastle upon Tyne, Sep 13-15, 2017. PDF
    14. Fouad S, Randell D, Galton A, Mehanna A, Landini G. Unsupervised morphological segmentation of tissue compartments in histological images. PLoS ONE,12(11): 2017, e0188717. PDF
    15. Randell DA, Galton A, Fouad S, Mehanna H, Landini G. Mereotopological correction of segmentation errors in histological imaging. Journal of Imaging, Selected Papers from “MIUA 2017”, 2017, 3(4), 63. PDF
    16. Fouad S, Randell D, Galton A, Mehanna H, Landini G. Epithelium and stroma identification in histopathological images using unsupervised and semi-supervised superpixel-based segmentation. Journal of Imaging, Selected Papers from “MIUA 2017”, 2017, 3(4), 61. PDF
    17. Flight R, Landini G, Styles I, Shelton RM, Milward MR, Cooper PR. Automated non-invasive cell counting in phase contrast microscopy with automated image analysis parameter selection. Journal of Microscopy, 2018. PDF 
    18. Landini G, Fouad S, Randell DA, Mehanna H. Epithelium and stroma segmentation using multiscale superpixel clustering. 14th European Congress on Digital Pathology & 5th Nordic Symposium on Digital Pathology, Helsinki, May 29-Jun 1, 2018. Extended abstract in:  J Pathol Inform 2019, 10:32 (11 Nov 2019), S45-S47. DOI: 10.4103/2153-3539.270744  PDF
    19. Fouad S, Landini G, Robinson M, Randell D, Mehanna H. Imaging and machine learning methods for assessing HPV in situ hybridisation patterns in oropharyngeal carcinomas. 14th European Congress on Digital Pathology & 5th Nordic Symposium on Digital Pathology, Helsinki, May 29-Jun 1, 2018. J Pathol Inform 2019, 10:32 (11 Nov 2019), S13. DOI: 10.4103/2153-3539.270744   PDF
    20. Landini G, Galton A, Randell D, Fouad S. Novel applications of Discrete Mereotopology to Mathematical Morphology. Signal Processing: Image Communication 76:109-117, 2019. PDF
    21. Song T-H, Landini G, Fouad S, Mehanna H. Epithelial segmentation from in situ hybridisation histological samples using a deep central attention learning approach. IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), Venice, April 8-11, 2019. PDF
    22. Landini G, Martinelli G, Piccinini F. Colour Deconvolution – stain unmixing in histological imaging. Bioinformatics 2020 (in press), PDF
    23. Fouad S, Landini G, Robinson M, Song T-H, Mehanna H. Human papilloma virus detection in oropharyngeal carcinomas with in situ hybridisation using hand crafted morphological features and deep central attention residual networks. Computerized Medical Imaging and Graphics 2021, 88 (March) 101853. PDF
    24. Pereira Prado V, Landini G, Mosqueda Taylor A, Vargas P, Bologna Molina R. Spatial distribution of CD34 protein in primordial odontogenic tumour, ameloblastic fibroma and the tooth germ. Journal of Oral Pathology and Medicine Oct 7, 2022. PDF
    25. Rocha MM, Landini G, Florindo JB. Medical image classification using a combination of features from convolutional neural networks. Multimedia Tools and Applications 2022. PDF

G. Landini’s full list of references.