@InProceedings{ZampirolliStraLorePaul:2010:ApGrAn,
author = "Zampirolli, Francisco de Assis and Stransky, Beatriz and Lorena,
Ana Carolina and Paulon, F{\'a}bio Luis de Melo",
affiliation = "{Universidade Federal do ABC} and {Universidade Federal do ABC}
and {Universidade Federal do ABC} and {Universidade Federal do
ABC}",
title = "Segmentation and classification of histological images -
application of graph analysis and machine learning methods",
booktitle = "Proceedings...",
year = "2010",
editor = "Bellon, Olga and Esperan{\c{c}}a, Claudio",
organization = "Conference on Graphics, Patterns and Images, 23. (SIBGRAPI)",
publisher = "IEEE Computer Society",
address = "Los Alamitos",
keywords = "image analysis, mathematical morphology, graph analysis, machine
learning, tissue.",
abstract = "The characterization and quantitative description of histological
images is not a simple problem. To reach a final diagnosis,
usually the specialist relies on the analysis of characteristics
easily observed, such as cells size, shape, staining and texture,
but also depends on the hidden information of tissue localization,
physiological and pathological mechanisms, clinical aspects, or
other etiological agents. In this paper, Mathematical Morphology
(MM) and Machine Learning (ML) methods were applied to
characterize and classify histological images. MM techniques were
employed for image analysis. The measurements obtained from image
and graph analysis were fed into Machine Learning algorithms,
which were designed and developed to automatically learn to
recognize complex patterns and make intelligent decisions based on
data. Specifically, a linear Support Vector Machine (SVM) was used
to evaluate the discriminatory power of the used measures. The
results show that the methodology was successful in characterizing
and classifying the differences between the architectural
organization of epithelial and adipose tissues. We believe that
this approach can be also applied to classify and help.",
conference-location = "Gramado, RS, Brazil",
conference-year = "30 Aug.-3 Sep. 2010",
doi = "10.1109/SIBGRAPI.2010.51",
url = "http://dx.doi.org/10.1109/SIBGRAPI.2010.51",
language = "en",
ibi = "8JMKD3MGPBW34M/3868QFL",
url = "http://urlib.net/ibi/8JMKD3MGPBW34M/3868QFL",
targetfile = "article_sibgrapi_v8.pdf",
urlaccessdate = "2024, May 01"
}