Abstract : In this paper we address a problem of HER2 and CEN-17 reactions detection in fluorescence in situ hybridization images. These images are very often used in situation where typical biopsy examination is not able to provide enough information to decide on the type of treatment the patient should undergo. Here the main focus is placed on the automatization of the procedure. Using an unsupervised neural network and principal component analysis, we present a segmentation framework that is able to keep the high segmentation accuracy. For comparison purposes we test the neural network approach against an automatic threshold method.
Khalid Saeed; Władysław Homenda. 15th IFIP International Conference on Computer Information Systems and Industrial Management (CISIM), Sep 2016, Vilnius, Lithuania. Springer International Publishing, Lecture Notes in Computer Science, LNCS-9842, pp.148-159, 2016, Computer Information Systems and Industrial Management. 〈10.1007/978-3-319-45378-1_14〉
https://hal.inria.fr/hal-01637485
Contributeur : Hal Ifip
<>
Soumis le : vendredi 17 novembre 2017 - 15:44:32
Dernière modification le : samedi 18 novembre 2017 - 01:16:39
Document(s) archivé(s) le : dimanche 18 février 2018 - 16:00:52
Marcin Stachowiak, Łukasz Jeleń. Automatic Segmentation Framework for Fluorescence in Situ Hybridization Cancer Diagnosis. Khalid Saeed; Władysław Homenda. 15th IFIP International Conference on Computer Information Systems and Industrial Management (CISIM), Sep 2016, Vilnius, Lithuania. Springer International Publishing, Lecture Notes in Computer Science, LNCS-9842, pp.148-159, 2016, Computer Information Systems and Industrial Management. 〈10.1007/978-3-319-45378-1_14〉. 〈hal-01637485〉
