Automatic Segmentation Framework for Fluorescence in Situ Hybridization Cancer Diagnosis

Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9842)

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.

Keywords

FISH Pattern recognition Image processing Computer aided diagnosis Breast cancer Nuclei segmentation HER2 Dot counting SOM PCA 
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Copyright information

© IFIP International Federation for Information Processing 2016

Authors and Affiliations

  1. 1.Department of Compuer EngineeringWrocław University of Science and TechnologyWrocławPoland

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