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Combined Hierarchical Watershed Segmentation and SVM classification for Pap-Smear Cell Nucleus Extraction This publication appears in: Computacion y Sistemas - An International journal of computing science and applications Authors: M. Orozco-Monteagudo, C. Mihai, H. Sahli and A. Taboada-Crispi Volume: 16 Issue: 2 Pages: 133-145 Publication Year: 2012
Abstract: In this paper, we propose a two-phase
approach to nuclei segmentation/classification in Pap
smear test images. The first phase, the segmentation
phase, includes a morphological algorithm (watershed)
and a hierarchical merging algorithm (waterfall). In the
merging step, waterfall uses spectral and shape
information as well as the class information. In the
second phase, classification, the goal is to obtain
nucleus regions and cytoplasm areas by classifying the
regions resulting from the first phase based on their
spectral and shape features, merging of the adjacent
regions belonging to the same class. Between the two
phases, three unsupervised segmentation quality
criteria were tested in order to determine the best one
selecting the best level after merging. The classification
of individual regions is obtained using a Support Vector
Machine (SVM) classifier. The segmentation and
classification results are compared to the segmentation
provided by expert pathologists and demonstrate the
efficacy of the proposed method.
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