Combined Hierarchical Watershed Segmentation and SVM Classification for Pap Smear Cell Nucleus Extraction
Extracción de núcleos de células en imágenes de la prueba de Papanicolaou usando watershed jerárquico y máquinas de vectores soporte
Maykel Orozco-Monteagudo1, Cosmin Mihai2, Hichem Sahli 2, 3, and Alberto Taboada-Crispi 1
1 Centro de Estudios de Electrónica y Tecnologías de la Información (CEETI), Universidad Central de Las Villas, Santa Clara, Cuba morozco@uclv.edu.cu, ataboada@uclv.edu.cu.
]]> 2 Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel, Brussels, Belgium cmihai@etro.vub.ac.be.3 Interuniversity Microelectronics Centre (IMEC), Leuven, Belgium hsahli@etro.vub.ac.be.
Article received on 30/04/2011.
Accepted on 29/02/2012.
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.
Keywords. Microscopic images, cell segmentation, watershed, SVM.
]]> Resumen
En el presente trabajo se presenta un método en dos etapas para la segmentación y clasificación de núcleos de células en imágenes tomadas de la prueba de Papanicolaou. La primera etapa, la etapa de segmentación, está formada por un algoritmo morfológico (watershed o marcas de agua) y un algoritmo jerárquico de mezclado (waterfall o salto de agua). Para realizar el mezclado de regiones, waterfall usa información espectral, de forma y de las regiones que se separarán. En la segunda etapa, la etapa de clasificación, el objetivo es obtener los núcleos a partir de las clasificaciones de las regiones obtenidas en la primera etapa. Antes de realizar la clasificación, fueron probadas tres medidas no supervisadas de calidad de la segmentación para determinar el mejor resultado de la mezcla de regiones. La clasificación de las regiones se realizó usando Máquinas de Vector Soporte. Los resultados fueron comparados con las segmentaciones realizadas por patólogos demostrándose la eficacia del método propuesto.
Palabras clave. Segmentación, imágenes microscópicas, segmentación de células, marcas de agua, máquinas de vector soporte.
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