Nucleus Detection of Uterine Cervical Pap-Smears using Contour Trucking Method and Fuzzy Reasoning Rule
Hyunjun Woo, Young Woon Woo, Kwang-Baek Kim · International Journal of Bio-Science and Bio-Technology · 2013
In this paper, we apply a set of algorithms to classify normal and cancer nucleus from uterine cervical pap-smear images.First, we use lightening compensation algorithm to restore color images that have defamation through the process of obtaining 400x microscope magnification.Then, we remove the background from images with the histogram distributions of RGB regions.We extract nucleus areas from candidates by applying histogram brightness, Kapur method, and our own 8-direction contour tracing algorithm.Various binarization methods, cumulative entropy, masking algorithms are used in that process.Then, we are able to recognize normal and cancer nucleus from those areas by using three morphological features -directional information, the size of nucleus, and area ratio -with fuzzy membership functions and deciding rules we devised.The experimental result shows our method has low false recognition rate.