Segmentation and Detection of Nuclei in Silver Stained Cell Specimens for Early Cancer Diagnosis

Abstract

For successful cure, cancer has to be detected as early as possible. Since cancer starts from a single cell, this can best be done using cytopathological methods. One important diagnostically relevant measure is the proliferation rate of the cells, which can be estimated from segmented silver stained nuclei. However, the microscopy images of silver stained specimens vary strongly in intensity and contrast and are furthermore compromised by an overall texture.We show that a precise segmentation of the nuclei is possible using a two-step approach. First, an oversegmentation with the mean shift algorithm is obtained. In a second step, these regions are merged to objects, guided by a suitable shape model, viz an ellipse, but simultaneously allowing deviations from this shape model. The segmentation results are compared to a gold standard of 8617 nuclei from 23 specimens of the thyroid gland, achieving a mean areal segmentation error of ΔĀ nucleus = 12μm 2 per nucleus.

Biologically Inspired Anomaly Detection in Pap-Smear Images

“…Mean-shift is a popular method to segment images and videos. It has been widely used in cell images segmentation with good results [15,16,17].…”

Automatic segmentation of lizard spots using an active contour model

“…One of the most important applications is disease diagnosis [10]. For disease diagnosis some methods are found in the literature.…”

Semi-Supervised Recognition of the Diploglossus Millepunctatus Lizard Species using Artificial Vision Algorithms

“…Spots segmentation has been used for medical purposes. One of the most important applications is disease diagnosis [14]. For disease diagnosis some methods are found in the literature.…”