Automated tool for the detection of cell nuclei in digital microscopic images: application to retinal images

• Published in: Molecular vision Vol. 12; pp. 949 - 960
• Main Authors: Byun, Jiyun, Verardo, Mark R, Sumengen, Baris, Lewis, Geoffrey P, Manjunath, B S, Fisher, Steven K
• Format: Journal Article
• Language: English
• Published: United States 16.08.2006
• Subjects: Algorithms; Animals; Automation; Cats; Cell Nucleus - ultrastructure; Diagnosis, Computer-Assisted; Image Processing, Computer-Assisted - standards; Microscopy; Microscopy, Confocal; Reproducibility of Results; Retina - ultrastructure; Retinal Detachment - pathology
• ISSN: 1090-0535

To develop an automated tool that provides reliable, consistent, and accurate results for counting cell nuclei in tissue sections. We propose a novel method based on an image processing algorithm to analyze large sets of digital micrographs. The nucleus detector design is based on a Laplacian of Gaussian filter. We use the leave-one-out cross validation method for estimating the generalization error, which is then used to choose the model and parameters of the proposed nucleus detector with both fluorescent and dye stained images. We also evaluate the performance of a nucleus detector by comparing the results with manual counts. When our nucleus detector is applied to previously unanalyzed images of feline retina, it correctly counts nuclei within the outer nuclear layer (ONL) with an average error of 3.67% ranging from 0 to 6.07%, and nuclei within the inner nuclear layer (INL) with an average error of 8.55% ranging from 0 to 13.76%. Our approach accurately identifies the location of cell bodies. Even though we have a relatively large error in the INL due to the large intra-observer variation, both manual counting and nucleus detector result in the same conclusion. This is the first time that cell death in the INL in response to retinal detachment is analyzed quantitatively. We also test the proposed tool with various images and show that it is applicable to a wide range of image types with nuclei varying in size and staining intensity. The proposed method is simple and reliable. It also has widespread applicability to a variety of sample preparation and imaging methods. Our approach will be immediately useful in quantifying cell number in large sets of digital micrographs and from high-throughput imaging. The tool is available as a plug-in for Image J.