Year: 2016
Type: article
Abstract: In biomedical imaging, segmentation and analysis play an important diagnostic role.Nuclei and nucleoli segmentation and classification have a significant impact on the cancer and tumor diagnostics in biological and medical research studies.Typically, segmentation is difficult in microscopic images because of object shapes and clustering in many samples.In this work we introduce a method that combines simplicity and efficiency.The proposed method utilizes ImageJ framework to automatically segment and classify nuclei and nucleoli after applying some preprocessing techniques to improve the image quality and remove noise.The required preprocessing steps differ based on the kind of segmentation required.Both 2D and 3D segmentation are achieved for the nuclei and nucleoli.The analysis approach provides statistics about volume, area, surface and other properties of the segmented nuclei and nucleoli.The classification process then groups the segmented nuclei and nucleoli based on the previous criteria.Finally, the visualization process shows the results of the proposed method overlaid on the original data set.The proposed method provides a very efficient system for nuclei and nucleoli segmentation and achieves about 98 % accuracy.Furthermore, the plugin is extremely fast when compared to manual segmentation especially with large data sets.
Author: Adil Lateef Mahmood Albukhnefis
Institution: Kent State University
Language: English
Subfield: Biophysics
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W2612471577