Comparison of Variational Mode Decomposition and Empirical Mode Decomposition Features for Cell Segmentation in Histopathological Images - Karaaslan, Omer Faruk; Bilgin, Gokhan (IEEE, 2020)
In this study, it is aimed to increase the segmentation performance of the cells in the digital histopathological images by data compatible feature extraction methods. For this purpose, it is proposed to use empirical mode decomposition and variational mode decomposition methods as a comparison. Initially, the conversion of digital histopathological images from RGB color space to gray level is performed. Then, empirical mode decomposition and variational mode decomposition methods are applied to these images, and the obtained features are classified by using support vector machines which is a kernel-based classifier and random forests which is an ensemble-based classifier. The results are evaluated according to three different metrics. In the application results section, the results obtained in this study are presented in detail.
Keywords: Cell segmentation; histopathological image analysis; empirical mode decomposition; variational mode decomposition; GRADE; Computer Science; Engineering