Anzeige
Erschienen in:
2020 | OriginalPaper | Buchkapitel
Edge Enhancement for Image Segmentation Using a RKHS Method
verfasst von : Liam Burrows, Weihong Guo, Ke Chen, Francesco Torella
Erschienen in: Medical Image Understanding and Analysis
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Abstract
Image segmentation has many important applications, particularly in medical imaging. Often medical images such as CTs have little contrast in them, and segmentation in such cases poses a great challenge to existing models without further user interaction. In this paper we propose an edge enhancement method based on the theory of reproducing kernel Hilbert spaces (RKHS) to model smooth components of an image, while separating the edges using approximated Heaviside functions. By modelling using this decomposition method, the approximated Heaviside function is capable of picking up more details than the usual method of using the image gradient. Further using this as an edge detector in a segmentation model can allow us to pick up a region of interest when low contrast between two objects is present and other models fail.