The Correlation Analysis of the Shape Parameters for Endothelial Image Characterisation

Abstract

Microscopic images of corneal endothelium cells are investigated to deliver information about their medical state. Although this could be achieved automatically, this examination is manual and very time consuming. Two medical parameters for endothelial layer quality description have been introduced and more are planned. Yet, since they will exploit image processing, a thoughtful overview of applicable existing shape parameters is necessary. This work investigates the possibility of exploiting well-known image processing techniques for describing the endothelial layer by calculating information about shape features using spatial moments or topological attributes. The comparison concentrates on finding which shape measures could be combined to improve descriptions, and which cannot due to their high correlation and the fact that they do not contain any new information. The performed experiments revealed a set of 17 non-correlated features and four groups of shape parameters that show some correlation, but one representative can always be selected. Moreover, the investigation proved some correlation between the metrics used in medicine and considered shape features.

Deep Learning as a Tool for Automatic Segmentation of Corneal Endothelium Images

“…This is not a problem when only the segmentation accuracy is considered, yet further analysis of cell's shapes (like those suggested in [7]) could be affected by the boundary line thickness. Therefore further processing is necessary.…”

“…Therefore, an automatic solution for this task is sought. Such a method should allow to automatically detect cell borders and then describe the cell structure [6][7][8]. Figure 1 shows an example of the corneal endothelium image.…”

The shape language in application to the diagnosis of cervical vertebrae pathology

“…Let us mention them very briefly. Some of them use shape properties such as elongation, perimeter, convexity and orientation, whereas others are based on invariant moments, and others are based on multi-scale shape representation—see [ 8 , 35 ] and references given there. Another approach to shape description, proposed in [ 21 ], is based on statistical methods.…”

CAS: Cell Annotation Software – Research on Neuronal Tissue Has Never Been so Transparent

“…An object’s shape is described by topological attributes (Russ 1998 ; Gonzalez & Woods 2001 ; Jahne 2002 ), of which only those not affected by translation and rotation were chosen in this research. These have found applications in research, where shape plays an important role (Nurzynska et al 2013 ; Nurzynska & Piorkowski 2016 ; Piorkowski et al 2017 ). The formulation of many attributes exploits information about area A and perimeter P to define a circularity measure.…”