A new CNN based tool for an automated morphometry analysis of the corneal endothelium

被引：10

作者：

Salerno, M ^{[1
]}

Sargeni, F ^{[1
]}

Bonaiuto, V ^{[1
]}

Amerini, P ^{[1
]}

Cerulli, L ^{[1
]}

Ricci, F ^{[1
]}

机构：

[1] Univ Roma Tor Vergata, Dept Elect Engn, I-00133 Rome, Italy

来源：

CNNA 98 - 1998 FIFTH IEEE INTERNATIONAL WORKSHOP ON CELLULAR NEURAL NETWORKS AND THEIR APPLICATIONS - PROCEEDINGS | 1998年

关键词：

D O I：

10.1109/CNNA.1998.685358

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Cellular Neural Networks show high performance capabilities in real time image processing applications. For this reason, their use in biomedical image analysis can be a real useful aid to the doctor in clinical diagnostic In this research area the improvements in the systems for the clinical specular microscopy in vivo gave a strong contribute to the study and the comprehension of the physiopathology of corneal endothelium. The more recent systems allow the acquisition of the images and the morphometric analysis as well. Nevertheless, their results (i.e. the automated reconstruction of the endothelium cell borders) are often inaccurate. Moreover, they do not allow the correct recognition of the cell shapes. On the other hand even if the semiautomatic systems allow an effective evaluation of the cell shape, they are high time consuming and provide results that could be affected by the criterion used by the operator in the cell corner detection. In this paper a software tool for the full automated morphometric analysis of corneal endothelium images will be presented The tool makes use of an analogue Cellular Neural Networks algorithm that allows both cell shape recognition and endothelial cell area measurement.

引用

页码：169 / 174

页数：4