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 useful aid to the doctor in clinical diagnosis. In this research area the improvements in systems for clinical specular microscopy in vivo made a strong contribution to the study and the comprehension of the physiopathology of corneal endothelium. The more recent systems allow acquisition of the images and morphometric analysis. Nevertheless, the 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 highly 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 is presented. The tool makes use of an analogue cellular neural network algorithm that allows both cell shape recognition and endothelial cell area measurement
Salerno, M., Sargeni, F., Bonaiuto, V., Amerini, P., Cerulli, L., Ricci, F. (1998). A new CNN Based Tool for an Automated Morphometry Analysis of the Corneal Endothelium. In Proc of 5-th IEEE International Workshop on CNN and their Application (CNNA 98) (pp.169-174) [10.1109/CNNA.1998.685358].
A new CNN Based Tool for an Automated Morphometry Analysis of the Corneal Endothelium
SALERNO, MARIO;SARGENI, FAUSTO;BONAIUTO, VINCENZO;
1998-01-01
Abstract
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 useful aid to the doctor in clinical diagnosis. In this research area the improvements in systems for clinical specular microscopy in vivo made a strong contribution to the study and the comprehension of the physiopathology of corneal endothelium. The more recent systems allow acquisition of the images and morphometric analysis. Nevertheless, the 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 highly 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 is presented. The tool makes use of an analogue cellular neural network algorithm that allows both cell shape recognition and endothelial cell area measurementI documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
