J-Net system: A new paradigm for Artificial Neural Networks applied to diagnostic imaging

In this paper we present a new unsupervised artificial adaptive system, able to extract features of interest in digital imaging, to reduce image noise maintaining the spatial resolution of high contrast structures and the expression of hidden morphological features. The new system, named J-Net, belongs to the family of ACM systems developed by Semeion Research Institute. J-Net is able to isolate in an almost geological way different brightness layers in the same image. These layers seem to be invisible to the human eye and for the other mathematical imaging system. This ability of the J-Net can have important medical applications. Two examples of application are reported: the first in digital subtraction angiography for arterial stenosis diagnosis and the second in Multi-slice CT for lung cancer early detection and evolution prediction.