Abstract:
In this paper, we present an original unsupervised segmentation scheme
which splits a grey level image into different sets of connected
pixels whose grey levels are homogeneous. This approach is based on
an analysis of a triangular table denoted "Normalized connectivity
degrees pyramid". This method is used in order to detect
cytomegalovirus retinitis lesions by fundus image analysis. First, we
determine the number of pixels classes and their cores. The core of
each class Cj is represented by an interval fo grey levels
![$[\min_{C_{j}}, \max_{C_{j}}]$](img2.gif){kind=small}
. For classification purpose, the
pixels whose grey level belongs to such an interval are labelled to
the corresponding class. The other pixels are assigned by comparison
of their conditional probability to belong to the different classes.