Color Image Segmentation using Competitive Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Magnetic resonance imaging (MRI) is becoming increasingly popular as a second-level technique, performed after ultrasonography (US) scanning, for detecting morphologic brain abnormalities. For this reason, several medical researchers in the past few years have investigated the field of fetal brain diagnosis from MR images, both to create models of the normal fetal brain development and to define diagnostic rules, based on biometric analysis; all these studies require the segmentation of cerebral structures from MRI slices, where their sections are clearly visible. A problem of this approach is due to the fact that fetuses often move during the sequence acquisition, so that it is difficult to obtain a slice where the structures of interest are properly represented. Moreover, in the clinical routine segmentation is performed manually, introducing a high inter and intra-observer variability that greatly decreases the accuracy and significance of the result. To solve these problems in this paper we propose an algorithm that builds a 3D representation of the fetal brain; from this representation the desired section of the cerebral structures can be extracted. Next, we describe our preliminary studies to automatically segment ventricles and internal liquors (from slices where they are entirely visible), and to extract biometric measures describing their shape. In spite of the poor resolution of fetal brain MR images, encouraging preliminary results have been obtained.