Segmentation and Classification of Brain Spect Images Using 3d Markov Random Field and Density Mixture Estimations

Thanks to its ability to yield functionally rather than anatomicallybased information, the SPECT imagery technique has become a great help in the diagnostic of cerebrovascular diseases. Nevertheless, SPECT images are very noisy and consequently their interpretation is difficult. In order to facilitate this visualization, we propose an unsupervised 3D Markovian model allowing to segment a brain SPECT image into three classes, corresponding to the three existing cerebral tissues, respectively ; “cerebrospinal fluid”, “white matter” and “grey matter”. This unsupervised Markovian model relies on a preliminary distribution mixture parameter estimation step which takes into account the diversity of the laws in the distribution mixture of a SPECT image. Next, in order to help the classification of these images, some features extracted from this segmentation map and the distribution mixture parameters are then exploited to constitute a discriminant feature vector for each image from our database. These feature vectors are then clustered into two distinct classes, namely ; “healthy brains” and “diseased brains” (i.e., brains with possible cerebrovascular diseases) by using once more a distribution mixture-based clustering procedure. The effectiveness of this classification scheme was tested on a database of 46 healthy and diseased brain images. The rate of good classification (74%) indicates that the proposed method may be useful in a first screening for a brain disease prior to a more thorough examination by the nuclear physician.