Brain image analysis by using sensor-array-signal processing technique

Various imaging modalities such as MRI, X-ray CT, PET/SPECT, and MR-angiography are used for brain imaging. The authors provides a non-model-based image analysis technique for these imaging modalities. In this approach, the brain tissue type and organ structure are represented by the image regions. The technique formulates the region detection problem in a multidimensional signal processing framework such that a signal structure similar to sensor-array-processing signal presentation is created and the advanced sensor-array-signal processing techniques are employed. Following the region detection, the image analysis is performed by segmentation which is completed by region parameter estimation and pixel classification. The proposed technique is an unsupervised, eigenstructure approach. It eliminates the ad-hoc assumptions in image modeling, and possesses extensive computation speed superiority over existing model-based approaches. The most important feature is that it properly utilizes the spatial correlations among the pixels. The results obtained by applying this technique to the simulated, MRI, CT, PET/SPECT, and MR-angiography images demonstrate its promise and effectiveness. The major applications of this technique in the brain image analysis are the tissue classification and quantification