Detrended fluctuation analysis of brain hemisphere magnetic resonnance images to detect cerebral arteriovenous malformations

We present a fractal-based methodology to analyze brain magnetic resonance images (MRI) for the automated detection of cerebral arteriovenous malformations (AVM). First, the MRI is split into right and left hemispheres components whose fractal dimensions (FD) are estimated using detrended fluctuation analysis (DFA). Then, the obtained FD values are used to characterize healthy and AVM-affected brain MRIs. Using a database of twenty-eight images, and ten-fold cross validation, classification by a support vector machine (SVM) was 100% accurate when using either a linear or a radial basis Gaussian kernel, and the total image processing time was 32.75 s on a midrange PC station. It is concluded that the presented cerebral AVM detection system is both simple and accurate, and its processing time makes it compatible for use in a clinical environment, should it performance be confirmed with a larger image database.