3D Brain Segmentation Based on Immune Genetic Algorithm

To solve large time-consumption subject of the complete search (CS), the instability and inaccurateness of the simple genetic algorithm (SGA), a 3D brain data segmentation procedure, utilizing optimal entropy multi-thresholding method, is proposed. Global maximum entropy for the segmentation is yielded fast by the immune genetic algorithm (IGA). Compared to the SGA, the IGA constructs a better selection scheme and ensures various individuals to be selected for preserving the diversity of the population. Meanwhile the general expressing form of this kind of selection probability is given. The proposed method also includes the elitist strategy and the adaptive crossover and mutation mechanism to enhance the convergence. Results of 50 simulations demonstrate that the 3D brain volume can be successfully classified into three parts: the white matter, the gray matter and the cerebrospinal fluid on the IDL platform. The stability and accuracy of our algorithm, compared with other methods, are all improved according to their performance contrasts.