Abdennacer El-Ouarzadi*, Anass Cherkaoui, Abdelaziz Essadike and Abdenbi Bouzid
This paper aims to evolve a fully automatized segmentation and detection of cerebral tumors using medical resonance images. To solve the problem of manual segmentation, which is a time consuming, error prone and delicate procedure, we implement two optically based segmentation methods based in our previous works [1-3] in the same architecture to improve the performance and accuracy. We combine the Vander Lugt optical Correlator (VLC) with a new approach based on Optical Scanning Holography (OSH). The two main characteristics of these methods are rapidity and automaticity, which makes our approach relevant, compared to other segmentation methods. The suggested method achieves high accurate detection of tumors. We have achieved a principal objective to upgrade the active contour theory from semiautomatic to automatic status by means of the OSH and VLC techniques, which leads to a more reliable brain tumor detection. In addition, it provides more reliable performances by the averages of sensitivity equals to 0.98, Hausdorff distance equals to 2.00, dice coefficient equals to 0.98, specificity equals to 1.00 and more rapidly with computation time averaging 0.30 seconds per frame. The underlying physics behind the hybrid method is the trustworthy extraction maxima of components in phase of the scanned current by OSH and the ones deduced from the correlation plane by VLC that correspond to the tumors’ positions.
