Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Automatic quantitative analysis of brain tissues has a high importance. Hover, lack of high precision still makes results unreliable.  presents a novel system which separates cortical GM from WM. It imitates human perception like edge detection algorithms but recovers their disability in segmentation. System is fully automatic and unsupervised. Fastened segments of geodesic passive contours (FSG) are utilized and the perceptive sensitivity to edges is imitated. This nature of the solution proved to treat the inhomogeneity and noise problems ll. The technique is tested on both real and synthetic databases and compared with widely used software of SPM and works faster. Our technique succeeded in getting average misclassification rate of 4.8% for WM and correct GM-WM boundary rate of 77% being very close to experts' agreement.
['Ali Iskurt', 'Yajar Becerikli']
I am writing a research paper on a idea Automatic extraction of cortical gray matter by geodesic contours
[{"from": "human", "value": "I am writing a research paper on a idea Automatic extraction of cortical gray matter by geodesic contours"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Automatic quantitative analysis of brain tissues has a high importance. Hover, lack of high precision still makes results unreliable.  presents a novel system which separates cortical GM from WM. It imitates human perception like edge detection algorithms but recovers their disability in segmentation. System is fully automatic and unsupervised. Fastened segments of geodesic passive contours (FSG) are utilized and the perceptive sensitivity to edges is imitated. This nature of the solution proved to treat the inhomogeneity and noise problems ll. The technique is tested on both real and synthetic databases and compared with widely used software of SPM and works faster. Our technique succeeded in getting average misclassification rate of 4.8% for WM and correct GM-WM boundary rate of 77% being very close to experts' agreement."}]