Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A procedure to improve the convergence rate for affine registration methods of medical brain images when the images differ greatly from the template is presented. The methodology is based on a histogram matching of the source images with respect to the reference brain template before proceeding with the affine registration. The preprocessed source brain images are spatially normalized to a template using a general affine model with 12 parameters. A sum of squared differences beten the source images and the template is considered as objective function, and a Gauss-Newton optimization algorithm is used to find the minimum of the cost function. Using histogram equalization as a preprocessing step improves the convergence rate in the affine registration algorithm of brain images as  show in this work using SPECT and PET brain images.
['Diego Salas-Gonzalez', 'Juan Manuel Górriz', 'Javier Ramírez 0001', 'Pablo Padilla', 'I. A. Illán']
I am writing a research paper on a idea Improving the Convergence Rate in Affine Registration of PET and SPECT Brain Images Using Histogram Equalization
[{"from": "human", "value": "I am writing a research paper on a idea Improving the Convergence Rate in Affine Registration of PET and SPECT Brain Images Using Histogram Equalization"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A procedure to improve the convergence rate for affine registration methods of medical brain images when the images differ greatly from the template is presented. The methodology is based on a histogram matching of the source images with respect to the reference brain template before proceeding with the affine registration. The preprocessed source brain images are spatially normalized to a template using a general affine model with 12 parameters. A sum of squared differences beten the source images and the template is considered as objective function, and a Gauss-Newton optimization algorithm is used to find the minimum of the cost function. Using histogram equalization as a preprocessing step improves the convergence rate in the affine registration algorithm of brain images as  show in this work using SPECT and PET brain images."}]