Abstract: A method for predicting clinical severity of a neurological disorder includes steps of: a) identifying, according to a magnetic resonance imaging (MRI) image of a brain, brain image regions each of which contains a respective portion of diffusion index values of a diffusion index, which results from image processing performed on the MRI image; b) for one of the brain image regions, calculating a characteristic parameter based on the respective portion of the diffusion index values; and c) calculating a severity score that represents the clinical severity of the neurological disorder of the brain based on the characteristic parameter of the one of the brain image regions via a prediction model associated with the neurological disorder.

Abstract: Disclosed herein is a method for diagnosing a neurological disorder based on at least one magnetic resonance imaging (MRI) image. The method includes identifying brain image regions that contain a respective portion of diffusion index values of at least one diffusion index. For each of the brain image regions, a characteristic parameter based on the respective portion of the diffusion index values is calculated. a diagnoses is then made for the brain using one of predetermined categories of the neurological disorder by performing classification on a combination of the characteristic parameters via a classifier.

Abstract: A method is to be implemented by a computing device and includes steps of: a) identifying, according to a magnetic resonance imaging (MRI) image, brain image regions each of which contains a respective portion of diffusion index values of at least one diffusion index, which results from image processing performed on said at least one MRI image; b) for each of the brain image regions, calculating a characteristic parameter based on the respective portion of the diffusion index values; and c) diagnosing the brain examined with one of predetermined categories of the neurological disorder by performing classification on a combination of the characteristic parameters via a classifier.

Abstract: A method for predicting clinical severity of a neurological disorder includes steps of: a) identifying, according to a magnetic resonance imaging (MRI) image of a brain, brain image regions each of which contains a respective portion of diffusion index values of a diffusion index, which results from image processing performed on the MRI image; b) for one of the brain image regions, calculating a characteristic parameter based on the respective portion of the diffusion index values; and c) calculating a severity score that represents the clinical severity of the neurological disorder of the brain based on the characteristic parameter of the one of the brain image regions via a prediction model associated with the neurological disorder.

Abstract: A network resource allocation system and method of the same are provided according to the present invention. The system and method are applicable to a wireless mesh network, the method including: programming traffic of connections between routers; exchanging data between neighboring routers, thereby enabling the routers to perform dispersive network resource allocation according to the exchanged data; checking a local time window and a global time window for expiration by the router; and, under the condition of local performance deterioration or local traffic variation, activating a local routing adjustment mechanism by the router, wherein, if global performance deterioration currently exists, then activating a global optimization mechanism by a central controller, and, if not, then resetting the global time window.

Abstract: A network resource allocation system and method of the same are provided according to the present invention. The system and method are applicable to a wireless mesh network, the method including: programming traffic of connections between routers; exchanging data between neighboring routers, thereby enabling the routers to perform dispersive network resource allocation according to the exchanged data; checking a local time window and a global time window for expiration by the router; and, under the condition of local performance deterioration or local traffic variation, activating a local routing adjustment mechanism by the router, wherein, if global performance deterioration currently exists, then activating a global optimization mechanism by a central controller, and, if not, then resetting the global time window.