ABSTRACT
The pathogenesis of autistic spectrum disorders is complicated and the exact etiology and pathogenesis remain unclear. Major advances in spatial information technology have revealed the potential of spatial information technology as an effective tool in research and treatment for children with autistic spectrum disorders. However, there are too many fragmented research topics. According to recent reports on spatial information technology, there is no precedent for the application of spatial information technology in autistic spectrum disorders in China. Space information technology analysis for autistic spectrum disorders can be divided into the following steps: pre analysis, spatial clustering analysis, spatial model analysis, and interpretation of related results. It is hopeful that the space information technology can provide proposals for the future research on the pathogenesis of autistic spectrum disorders in our country.
Subject(s)
Child , Humans , Autistic Disorder , China , Cluster Analysis , Information Technology , Spatial AnalysisABSTRACT
Se determinó en dos estudios la relación entre la connotación emocional negativa y la memorización de la información espacial. El propósito fue demostrar que un estímulo emocional negativo podría provocar una reacción que facilitara la integración de los elementos en memoria, como sus características y propiedades viso-espaciales. 76 estudiantes universitarios participaron de estos dos estudios. Durante el primero, se realizó una tarea de denominación. El plan experimental se componía de un factor de grupo y uno de inducción. Fue utilizado el test X2. No se encontró un efecto facilitador de la emoción sobre la integración de los datos perceptivos. El segundo estudio era una tarea de reconocimiento de la estructura espacial. Tres variables independientes fueron parte del estudio (La inducción, la complejidad y la replicación). Se observó que los desempeños no fueron significativamente mejores después de una simulación emocional negativa que después de una simulación neutra o ausente.
Two studies were realized to determine the relationship between the emotional negative connotation and the memorization of the spatial information. The purpose was to prove that a negative emotional stimulus could trigger a reaction that would facilitate the integration of elements in the memory, elements such as visual-spatial characteristics. 76 university students participated in the two studies. During the first study, a denomination task was performed. The experimental plan had two variables: a group variable and an induction variable. It was found that there is no facilitator effect in the emotion over the integration of the perceptive data. The second study was a recognition task of a spatial structure. Three independent variables were part of the study (induction, complexity, and replication). It was observed that the performances in both studies were not significantly better after a negative emotional simulation than after a neutral or absent simulation.
ABSTRACT
Objective To segment brain magnetic resonance (MR) images corrupted by noises. Methods We presented a novel Fuzzy C-Means (FCM) algorithm for image segmentation. The algorithm was by modifying the objective function in the conventional FCM. Firstly,by using kernel method,the original Euclidean distance in the FCM was replaced by a kernel-induced distance. Then,a spatial penalty term was added to the objective function to compensate the influence of the neighboring pixels on the center pixel. Results Segmentation results on a four-class synthetic image corrupted by salt & pepper noise shows that the new algorithm is less speckled and smoother. The new algorithm is applied to simulation MR images and is shown to have less misclassification rate than the other FCM-based methods. Conclusion The results of experiments show that the proposed algorithm is more robust to noise than other FCM-based methods.
ABSTRACT
Fuzzy c-means (FCM) clustering algorithm is a popular model widely used in the segmentation of magnetic resonance image (MRI). The conventional FCM doesn't involve the spatial information of MRI and then unexpected segmentation results appear when it is applied to inhomogeneous MRI with noise and bias field. Modifying the objective function of FCM and introducing a variable as the parameter to control the tight degree of neighborhood effect present a spatial model to FCM clustering algorithm. The variable can reasonably use the spatial information of MRI. The experiment results show that the proposed algorithm can provide a powerful segmentation than the conventional FCM and others.