Résumé
Objective To investigate the feasibility and value of automatic segmentation of infant brain images based on diffusion tensor image (DTI).Methods A method of segmentation of infant brain based on DTI images was proposed.The method was mainly included two stages:①Extracting the cerebrospinal fluid (CSF) using the distribution of water;②Extracting the white matter (WM) adopting the anisotropic diffusion of water in neurons,followed by distinguishing the gray matter (GM) component.Results Through the feature selection method designed in this study,the effective DTI feature combination was selected.The first step was to extract CSF with mean diffusity (MD) and the third eigenvalue (L3),and the second step was to extract WM and GM with fractional anisotropy (FA) and the L3.The highest average similarity was obtained by the two steps.The two-step segmentation could be successfully performed in infant brain image segmentation and satisfied with the split effect.Conclusion The automatic segmentation of infant brain based on DTI in this study is reasonable and feasible,and has high segmentation accuracy.
Résumé
In this paper a 3D morphological composition of transformations for brain extraction on brain Magnetic Resonance Images T1 (MRI T1) is presented. The proposal makes use of two morphological connected transformations, the lower leveling and a family of the viscous alternating sequential filters (VASFs). The properties of these operators -which consist in the control of the reconstruction process of a marker into the original image-, are exploited to segment the brain in 20 volumes of MRI T1. The segmented brains are compared with respect to: i) the segmentations obtained from BET which is popular among the scientific community for segmenting the brain; and ii) manual segmentations. The computed indices indicate that the proposed transformation produces good results during its performance. The consumed time for the algorithm during the execution is acceptable and it can be implemented in Matlab.
En este artículo se presenta una composición de transformaciones morfológicas para la extracción del cerebro en imágenes de resonancia magnética T1 (MRI T1) en 3D. La propuesta hace uso de dos transformaciones morfológicas conexas, la nivelación inferior y una familia de filtros viscosos alternados secuenciales (VASFs). Las propiedades de estos operadores- las cuales consisten en el control del proceso de reconstrucción de un marcador dentro de la imagen original-, se explotan para segmentar el cerebro de 20 volúmenes de MRI T1. Los cerebros segmentados se comparan respecto a: i) segmentaciones obtenidas a partir del algoritmo BET, el cual es popular en la comunidad científica para la segmentación del cerebro; y ii) segmentaciones manuales. Los índices calculados indican que la transformación propuesta produce buenos resultados en su desempeño. El tiempo empleado por el operador durante su ejecución es aceptable además de que la propuesta puede ser implementada en Matlab.