基于视觉感知增强的最大密度投影算法

Maximum Intensity Projection Based on Visual Perception Enhancement

提出一种基于视觉感知增强的最大密度投影算法,无需调节复杂的传输函数,就可以有效增强体数据内部最大密度特征的深度感知和形状感知.在传统的最大密度投影算法的基础上,利用梯度模属性精确查找特征或相似特征的边界,以确定最佳法向特征;利用最佳法向特征的深度信息自适应地修改局部光照系数,进而对最大密度特征进行光照处理,以获得视觉感知增强的可视化结果;采用基于密度值和三维空间距离的双阈值区域增长策略,动态区分感兴趣区域和背景区域,交互地实现特征突出显示.实验结果表明,该算法在传统算法的基础上进一步增强了最大密度特征的视觉感知,并提供了丰富的形状信息和背景补偿信息,具有较强的实用性.

This paper proposed a maximum intensity projection method to enhance the depth and shape perception of the internal maximum intensity features, without a sophisticated or time-consuming transfer function specification. On the basis of a traditional maximum intensity projection, the study first searched for the boundary sample with a similar intensity value and the optimal normal in front of the maximum intensity feature. Through by comparing the intensity and gradient norm. Next, the local illumination coefficients were updated according to the depth of boundary structures, the consequential depth-based shading results largely enhanced the depth, and the shape perception of internal feasible structures. A two-threshold region growing scheme was designed to perform and further highlight the features of interest. The seed was selected by users interactively on the rendered image, and the growing process depended on the intensity values and 3D spatial distances of the boundary samples with optimal normal. The comparison results showed that the proposed method provided more depth cues and shape information of the maximum intensity features than traditional methods and had practical applications in medical and engineering fields.