A fast 3D adaptive bilateral filter for ultrasound volume visualization. Issue 133 (September 2016) - Kwon, Koojoo; Kim, Min-Su; Shin, Byeong-Seok
Highlights: We propose a noise removal method for 3D ultrasound volume data using parallel bilateral filter. We can adjust the filter window size proportionally depending on the ultrasound coordinates. We performed to compare noise removal for anisotropic diffusion and adaptive bilateral filtering. We can more efficiently and properly remove noise from ultrasound data using our method. Abstract: Background and objective: This paper introduces an effective noise removal method for medical ultrasound volume data. Ultrasound data usually need to be filtered because they contain significant noise. Conventional two-dimensional (2D) filtering methods cannot use the implicit information between adjacent layers, and existing 3D filtering methods are slow because of complicated filter kernels. Even though one filter method utilizes simple filters for speed, it is inefficient at removing noise and does not take into account the characteristics of ultrasound sampling. To solve this problem, we introduce a fast filtering method using parallel bilateral filtering and adjust the filter window size proportionally according to its position. Methods: We devised a parallel bilateral filtering by obtaining a 3D summed area table of a quantized spatial filter. The filtering method is made adaptive by changing the kernel window size according to the distance from the ultrasound signal transmission point. Results: Experiments were performed to compare the noise removal and loss of original data of the anisotropic diffusion filtering, bilateral filtering, and adaptive bilateral filtering of ultrasound volume-rendered images. The results show that the adaptive filter correctly takes into account the sampling characteristics of the ultrasound volumes. Conclusions: The proposed method can more efficiently remove noise and minimize distortion from ultrasound data than existing simple or non-adaptive filtering methods.