Real time kidney ultrasound images segmentation: a prospective study

摘要

Segmentation of ultrasound kidney images represents a challenge due to low quality data. Speckle, shadows, signal dropout and low contrast make segmentation a harsh task. In addition, kidney ultrasound imaging presents a great variability concerning the organ's shape on the image. This characteristic makes learning methods hard to use. The aim of this study is to develop a real time kidney ultrasound image segmentation method usable during surgical operations such as punctures. To deal with real time constraints, we decided to focus on region based methods and particularly split and merge algorithm. In this prospective study, the selection of the interesting area in the initial image is made by the physician, drawing a coarse bounding box around the organ. A pre-processing phase is first performed to correct image's artefacts. This phase is composed of three major steps. First, an image specification is made between the image to segment and a reference one. Then, a Haar wavelet filtering method is applied on the resulting image and finally an anisotropic diffusion filter is applied to smooth the result. Then, a split and merge algorithm is applied on the resulting image. Both split and merge criteria are based on regions statistics. Our method has been successfully applied on a set of 22 clinical images coming from 10 different patients and presenting different points of view regarding kidney's shape. We obtained very good results, for an average computational time of 8.5 seconds per image.