Features extraction and analysis methods for sequences of ultrasound Images

Our principal motivation is to study time sequences of echocardiographic raw data to track specific anatomical structures. First, we show that the image processing can make direct use of the audio signal data, avoiding loss of information and yielding optimal results. Second, we develop a strategy which takes a time sequence of raw data as input, computes edges, initiates a segmentation of a pre-selected anatomical structure, and uses a derformable model for its temporal tracking. This approach is validated in a real-time sequence of ultrasound images of the heart to track the left auricle and the mitral valve.     

基于尺度空间分析和概率松弛的细胞图像分割算法

提出基于直方图尺度空间分析和概率迭代松弛的混合方法分割背景复杂的细胞图像。首先根据原始图像直方图的尺度空间特性和多尺度滤波结果,选取最佳阈值将图像分为多个类。然后,利用迭代的概率松弛法对粗分结果进行优化,并在后处理阶段中切割粘连细胞。将该方法与最大方差法和区域增长法进行比较,通过实例表明该方法的有效性。