以阈值分割技术为基础，提出了基于3D直方图的生长法和尺度空间聚类方法。分别利用3D空间中同一类目标像素频度的连通性的尺度空间聚类原理，简单而有效地解决了多维阈值分割带来的过度分割问题。该方法对不同类型图象具有很好的适应性，并已成功应用于医学彩色图象处理领域。

Color image segmentation is essentially a clustering process in 3D color space, and color images can be considered as s special case of multi-spectral images. It is quite often when the objects cannot be extracted using three features but can be extracted using more than three features. In this paper, every pixel of an image is described using 5 features which are transformed from its own RGB features, then multi-dimensional thresholding(MDT) divide color space by thresholding each component histogram. This approach is equivalent to partitioning the multi-dimensional histogram into rectangular hyper-prisms. But MDT will lead oversegmentation in result. Hence, two-step algorithm is proposed to solve the problem of oversegmentation. The first step is region growing in 3D histogram by connectivity of frequency of neighbor bin in same class. The second step is data clustering based on scale space theory, which models the blurring effect of lateral retinal interconnerctions. The final algorithm that combines different approaches results in further improvement in performance. It is simple and effective to different color image, and has been used to segment the microscope medical image successfully.