Studies on quantitative analysis and automatic recognition of cell types of lung cancer.

Recognition of lung cancer cells is very important to the clinical diagnosis of lung cancer. In this paper we present a novel method to extract the structure characteristics of lung cancer cells and automatically recognize their types. Firstly soft mathematical morphology methods are used to enhance the grayscale image, to improve the definition of images, and to eliminate most of disturbance, noise and information of subordinate images, so the contour of target lung cancer cell and biological shape characteristic parameters can be extracted accurately. Then the minimum distance classifier is introduced to realize the automatic recognition of different types of lung cancer cells. A software system named "CANCER.LUNG" is established to demonstrate the efficiency of this method. The clinical experiments show that this method can accurately and objectively recognize the type of lung cancer cells, which can significantly improve the pathology research on the pathological changes of lung cancer and clinical assistant diagnoses.

Three-dimensional reconstruction and quantitative analysis of the brain stem nuclei based on fast centroid auto-registration.

This paper introduces a three-dimensional (3D) reconstruction algorithm of the brain stem nuclei based on fast centroid auto-registration. The research is based on methods and theories of computer stereo vision, and by image information processing three-point pattern local search, registration and auto-tracing for the centroids of the brain stem nuclei were accomplished. We adopt two-peak threshold, edge detection and grayscale image enhancement to extract contours of the nuclei's structures. The experimental results obtain the spatial structure information and 3D image of the brain stem nuclei, show spatial relationship between 14 pairs of nuclei, and quantitate morphological parameters of each type of nuclei's 3D structure. This work is significant to neuroanatomy research and clinic applications. Furthermore, a software system named BRAIN.HUK is established.

Automatic quantitative analysis of bipolysaccharide liposome by image processing method.

This paper established the extracting method and algorithm for liposome images on the basis of analyzing the form feature of liposome, and its corresponding automatic analysis software. Some algorithms, such as the logical algorithms of mathematical morphology, the grayscale-gradient co-occurrence matrix-based method and the image enhancement method of grayscale image, were used to restrain the noise effectively and increase the definition of image. The result on bipolysaccharide liposome indicates that the method increases the measuring speed and precision.