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 recognition of small cell carcinoma based on the self-organizing neural network.

In this paper the recognition of Small Cell Carcinoma (SCC) is studied. For each type we select 128 samples for training, and randomly measure 200 cells in each sample. We introduce multi-scale morphology based on centroid coordinates to extract the boundaries of nuclei and obtain feature images of nuclei. The features of lung cancer cells are described by morphological and colorimetrical parameters, which is valuable to recognize SCC. Then the architecture of self-organizing feature mapping (SOFM) neural network is studied for recognition of SCC. The weights of the network are adjusted by self-organizing competition, and finally inputted patterns are classified. This algorithm has the advantage of parallelism and fast-convergence, and may simplify the analysis of SCC. Clinical experiment results show that the correctness ratio of this system may reach 95.3% while recognizing lung cancer cell types. Our work is significant to the pathological researches of lung cancer, assistant clinic diagnosis, and assessment of therapeutic effects. Meanwhile a software system named as SCC. LUNG is established for automatic analysis.

Automatic quantitative analysis of ore content of human tooth enamel based on image processing.

Introducing the theory of fuzzy set, mathematical morphology and computerized mask fast scanning, we developed the TOOTH.SCA software and method to analyze the effect of fluoride (NaF) on ore content of human tooth enamel automatically and quantitatively. And we obtained some characteristic parameters, such as the depth, the type and the demineralized content of every scathing layer of dental caries. The smallest scale of mask scanning is 0.1 microm x 0.1 microm and the time required to analyze a sample is only 12 s. The applied software and method we built play an important role to the research on the mechanism of pathological changes of teeth and preventing dental caries.

Recent advances in the pathogenesis of lupus nephritis: autoantibodies and B cells.

Although many factors contribute to the clinical presentation and subsequent course of individuals with lupus nephritis, the formation of glomerular immune deposits is typically one of the initial events. In general, breakdown in immunologic tolerance leads to the production of autoreactive B and T cells that, either through direct infiltration and/or their secretory products, initiate inflammation. Immune deposition within glomeruli results in complement activation and recruitment of inflammatory cells, along with activation of endogenous renal cells. This inflammatory cascade leads to secretion of cytokines and chemokines, which in turn attract more infiltrating cells. Up-regulation of lymphoid-derived chemokines further enhance the cellular influx, augmenting inflammation and resulting in further tissue damage. The degree of inflammation is determined by the extent of this invasion along with both the systemic and local responses to the assault. This review focuses mainly on the contributions of pathogenic autoantibodies, autoreactive B cells to lupus nephritis, and potential immunologic therapies for lupus nephritis. Manipulation of both the cells and soluble mediators that initiate and perpetuate the disease are essential to suppressing autoreactivity and inflammation and preventing disease progression.