The diagnostic rules of peripheral lung cancer preliminary study based on data mining technique

Objective： To discuss the clinical and imaging diagnostic rules of peripheral lung cancer by data mining technique, and to explore new ideas in the diagnosis of peripheral lung cancer, and to obtain early-stage technology and knowledge support of computer-aided detecting （CAD）. Methods： 58 cases of peripheral lung cancer confirmed by clinical pathology were collected. The data were imported into the database after the standardization of the clinical and CT findings attributes were identified. The data was studied comparatively based on Association Rules （AR） of the knowledge discovery process and the Rough Set （RS） reduction algorithm and Genetic Algorithm（GA） of the generic data analysis tool （ROSETTA）, respectively. Results： The genetic classification algorithm of ROSETTA generates 5 000 or so diagnosis rules. The RS reduction algorithm of Johnson＇s Algorithm generates 51 diagnosis rules and the AR algorithm generates 123 diagnosis rules. Three data mining methods basically consider gender, age, cough, location, lobulation sign, shape, ground-glass density attributes as the main basis for the diagnosis of peripheral lung cancer. Conclusion： These diagnosis rules for peripheral lung cancer with three data mining technology is same as clinical diagnostic rules, and these rules also can be used to build the knowledge base of expert system. This study demonstrated the potential values of data mining technology in clinical imaging diagnosis and differential diagnosis.

A Tongue-images Segmentation Method Based on Local Restoration and Watershed Algorithm

Objective: To propose a method to segment tongue- images efficiently, and extract tongue- body accurately and quickly. Methods: Firstly, a kind of color- images' pre- processing technique was used to solve tongue- surface reflection problem. Neighbouring and similar region's information was used to restore the region with tongue- surface reflection problem by replacement. Secondly, the restored image was transformed into a gray one, and then processed by mathematical morphological operation- dilation to get a closed- loop edge. The third technique used was watershed algorithm, which is an usual tool in image segmentation. 'Watershed' function of matlab software was used to complete this algorithm. After that, region- combination technique was used. Through measuring neighbourship and similarity of regions, a non- objective and non- background region was merged into one of its neighbouring regions. This step was repeated until only two regions, objective and background regions, were left in the image. At last, corresponding to the merged image, tongue- body image was got from the original image. Results: 316 images were randomly taken from the image library for experiments, and 299 images were correctly segmented, so, the successful ratio is 94.62%. On the other hand, average time of running this method was about 50 s under whole sampling environment. Conclusion: The method presented in this paper can segment a tongue- body image from its original one effectively, and thus laying a good foundation for the following analysis work.

A Three-Dimensional Case System Model for Programming Course

This research aims to construct a case resource library for programming course,which can be used for either teachers’teaching or students’learning.The cases cannot be simply piled up but rather require a systematic planning.The solution to this is to design a case system model.The outcome-based education(OBE)concept is adopted to guide the research,and a three-dimensional case system model matching the course objectives is designed.Under the guidance of the model,the case resource library construction is more planned.Cases based on the model can provide all-round support for the cultivation of students’ability by gradually promoting knowledge and technology,frequently exercising one’s abilities,as well as expanding diverse and innovative problems.