Pancreatic Endoscopic Ultrasound Computer-aided Diagnosis System

Pancreatic cancer is a major disease which seriously threatens human health. The early detection and diagnosis are problems which have long plagued the medical profession. In current diagnostic methods, the endoscopic ultrasonography(EUS) have greater diagnostic value over pancreatic cancer. However, the diagnosis based on EUS images is affected considerably by the doctors' experience and subjective factors, and the cytology examination is invasive. In this paper, we focus on the research and basic development of the computer aided diagnosis(CAD) system of pancreatic EUS images, in order to create a variety of objective, quantitative diagnostic indices and an appropriate method to describe and understand the EUS images, and finally, increasing the accuracy of early diagnosis of pancreatic cancer through EUS.The proposed methods are composed of four parts:textural feature extraction of the pancreatic EUS images, feature selection, classification and evaluation system and the software development. Study and improvement on each part have been done for specific problems.As for the textural feature extraction, textural features, which have been widely used in different papers for textural segmentation and classification, are introduced into the area of pancreatic EUS image classification. 74 texture features of 9 categories are extracted, and good classification results are achieved using these features. Further work has been done on the study and improvement of tradition fractal features. By modifying the M-band wavelet transform fractal feature based on the fractal dimension(Lee,2003), the multi-fractal dimension was presented with the feature selection to obtain the multi-fractal feature vector of M-band wavelet transform. Experimental results showed that the classification based on the proposed fractal feature outperformed those based on the traditional fractal feature in both executing time and classifying accuracy.In the part of feature selection, the kernel distance measure is proposed as a new type of class separability, considering the poor performance of traditional class separability on linearly non-separable data set. The distance of samples from two classes is measured in the kernel space, and used to evaluate the separability of subsets. The proposed method embodies the advantages of kernel-based methods, especially on small sample set and linearly non-separable data set. Meanwhile, the kernel distance measure changes with the kernel parameter monotonously, so that it is 4 easy to eliminate the influence of kernel parameter. Compared with kernel scatter matrix measure(Wang,2008), the proposed method is much faster in running time and retains the advantages of kernel-based methods as well.According to the clinical requirements of classification and evaluation system, the fuzzy pattern recognition based on generalized membership function is proposed to realize the classification and evaluation system of pancreatic EUS images. The features are made fuzzy to increase the robustness. The training samples are used to construct a reasonable objective function and item of punishment. The unknown parameters of generalized membership function is estimated through optimization, and finally the classification and evaluation is implemented by the evaluation function.Based on the above research on algorithm, the Computer Aided Diagnosis Software System is developed. The proposed feature extraction and classification algorithm is programmed using standard C++ language in the environment of Visual Studio. The different function mode of browsing, CAD, zooming, measuring and database connecting is realized through the technology of MFC, GDI+, ODBC, etc, to facilitate-medical clinical use.