| Lung cancer is the most common malignant tumor and one of the lowest livability tumors after diagnosis as is known so far. It is increasing annually and now the first cause of cancer-related mortality in cities. In order to improve the survival rate of lung cancer patients, detection in the early stages has a significantly more hopeful prognosis and is the key treatment.The dissertation focuses on extracted features of Shape of fractal and Anisotropic Fractal from nodules of the earlier period stage Lung cancer, mian aim is able to quantitative analysis the disease types of nodules.1. Nodules rough Classification Base one the fractal theory , propose the method of enhancement the low -contrast of ROI region and the coefficient as a criterion for discrimination is found based on a new kind of weighted differential box-counting algorithm. In orde to segmentation a region of ROI image by combined the OTSU method and morphological , then to be extracted SPN from pulmonary parenchyma.2. Shape fractal features extraction of noules Lobulation and Burr play a significant role in distinction between benign and malignant Solitary Pulmonary Nodules diagnosis. And benign and malignant lung nodules differ in lobulation and burr features. In this chapter, features of malignant lung nodules are analyzed based on shape of fractal theory.3. Texture features extraction of nodules Anisotropic Fractal is used to analyze and extract the texture features of lung nodules. This method can compute different dimensions in different directions.4. Quantitative analysis the disease types of nodules The experiment indicates that fractal features are effective on reflecting the texture and edge shape of lung nodules. Further more, more feature information can be gained from multifractal than Isotropic fractal. And this method can be applied to any texture and shape images. Then Mahalanobis discriminant analysis is used to classify nodules,the discriminant scores are analyzed using Shannnon entrop method. |
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