Pulmonary Nodules Detection in CT Scanned Images Based on Optimization Region Growing and MCSVMs

Abstract

Lung CT images tumor automatic recognition method combined parameter optimization of regional growth with matMCSVMs (Matrix-mode within-class scatter support vector machine) was proposed to reduce the influence of lung CT image segmentation and support vector (SVM) identification in computer aided the tumor detection. Firstly, in view of the main problems existing in the traditional region growing, such that the initial seed point was too sensitive to select and the growth constraint criterion was difficult to determine, 2D PCA (2D principal component analysis) method was put forward. Secondly, a new growth criteria based on watershed range diagram was also designed for image segmentation. And then, aiming at the current problem that it was hard to compute the decision vector iterative operation in the matMCSVMs, a new method with a preprocessing conjugate gradient descent was used to optimize solution process and improve the classifier identification accuracy. The experimental results show that the methods are effective.