An improved algorithm based on boundary normal vector overlap for suspected pulmonary nodule extraction

作者：Luan Guo Xin^*; Wei Ying; Xue Ding Yu

来源：Journal of Northeastern University, 2010, 31(8): 1078-1081.

• Convex-concave feature
• Local shape
• Medical Image Processing
• Normal vector
• Pulmonary nodules

An improved boundary normal vector overlap algorithm based on local shape constraint and adaptively defined normal vector magnitude is proposed to detect suspected pulmonary nodules. The original region of interest(ROI) is segmented with adaptive thresholding method in pulmonary parenchyma, then the local convex-concave shape feature is gained for every pixel of initial ROI boundaries to satisfy the calculations of normal vector direction and adaptively defined normal vector magnitude of every convex pixel on the initial ROI boundaries. Overlapping the normal vectors, the locally maximum overlap can be chosen so as to detect the suspected round pulmonary nodules of different sizes. The restriction due to the local convex-concave shape feature should be taken into account before overlapping the normal vectors, thus simplifying the calculation of overlap. The adaptively defined normal vector magnitude is available to get rid of the limitation of fixed-size nodules as the result of detection. Experiment results indicated that the improved algorithm can detect suspected pulmonary nodules effectively and applicable to the nodules of any sizes.

• 出版日期2010
• 单位东北大学