To solve the problem that the current Computer Aided Diagnosis(CAD) based 2D schemes only processes the formation for each scanned image itself and ignors the relation between the images, moreover, to reduce the computational complexity of high-order technology in an actual project, a more effective algorithm to detect pulmonary lesions in CT scanned images based on the fast Three Dimension Principle Component Analysis (3DPCA) was presented. Firstly, the Higher-Order Singular Value Decomposition (HOSVD) was introduced to design the 3D PCA. Then, by choosing the feature points as the seed points, the region grow was used to obtain the whole suspected lesion. Finally, a fast decomposition algorithm was presented according to the feature of CT scanned images. The technique was tested against more than 500 CT images form 10 typical cases of Jilin Tumor Hospital. The results confirm the validity of technique as well as enhanced performance. Comparing with other algorithms, the ratio of detection is improved by 10%-21%, and the computation is reduced to 1/3 of the original algorithm. Obtained data show that the fast 3D PCA could excavate more information exiting in the successive CT images. Moreover, improved high order technology can be more effectively applied in actual projects.