Accurately segmenting and quantifying pulmonary nodule structure is a key issue in three-dimensional (3-D) computer-aided diagnosis (CAD) schemes. This paper presents a nodule segmentation method from 3-D thoracic CT images based on a deformable surface model. In this method, first, a statistical analysis of the observed intensity is performed to measure differences between the nodule and other regions. Based on this analysis, the boundary and region information are represented by boundary and region likelihood, respectively. Second, an initial surface in the nodule is manually set. Finally, the deformable surface model moves the initial surface so that the surface provides high boundary likelihood and high posterior segmentation probability with respect to the nodule. For the purpose, the deformable surface model integrates the boundary and region information. This integration makes it possible to cope with inappropriate position or size of an initial surface in the nodule. Using the practical 3-D thoracic CT images, we demonstrate the effectiveness of the proposed method.

This paper presents a method to analyze volumetrically evolutions of pulmonary nodules for discrimination between malignant and benign nodules. Our method consists of four steps; (1) The 3-D rigid registration of the two successive 3-D thoracic CT images, (2) the 3-D affine registration of the two successive region-of-interest (ROI) images, (3) non-rigid registration between local volumetric ROIs, and (4) analysis of the local displacement field between successive temporal images. In the preliminary study, the method was applied to the successive 3-D thoracic images of two pulmonary nodules including a metastasis malignant nodule and a inflammatory benign nodule to quantify evolutions of the pulmonary nodules and their surrounding structures. The time intervals between successive 3-D thoracic images for the benign and malignant cases were 150 and 30 days, respectively. From the display of the displacement fields and the contrasted image by the vector field operator based on the Jacobian, it was observed that the benign case reduced in the volume and the surrounding structure was involved into the nodule. It was also observed that the malignant case expanded in the volume. These experimental results indicate that our method is a promising tool to quantify how the lesions evolve their volume and surrounding structures.

In this paper, we present a computer-aided diagnosis (CAD) system to automatically detect lung cancer candidates at an early stage using a present and a past helical CT screening. We have developed a slice matching algorithm that can automatically match the slice images of a past CT scan to those of a present CT scan in order to detect changes in the lung fields over time. The slice matching algorithm consists of two main process: the process of extraction of the lungs, heart, and descending aorta and the process of matching slices of the present and past CT images using the information of the lungs, heart, and descending aorta. To evaluate the performance of this algorithm, we applied it to 50 subjects (total of 150 scans) screened between 1993 and 1998. From these scans, we selected 100 pairs for evaluation (each pair consisted of scans for the same subject). The algorithm correctly matched 88 out of the 100 pairs. The slice images for the present and past CT scans are displayed in parallel on the CRT monitor. Feature measurements of the suspicious regions are shown on the relevant images to facilitate identification of changes in size, shape, and intensity. The experimental results indicate that the CAD system can be effectively used in clinical practice to increase the speed and accuracy of routine diagnosis.