Pulmonary nodule detection is a binary classification problem. The main objective is to classify nodule from the lung computed tomography (CT) images. The intra class variability is mainly due to the grey-level variance, texture differences and shape. The purpose of this study is to develop a novel nodule detection method which is based on Two-dimensional Principal Component Analysis (2DPCA). We extract the futures using 2DPCA from nodule candidate images. Nodule candidates are classified using threshold. The proposed method reduces False Positive (FP) rate. We tested the proposed algorithm by using Lung Imaging Database Consortium (LIDC) database of National Cancer Institute (NCI). The experimental results demonstrate the effectiveness and efficiency of the proposed method. The proposed method achieved 85.11% detection rate with 1.13 FPs per scan.
In this paper, we propose shape recovery method for measuring protrusions on LCD Color filter in TFT-LCD manufacturing process. We use 3-D Focus Measure operator to find focused points. Then we find the lens step that maximizes the sum of the Focus Measure. In order to reduce the computational complexity, we apply the successive focus measure update algorithm. The 3-D shape of the object can be easily estimated from the best-focused points. Experiments are conducted on both synthetic and real images to evaluate performance of the proposed algorithms. The experimental results show that our new method is faster than previous method.