The detection of solitary pulmonary nodules (SPN) is proven to be of critical importance in early stage lung cancer diagnosis. Aiming at reducing the false positive regions caused by the dot enhancement filter which is sensitive to the lung nodules

a new recognition method based on calculation of three dimensional (3D) enhancement density index and decision rule is proposed. An adaptive bilateral filter is applied to reduce the noisy and smooth CT image sequences. Then

the pre enhancement coefficients and volume of interest (VOI) are obtained by computing the Hessian matrix and corresponding eigenvalues. After analyzing the distribution of pre enhancement coefficients

3D enhancement density index is constructed. Finally

a decision rule is adopted to identify nodule candidates. The proposed method is tested on two lung CT image sets. The experimental results illustrate the efficiency of the proposed algorithm.