Aiming to overcome the problem that fuzzy or even inaccurate results will be concluded when evaluating the applicability of Dempster's combination rule while the bodies of evidence contain some non-singleton evidences which basic probability assignments have larger differences between any two bodies of evidence, a modified pignistic probability distance was proposed to describe the relevance between bodies of evidence. And then, combining the modified pignistic probability distance with the classical conflict coefficient, a new method of evaluating the applicability of Dempster's combination rule was presented. In the proposed method, a new conflict coefficient was defined to measure the conflict between bodies of evidence. The new conflict coefficient was consistent with the modified pignistic probability distance when the classical conflict coefficient was zero, and it was consistent with an average value of the modified pignistic probability distance and the classical conflict coefficient when the classical conflict coefficient was not zero. The results of the numerical analysis examples demonstrate that compared with the evaluating method based on the pignistic probability distance, the proposed method based on the improved pignistic probability distance can provide more applicable and reasonable evaluating results of the applicability of the Dempster's combination rule.

A new method was proposed to accurately detect and quantitatively evaluate the lung nodule spiculation. First, the region growing method followed by level set method was used to accurately segment the main part of the lung nodule. Then, spiculated lines connected to the nodule boundary were extracted using a line detector in polar coordinates system. Finally, spiculation index was introduced as the quantitative measurement of spiculation features, which was then used as a criteria for distinguishing between spiculated and non-spiculated nodules. The consistency and correlation of spiculation index of the method and Lung Image Database Consortium (LIDC) were evaluated in detail. The experimental results show that the proposed method can effectively detect and quantitatively describe the lung nodule spiculation in CT images.