Cyclotorsion measurement using scleral blood vessels.

BACKGROUND AND OBJECTIVES: Measurements of the cyclotorsional movement of the eye are crucial in refractive surgery procedures. The planned surgery pattern may vary substantially during an operation because of the position and eye movements of the patient. Since these factors affect the outcome of an operation, eye registration methods are applied in order to compensate for errors. While the majority of applications are based on features of the iris, we propose a registration method which uses scleral blood vessels. Unlike previous offline techniques, the proposed method is applicable during surgery. METHODS: The sensitivity of the proposed registration method is tested on an artificial benchmark dataset involving five eye models and 46,305 instances of eye images. The cyclotorsion angles of the dataset vary between -10° and +10° at 1° intervals. Repeated measurements and ANOVA and Cochran's Q tests are applied in order to determine the significance of the proposed method. Additionally, a pilot study is carried out using data obtained from a commercially available device. The real data are validated using manual marking by an expert. RESULTS AND CONCLUSIONS: The results confirm that the proposed method produces a smaller error rate (mean = 0.44 ± 0.41) compared to the existing method in [1] (mean = 0.64 ± 0.58). A further conclusion is that feature extraction algorithms affect the results of the proposed method. The SIFT (mean = 0.74 ± 0.78), SURF64 (mean = 0.56 ± 0.46), SURF128 (mean = 0.57 ± 0.48) and ASIFT (mean = 0.29 ± 0.25) feature extraction algorithms were examined; the ASIFT method was the most successful of these algorithms. Scleral blood vessels are observed to be useful as a feature extraction region due to their textural properties.

A weighted rule based method for predicting malignancy of pulmonary nodules by nodule characteristics.

Predicting malignancy of solitary pulmonary nodules from computer tomography scans is a difficult and important problem in the diagnosis of lung cancer. This paper investigates the contribution of nodule characteristics in the prediction of malignancy. Using data from Lung Image Database Consortium (LIDC) database, we propose a weighted rule based classification approach for predicting malignancy of pulmonary nodules. LIDC database contains CT scans of nodules and information about nodule characteristics evaluated by multiple annotators. In the first step of our method, votes for nodule characteristics are obtained from ensemble classifiers by using image features. In the second step, votes and rules obtained from radiologist evaluations are used by a weighted rule based method to predict malignancy. The rule based method is constructed by using radiologist evaluations on previous cases. Correlations between malignancy and other nodule characteristics and agreement ratio of radiologists are considered in rule evaluation. To handle the unbalanced nature of LIDC, ensemble classifiers and data balancing methods are used. The proposed approach is compared with the classification methods trained on image features. Classification accuracy, specificity and sensitivity of classifiers are measured. The experimental results show that using nodule characteristics for malignancy prediction can improve classification results.