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In ophthalmology, aligning images in indocyanine green and fluorescein angiograph sequences is important for the treatment of subretinal lesions. This paper introduces an algorithm that is tailored to align jointly in a common reference space all the images in an angiogram sequence containing both modalities. To overcome the issues of low image contrast and low signal-to-noise ratio for late-phase images, the structural similarity between two images is enhanced using Gabor wavelet transform. Image pairs are pairwise registered and the transformations are simultaneously and globally adjusted for a mutually consistent joint alignment. The joint registration process is incremental and the success depends on the correctness of matches from the pairwise registration. To safeguard the joint process, our system performs the consistency test to exclude incorrect pairwise results automatically to ensure correct matches as more images are jointly aligned. Our dataset consists of 60 sequences of polypoidal choroidal vasculopathy collected by the EVEREST Study Group. On average, each sequence contains 20 images. Our algorithm successfully pairwise registered $95.04\%$ of all image pairs, and joint registered $98.7\%$ of all images, with an average alignment error of 1.58 pixels.

Data mining is traditionally adopted to retrieve and analyze knowledge from large amounts of data. Private or confidential data may be sanitized or suppressed before it is shared or published in public. Privacy preserving data mining (PPDM) has thus become an important issue in recent years. The most general way of PPDM is to sanitize the database to hide the sensitive information. In this paper, a novel hiding-missing-artificial utility (HMAU) algorithm is proposed to hide sensitive itemsets through transaction deletion. The transaction with the maximal ratio of sensitive to nonsensitive one is thus selected to be entirely deleted. Three side effects of hiding failures, missing itemsets, and artificial itemsets are considered to evaluate whether the transactions are required to be deleted for hiding sensitive itemsets. Three weights are also assigned as the importance to three factors, which can be set according to the requirement of users. Experiments are then conducted to show the performance of the proposed algorithm in execution time, number of deleted transactions, and number of side effects.