An Efficient Rule-Hiding Method for Privacy Preserving in Transactional Databases

One of the obstacles in using data mining techniques such as association rules is the risk of leakage of sensitive data after the data is released to the public. Therefore, a trade-off between the data privacy and data mining is of a great importance and must be managed carefully. In this study an efficient algorithm is introduced for preserving the privacy of association rules according to distortion-based method, in which the sensitive association rules are hidden through deletion and reinsertion of items in the database. In this algorithm, in order to reduce the side effects on non-sensitive rules, the item correlation between sensitive and non-sensitive rules is calculated and the item with the minimum influence in non-sensitive rules is selected as the victim item. To reduce the distortion degree on data and preservation of data quality, transactions with highest number of sensitive items are selected for modification. The results show that the proposed algorithm has a better performance in the non-dense real database having less side effects and less data loss compared to its performance in dense real database. Further the results are far better in synthetic databases in compared to real databases

A brief review on vessel extraction and tracking methods

Extracting an accurate skeletal representation of coronary arteries is an important step for subsequent analysis of angiography images such as image registration and 3D reconstruction of the arterial tree. This step is usually performed by enhancing vessel-like objects in the image, in order to differentiate between blood vessels and background, followed by applying the thinning algorithm to obtain the final output. Another approach is direct extraction of centerline points using exploratory tracing algorithm preceded by a seed point detection schema to provide a set of reliable starting points for the tracing algorithm. A large number of methods fall in these two approaches and this paper aims to contrast them through a brief review of their innate characteristics, associated limitations and current challenges and issues

Automatic selection of initial points for exploratory vessel tracing in fluoroscopic images

Automatic extraction of vessel centerlines has been an essential process in most of the image guided diagnosis and therapy applications. Among a considerable number of methods, direct exploratory tracing method is known to be an efficient solution for reliable extraction of vessel features from two-dimensional fluoroscopic images. The first step of most automatic exploratory tracing algorithms is collecting a number of candidate initial seed points and their initial tracing directions. To detect reliable initial points, a validation step is required to filter out the false candidates and avoid unnecessary tracing. Staring from reliable initial points, the algorithm efficiently extracts the centerline points along the initial direction until certain pre-defined criteria are satisfied. However, most of these algorithms suffer from incomplete results due to inappropriate selection of the initial seed points. The conventional seed point selection algorithms either rely merely on signal-to-noise ratio analysis, which results in a large number of false traces, or impose a set of strict geometrical validation rules that lead to more false negatives and require more computation time. This paper presents a new method for efficient selection of initial points for exploratory tracing algorithms. The proposed method improves the performance upon existing methods by employing a combination of geometrical and intensity-based approaches

Improving performance of automated coronary arterial tree center-line extraction, stent localization and tracking

Stent placement is a common procedure in Percutaneous Transluminal Coronary Angioplasty which helps many patients to avoid emergency heart bypass surgery or heart attack. In this procedure, a stent is implanted at the narrowing part of an artery to keep its lumen open allowing blood to flow normally through the artery. A potential risk of this treatment is the inaccurate placement of the stent (geographic miss), which could result in serious complications for the patient such as development of new stenotic lesions, increasing the likelihood of blood clot formation and the need for revascularization. Over the last decade, many algorithms have been developed to address this problem. However, most of them either fail to meet the demanding requirements of real-time assistant systems or to provide quantitative analysis for erification of stent placement task. In this research work, we report on new contributions to the major parts of a computer assisted stent positioning system. The first contribution is automatic detection of seed points which serve as a prerequisite step for centerline extraction algorithm. The solution consists of an algorithm for automatic collection of candidate seed points using efficient grid line searching mechanism and a validation method which uses local geometric and intensity based features as effective validation rules to discriminate between the actual seed point and false alarms. The experimental results show that combining the advantages of the geometric based validation and contrast based filtering as well as avoiding large quantization errors, lead to significant enhancement in the performance of the seed point detection algorithm in terms of balancing between the precision and recall. The second contribution is related to the robust and accurate extraction of centerlines for all vessel segments of the arterial tree in the angiogram images. This problem is addressed by proposing an accurate and robust centerline extraction method. Starting at each detected seed point, the centerline extraction method utilizes eigenvalues and eigenvectors of Hessian matrix for the pixels located on a semi-circular scanning profile for robust estimation of the next centerline point. The experimental validations show that the use of Hessian matrix results in significant improvement in the robustness of the tracing algorithm. The stent localization and tracking in angiogram image sequence is the topic of the third contribution. The proposed method combines special fast filtering, region of interest processing and graph based trajectory analysis approach to localize and track the radioopaque markers of the stent in fluoroscopic frame sequences. The most interesting finding was that the validation of the potential markers prior to building tracks of marker pairs, causes the landmark detection process to avoid dealing with a large number of outliers and misdetections. In total, the current study found that the proposed algorithms outperform their well-established existing counterparts indicating their suitability to be adopted in practical computer assisted stent positioning systems

Coronary Artery Center-Line Extraction Using Second Order Local Features

Of interest is the accurate and robust delineation of vessel center-lines for complete arterial tree structure in coronary angiograms which is an imperative step towards 3D reconstruction of coronary tree and feature-based registration of multiple view angiograms. Most existing center-line tracking methods encounter limitations in coping with abrupt variations in local artery direction and sudden changes of lumen diameter that occur in the vicinity of arterial lesions. This paper presents an improved center-line tracing algorithm for automatic extraction of coronary arterial tree based on robust local features. The algorithm employs an improved scanning schema based on eigenvalues of Hessian matrix for reliable identification of true vessel points as well as an adaptive look-ahead distance schema for calculating the magnitude of scanning profile. In addition to a huge variety of clinical examples, a well-established vessel simulation tool was used to create several synthetic angiograms for objective comparison and performance evaluation. The experimental results on the accuracy and robustness of the proposed algorithm and its counterparts under difficult situations such as poor image quality and complicated vessel geometry are presented