Visual identification of coronary arterial lesion from three-dimensional coronary computed tomography angiography (CTA) remains challenging. We aimed to develop a robust automated algorithm for computer detection of coronary artery lesions by machine learning techniques. A structured learning technique is proposed to detect all coronary arterial lesions with stenosis ≥25%. Our algorithm consists of two stages: (1) two independent base decisions indicating the existence of lesions in each arterial segment and (b) the final decision made by combining the base decisions. One of the base decisions is the support vector machine (SVM) based learning algorithm, which divides each artery into small volume patches and integrates several quantitative geometric and shape features for arterial lesions in each small volume patch by SVM algorithm. The other base decision is the formula-based analytic method. The final decision in the first stage applies SVM-based decision fusion to combine the two base decisions in the second stage. The proposed algorithm was applied to 42 CTA patient datasets, acquired with dual-source CT, where 21 datasets had 45 lesions with stenosis ≥25%. Visual identification of lesions with stenosis ≥25% by three expert readers, using consensus reading, was considered as a reference standard. Our method performed with high sensitivity (93%), specificity (95%), and accuracy (94%), with receiver operator characteristic area under the curve of 0.94. The proposed algorithm shows promising results in the automated detection of obstructive and nonobstructive lesions from CTA.
In general, it is necessary for Multi-view Video Coding (MVC) methods to compress multi-view videos efficiently and
have a property of view-scalability in order to decode arbitrary views according to any viewer's interests. Much research
has been done on MVC methods, with the goal of increasing coding efficiency. Although these previous methods have
considered the property of view-scalability, a lot of coding bits and delays were necessary to decode arbitrary views. In
this paper, we propose an MVC method based on image stitching. We generated a stitched reference and encoded multiview
sequences using disparity-compensated method. The proposed method is able to reduce delays during the decoding
stage. Experimental results show that the proposed MVC method increased the PSNR by 1.5~2.0dB and saved 10% of
the coding bits compared to simulcast coding.