Coronary artery trees (CATs) are often extracted to aid the fully automatic analysis of coronary artery disease on coronary computed tomography angiography (CCTA) images. Automatically extracted CATs often miss some arteries or include wrong extractions which require manual corrections before performing successive steps. For analyzing a large number of datasets, a manual quality check of the extraction results is time-consuming. This paper presents a method to automatically calculate quality scores for extracted CATs in terms of clinical significance of the extracted arteries and the completeness of the extracted CAT. Both right dominant (RD) and left dominant (LD) anatomical statistical models are generated and exploited in developing the quality score. To automatically determine which model should be used, a dominance type detection method is also designed. Experiments are performed on the automatically extracted and manually refined CATs from 42 datasets to evaluate the proposed quality score. In 39 (92.9%) cases, the proposed method is able to measure the quality of the manually refined CATs with higher scores than the automatically extracted CATs. In a 100-point scale system, the average scores for automatically and manually refined CATs are 82.0 (±15.8) and 88.9 (±5.4) respectively. The proposed quality score will assist the automatic processing of the CAT extractions for large cohorts which contain both RD and LD cases. To the best of our knowledge, this is the first time that a general quality score for an extracted CAT is presented.
Intravascular optical coherence tomography (IVOCT) is an imaging technique that is used to analyze the underlying cause of cardiovascular disease. Because a catheter is used during imaging, the intensities can be affected by the catheter position. This work aims to analyze the effect of the catheter position on IVOCT image intensities and to propose a compensation method to minimize this effect in order to improve the visualization and the automatic analysis of IVOCT images. The effect of catheter position is modeled with respect to the distance between the catheter and the arterial wall (distance-dependent factor) and the incident angle onto the arterial wall (angle-dependent factor). A light transmission model incorporating both factors is introduced. On the basis of this model, the interaction effect of both factors is estimated with a hierarchical multivariant linear regression model. Statistical analysis shows that IVOCT intensities are significantly affected by both factors with p<0.001, as either aspect increases the intensity decreases. This effect differs for different pullbacks. The regression results were used to compensate for this effect. Experiments show that the proposed compensation method can improve the performance of the automatic bioresorbable vascular scaffold strut detection.
Imaging mass spectrometry is a technique to determine of which materials a small, physical sample is made.
Current feature extraction techniques fail to extract certain small, high resolution characteristics from these
multi-spectral datacubes. Causes are a low signal-to-noise ratio, the presence of dominant but uninteresting
features, and the huge amount of variables in the dataset. In this paper, we present a zooming technique based on principal component analysis (PCA) to select regions
in a datacube for enhanced feature extraction at the highest possible resolution. It enables the selection of
spectral and spatial regions at a low resolution and recursively apply PCA to zoom in on interesting, correlated
features. This approach is not based on complex and data-specific denoising algorithms. Moreover, it decreases
execution time when additional filters have to be applied.
The technique utilizes a higher signal-to-noise ratio in the data, without losing the high resolution characteristics.
Less interesting and/or dominating features can be excluded in the spectral and spatial dimension. For
these reasons, more features can be distinguished and in greater detail. Analysts can zoom into a feature of
interest by increasing the resolution.