Abstract
Recent studies indicated that small calcified particles observable by scanning electron microscopy (SEM) may initiate calcification in cardiovascular tissues. We hypothesized that if the calcified particles precede gross calcification observed in calcific aortic valve disease (CAVD), they would exhibit a regional asymmetric distribution associated with CAVD development, which always initiates at the base of aortic valve leaflets adjacent to the aortic outflow in a region known as the fibrosa. Testing this hypothesis required counting the calcified particles in histological sections of aortic valve leaflets. SEM images, however, do not provide high contrast between components within images, making the identification and quantification of particles buried within tissue extracellular matrix difficult. We designed a new unique pattern-matching based technique to allow for flexibility in recognizing particles by creating a gap zone in the detection criteria that decreased the influence of non-particle image clutter in determining whether a particle was identified. We developed this flexible pattern particle-labeling (FpPL) technique using synthetic test images and human carotid artery tissue sections. A conventional image particle counting method (preinstalled in ImageJ) did not properly recognize small calcified particles located in noisy images that include complex extracellular matrix structures and other commonly used pattern-matching methods failed to detect the wide variation in size, shape, and brightness exhibited by the particles. Comparative experiments with the ImageJ particle counting method demonstrated that our method detected significantly more (p < 2 × 10(-7)) particles than the conventional method with significantly fewer (p < 0.0003) false positives and false negatives (p < 0.0003). We then applied the FpPL technique to CAVD leaflets and showed a significant increase in detected particles in the fibrosa at the base of the leaflets (p < 0.0001), supporting our hypothesis. The outcomes of this study are twofold: (1) development of a new image analysis technique that can be adapted to a wide range of applications and (2) acquisition of new insight on potential early mediators of calcification in CAVD.