# Using Imaging Techniques to Analyze the Microstructure of
## 2 Digital Camera Imaging
### AC Microstructure Analysis Using Digital Camera  
The simplicity, availability, ability to capture high-quality images, and low cost of digital cameras have motivated researchers to use them to analyze the internal structure of AC mixtures. By generating digital samples from optically scanned surface images of laboratory-prepared asphalt specimens, [PERSON] et al. [10] captured the microstructure of stone-based materials. Through image processing techniques, [PERSON] et al. [11] extracted valuable information regarding the distribution and size of pores within AC mixtures, as well as their connectivity. Moreover, the use of fractal analysis techniques can provide insight into the complexity and heterogeneity of crack patterns within AC mixtures [12]. Digital cameras have been used to acquire quantitative measurements of AC microstructure with consideration of the random nature of aggregate distribution on micro and macro scales [13; 14]. By using 2D digital images and DIP techniques, [PERSON] et al. [15] could estimate the aggregates' size, gradation, distribution, orientation, and other properties such as asphalt content (AC%), air void percent (AV%), and other properties of AC mixtures. Mineral aggregates compose more than 90% by volume of AC mixtures. Two types of mineral aggregates are used in AC mixtures: coarse aggregates and fine aggregates. Some coarse aggregate characteristics, related to the shape of aggregate particles, can be extracted from top and side views in digital images of free or loose aggregate samples [16; 17]. Aggregate gradation affects the microstructure of AC mixtures. Gradation of coarse aggregate can be accurately estimated from digital images of vertical and/or horizontal cross-sections of compacted AC samples [18]. Planner areas of aggregate particles in digital images are compared with the area of sieve openings to estimate the percentages of retained particles on each sieve. The estimated gradation from this technique is known as a planner gradation. To estimate the original gradation, some assumptions about the aggregate shapes have to be made and applied through shape correction factors. Coarse aggregate orientation and aggregate-size distributions can also be estimated from the same cross-section images using the scaled dimensions of the particles and the distances between them [19; 20]. Estimated aggregate-size distributions are generally biased because of the random cut of aggregates [21]. However, the measured dimensions can also be corrected to obtain accurate estimates [22]. One of the challenges in the cross-section images is the contact between two or more aggregate particles, where they can be recognized as one larger aggregate, see Figure 1. To separate these contacted aggregates, the watershed algorithm was the image processing [23]. Air void percentage, which is typically 4% to 6% of the volume of an AC mixture, affects the service level and service life of AC mixtures. Higher or lower air void content than optimum will adversely affect the performance of the AC mixture. Integrated models have been built to estimate volumetric indices from the correlations between the known air void content and the extracted parameters from digital images of AC mixture cross-sections, such as planner areas and dimensions of the coarse aggregates and the spaces between them. Cellular phone images and the DIP technique have been applied by [PERSON] et al. [13] to analyze AC microstructure and estimate volumetric indices. Air void content is influenced by traffic load and environmental conditions. After exposure to traffic loading, aggregate  
Figure 1: An example of contact between two aggregate particles. distribution and orientation change, which affects the existing air void distribution and adds more air voids and micro-cracks to the mix [24]. [PERSON] et al. [25] presented a methodology for efficient and reliable recognition of AC microstructure via image processing, which enables further numerical analysis.