Over the years, many Automated Image Collection Systems (AICS) have been developed to capture pavement images. The cameras used by most of the AICS are based on Charge-Coupled Device (CCD) image sensors where visible ray is projected. However, the quality of the images captured by the CCD cameras was limited by the inconsistent illumination and shadows caused by sunlight. To enhance the CCD image quality, a high-power artificial lighting system has been used, which required a complicated strobe lighting system and a significant power source. Recently, laser lighting system was introduced which captured images without a shadow under an invisible laser frequency. Laser images were very sharp and did not present any shadow but, due to a high contrast of pavement surface, it resulted in a higher amount of background noises. This paper presents a new automated crack measurement and classification algorithm, which suppresses background noises, enhances crack images and determines crack types.