Achieving the optimal size distribution and fragmentation of blasted muck-pile has an important role in controlling mining processes and mineral processing as well as reducing production costs, including: transportation, loading, crushing, and milling. In order to optimize the production process and to have rapid and reliable evaluation of size distribution and rock fragmentation, image processing method is one of the most common methods. Despite of numerous advantages of this method, the associated inherent limitations and errors affect precision, accuracy, and reproducibility of measured results. Understanding such limitations and errors thus decreasing their effectiveness will improve the results of image processing. Current study, while introducing different types of image processing errors, analyzes errors of inappropriate imaging angle (perspective) and suggests procedures to decrease this error. Results of studies on 240 digital images of blasted rocks, showed an average of 97% accuracy and standard deviation of 4. 5% in eliminating perspective errors. Also, it was found that when the distance factor is between 0. 8 and 1. 5 the results are more reliable. Moreover, with a smaller particle size, optimal results can be achieved when the distance factor is reduced to a value between 0. 2 and 0. 4. Furthermore, when data is normally distributed, the frequency of distance factor in the range of 0. 5 to 1. 5 with the average of 1. 48 has a range of frequency error with an average of 3%. Comparing sieve size distribution curve of muck-piles with the size distribution curve of images taken before and after perspective error elimination using Split Desktop software, showed that after perspective error elimination, mean value of size distribution error depending on the amount of perspective distortion, were decreased from 5 to 25 %.