Visually searching for analyzable metaphase chromosome cells under microscopes is a routine and time-consuming task in genetic laboratories to diagnose cancer and genetic disorders. To improve detection efficiency, consistency, and accuracy, we developed an automated microscopic image scanning system using a 100X oil immersion objective lens to acquire images that has sufficient spatial resolution allowing clinicians to do diagnosis. Due to the high-resolution, the field of image depth is very limited and multiple scans up to seven layers are required. Thus, a metaphase cell can spread over multiple images at different focal levels. Among them only one or two are adequate for the diagnosis and the others are typically fuzzy images. In this study, we developed and tested a computer-aided detection (CAD) scheme to automatically select one image with the sharpest image quality and discard all of the other fuzzy images based on the computed sharpness index. From three scanned bone marrow specimen slides, the on-line and offline metaphase finding modules automatically selected 100 chromosome cells with 534 images. These images were selected to build a testing dataset. For each cell, the CAD scheme selects one image with the maximum sharpness index. Three observers also independently visually selected one best image for diagnosis from each cell. The agreement rate between CAD and visually selected images ranges from 89% to 96%, which is also very comparable to the agreement rate between the two observers. This experiment demonstrated the feasibility of applying a CAD scheme to select the images with sharpest high-resolution metaphase chromosome cell and potentially improve diagnostic efficiency and accuracy in the future clinical practice.