This study is part of the research of improving early detection of breast cancer in screening mammograms by focusing on computerized analysis and detection of cancers missed by radiologists. It is directed to the analysis of breast density in missed cancer cases and the effect of tissue density on cancer detection. A total of 100 missed cancer cases were collected which were used to generate three different datasets including mammograms with missed cancer, mammograms with screening-detected cancer and normal mammograms. A statistical-based method was applied to segment the breast density tissue. The percentage of the segmented density tissue area out of the whole breast area is calculated as the index of breast density. A set of tests was applied to examine (1) the differences in density between the mammograms at the detected stage and that at missed stage, (2) the density difference between the normal mammograms and the cancerous mammograms; (3) the effect of breast density on CAD cancer detection. The results demonstrate that (1) no significant difference in breast density between the detected and missed stages; (2) the density of cancerous mammograms is significantly higher than normal mammograms; (3) similar to mammogram screening by radiologists, the lesions occurred in dense breasts are more likely to be missed in CAD detection especially at their early stage.