Spreadsheets are the most prominent example of end-user programs. Unfortunately, spreadsheets often contain faults. In this paper, we propose a static analysis approach that can be used in a variety of spreadsheet quality assurance techniques. Our approach automatically detects groups of related formula cells and input cells, computation blocks, and the headers of the input and formula cells for a given spreadsheet. In the empirical evaluation, we show that 99% of the blocks of the investigated spreadsheets and more than 76% of the headers can be detected. Furthermore, we explain how the results of the static analysis approach can be used to improve existing spreadsheet smells and we propose four new spreadsheet smells.