Mass spectrometry (MS) has become a versatile and powerful tool in proteomics for the analysis of complex biological systems. Unlike the common MS techniques the more recent imaging mass spectrometry (IMS) preserves the spatial distribution inherent in tissue samples. IMS data consist of tens of thousands of spectra measured over a large range of masses, the variables. Each spectrum arises from a grid point on the surface of a tissue section. Motivated by the requirements in cancer research to differentiate cell populations and tissue types of such data accurately and efficiently, we consider two approaches -- normalisation and feature extraction -- and we illustrate these approaches on IMS data obtained from tissue sections of patients with ovarian cancer. In proteomics, normalisation refers to the process of scaling spectra in order to correct for artefacts occurring during data acquisition. Normalising the mass spectra is essential in IMS for an interpretation of the data. We propose a new and efficient normalisation of the mass spectra, based on peak intensity correction (PIC), and illustrate its effect for individual mass images and cluster maps. The selection of mass variables which distinguish cancer tissue from non-cancerous tissue regions -- or responders from non-responders -- is an important step towards identification of biomarkers. We consider a combined cluster analysis and feature extraction approach for derived binary mass data. This approach exploits the difference in proportions of occurrence (DIPPS) statistic of subsets of data in the selection and ranking of variables. We apply these ideas to the cancer and non-cancerous regions of the tissue sections, and we summarise the `best’ variables in a single image which has a natural interpretation.