In 2012, more than 1.6 million new cases of breast cancer were diagnosed and about half a million women died of breast cancer. The incidence has increased in the developing world. The mortality, however, has decreased. This is thought to partly be the result of advances in diagnosis and treatment. Studying tissue samples from biopsies through a microscope is an important part of diagnosing breast cancer. Recent techniques include camera-equipped microscopes and whole slide scanning systems that allow for digital high-throughput scanning of tissue samples. The introduction of digital pathology has simplified parts of the analysis, but manual interpretation of tissue slides is still labor intensive and costly, and involves the risk for human errors and inconsistency. Digital image analysis has been proposed as an alternative approach that can assist the pathologist in making an accurate diagnosis by providing additional automatic, fast and reproducible analyses. This thesis addresses the automation of conventional analyses of tissue, stained for biomarkers specific for the diagnosis of breast cancer, with the purpose of complementing the role of the pathologist. In order to quantify biomarker expression, extraction and classification of sub-cellular structures are needed. This thesis presents a method that allows for robust and fast segmentation of cell nuclei meeting the need for methods that are accurate despite large biological variations and variations in staining. The method is inspired by sparse coding and is based on dictionaries of local image patches. It is implemented in a tool for quantifying biomarker expression of various sub-cellular structures in whole slide images. Also presented are two methods for classifying the sub-cellular localization of staining patterns, in an attempt to automate the validation of antibody specificity, an important task within the process of antibody generation.  In addition, this thesis explores methods for evaluation of multimodal data. Algorithms for registering consecutive tissue sections stained for different biomarkers are evaluated, both in terms of registration accuracy and deformation of local structures. A novel region-growing segmentation method for multimodal data is also presented. In conclusion, this thesis presents computerized image analysis methods and tools of potential value for digital pathology applications.

The purpose of this report is to compare seven different methods for biomarker detection and quantification based on previously published papers. The methods investigated are ELISA, LC-MS/MS, UPLC-MS/MS, LC-IM/MS, IA-LC-MS/MS, MSIA-HR/AM, HTRF and AlphaLISA ® . The focus lies on biomarkers relevant for diabetes, obesity and cardiovascular diseases.Namely insulin, proinsulin, glucagon and C-peptide. Particular significance is assigned to the comparison of the currently widest used method, ELISA, with various types of LC-MS/MS. The report concludes ELISA being superior to LC-MS/MS methods in terms of recovery and precision, while LC-MS/MS is superior in accuracy, multiplexing, specificity, throughput and sample cost. This suggests that different types of LC-MS/MS has the potential to gain momentum in the field of biomarker quantification if they become more available.