The scoring of mitotic figures is an integrated part of the Bloom and Richardson system for grading of invasive breast cancer. It is routinely done by pathologists by visual examination of hematoxylin and eosin (H&E) stained histology slides on a standard light microscope. As such, it is a tedious process prone to inter- and intra-observer variability. In the last decade, whole-slide imaging (WSI) has emerged as the “digital age” alternative to the classical microscope. The increasing acceptance of WSI in pathology labs has brought an interest in the application of automatic image analysis methods, with the goal of reducing or completely eliminating manual input to the analysis. In this paper, we present a method for automatic detection of mitotic figures in breast cancer histopathology images. The proposed method consists of two main components: candidate extraction and candidate classification. Candidate objects are extracted by image segmentation with the Chan-Vese level set method. The candidate classification component aims at classifying all extracted candidates as being a mitotic figure or a false object. A statistical classifier is trained with a number of features that describe the size, shape, color and texture of the candidate objects. The proposed detection procedure was developed using a set of 18 whole-slide images, with over 900 manually annotated mitotic figures, split into independent training and testing sets. The overall true positive rate on the testing set was 59.5% while achieving 4.2 false positives per one high power field (HPF).