Detection and classification of cells in histological images is a challenging task because of the large intra-class variation in the visual appearance of various types of biological cells. In this paper, we propose a discriminative dictionary learning paradigm, termed as Cell Words, for modelling the visual appearance of cells which includes colour, shape, texture and context in a unified manner. The proposed framework is capable of distinguishing mitotic cells from non-mitotic cells (apoptotic, necrotic, epithelial) in breast histology images with high accuracy.
The proposed paradigm for learning the visual appearance of cells.
The proposed method has been evaluated on the publicly available MITOS dataset for mitosis detections in breast cancer histopathology images.
Learned discriminative dictionaries between mitotic and non-miotic cells. Mitotic cell words in mitotic dictionary are representative of various phases of cell mitosis.
Classification Performance based on the Learned Dictionary.
Please contact Nasir Rajpoot for the source code of Cell Words.
Cell words: Modelling the visual appearance of cells in histopathology images
K. Sirinukunwattana , A.M. Khan, and N. M. Rajpoot,
Computerized Medical Imaging and Graphics, Elsevier
doi: 10.1016/j.compmedimag.2014.11.008 [link]