Multi-level feature fusion for nucleus detection in histology images using correlation filters

Nucleus detection is an important step for the analysis of histology images in the field of computational pathology. Pathologists use quantitative nuclear morphology for better cancer grading and prognostication. The nucleus detection becomes very challenging because of the large morphological variations across different types of nuclei, nuclei clutter, and heterogeneity. To address these challenges, we aim to improve the nucleus detection using multi-level feature fusion based on discriminative correlation filters. The proposed algorithm employs multiple features pool, based on varying features combinations. Early fusion is employed to integrate multi-feature information within a pool and inter-pool fusion is proposed to fuse information across multiple pools. Inter-pool consistency is proposed to find the pools which are consistent and complement each other to improve performance. For this purpose, the relative standard deviation is used as an inter-pool consistency measure. Pool robustness to noise is also estimated using relative standard deviation as a robustness measure. High-level pool fusion is proposed using inter-pool consistency and pool-robustness scores. The proposed algorithm facilitates a robust and reliable appearance model for nucleus detection. The proposed algorithm is evaluated on three publicly available datasets and compared with several existing state-of-the-art methods. Our proposed algorithm has consistently outperformed existing methods on a wide range of experiments.