Automatic Nuclei Detection based on Generalized Laplacian of Gaussian Filters.

Research paper by Hongming H Xu, Cheng C Lu, Richard R Berendt, Naresh N Jha, Mrinal M Mandal

Indexed on: 29 Mar '16Published on: 29 Mar '16Published in: IEEE journal of biomedical and health informatics


Efficient and accurate detection of cell nuclei is an important step towards automatic analysis in histopathology. In this work, we present an automatic technique based on generalized Laplacian of Gaussian (gLoG) filter for nuclei detection in digitized histological images. The proposed technique first generates a bank of gLoG kernels with different scales and orientations, and then performs convolution between directional gLoG kernels and the candidate image to obtain a set of response maps. The local maxima of response maps are detected and clustered into different groups by mean-shift algorithm based on their geometrical closeness. The point which has the maximum response in each group is finally selected as the nucleus seed. Experimental results on two datasets show that the proposed technique provides a superior performance in nuclei detection compared to existing techniques.