Perceptual clustering for automatic hotspot detection from Ki-67-stained neuroendocrine tumour images

Hotspot detection plays a crucial role in grading of neuroendocrine tumours of the digestive system. Hotspots are often detected manually from Ki-67-stained images, a practice which is tedious, irreproducible and error prone. We report a new method to segment Ki-67-positive nuclei from Ki-67-stained slides of neuroendocrine tumours. The method combines minimal graph cuts along with the multistate difference of Gaussians to detect the individual cells from images of Ki-67-stained slides. It, then, automatically defines the composite function, which is used to determine hotspots in neuroendocrine tumour slide images. We combine modified particle swarm optimization with message passing clustering to mimic the thought process of the pathologist during hotspot detection in neuroendocrine tumour slide images. The proposed method was tested on 55 images of size 10 × 5 K and resulted in an accuracy of 94.60%. The developed methodology can also be part of the workflow for other diseases such as breast cancer and glioblastomas.