Adaptive filtering and hypothesis testing: Application to cancerous cells detection
We propose a new method to detect cells in microscopic imagery, the problem under study being the analysis of cancerous cells experiencing metastasis i.e. cells susceptible to migration. This work would help medical researchers to study the evolution of a cancer. The peculiar nature of the images due to the acquisition protocol causes some difficulties. These are resolved through tailored preprocessing involving correction of uneven illumination and enhancement of cellular information. Detection and counting of cells are performed by our proposed filtering that provokes peaks in its convolution space wherever cells are present. We compare our counting results with those provided by human experts and with a Hough transform developed for similar purposes. The validity of the cell segmentation from the peaks is then established by a statistical test of the closeness of the segmented cell to a cell model.
adaptive filtering microscopic imagery maximum likelihood test object detection image preprocessing