Abstract
Detecting, counting, and classifying various cell types in
images of human blood is important in many biomedical applications. However, these tasks can be very difficult due to
the wide range of biological variability and the resolution
limitations of many imaging modalities. This paper proposes a new approach to detecting, counting and classifying
white blood cell populations in holographic images, which
capitalizes on the fact that the variability in a mixture of
blood cells is constrained by physiology. The proposed approach is based on a probabilistic generative model that describes an image of a population of cells as the sum of atoms
from a convolutional dictionary of cell templates. The class
of each template is drawn from a prior distribution that
captures statistical information about blood cell mixtures.
The parameters of the prior distribution are learned from
a database of complete blood count results obtained from
patients, and the cell templates are learned from images
of purified cells from a single cell class using an extension of convolutional dictionary learning. Cell detection,
counting and classification is then done using an extension
of convolutional sparse coding that accounts for class proportion priors. This method has been successfully used to
detect, count and classify white blood cell populations in
holographic images of lysed blood obtained from 20 normal blood donors and 12 abnormal clinical blood discard
samples. The error from our method is under 6.8% for all
class populations, compared to errors of over 28.6% for all
other methods tested.