Accurate detection and classification of stained cells in microscopy images enable quantitative measurements of
cell distributions and spatial structures, and are crucial for developing new analysis tools for medical studies and
applications such as cancer diagnosis and treatment. In this paper, we present a learning based approach for
identifying different types of cells in multi-spectral microscopy images of tumor-draining lymph nodes (TDLNs)
and locating their centroid positions. With our approach, a set of features based on the eigenvalues of the
Hessian matrix is constructed for each image pixel to determine whether the local shape is elliptic. The elliptic
features are then used together with the intensity-based ring scores as the feature set for the supervised learning
method. Using this new feature set, a random forest based classifier is trained from a set of training samples
of different cell types. In order to overcome the difficulties of classifying cells with varying stain qualities, sizes,
and shapes, we build a large set of prior training data from a variety of tissue sections. To deal with the issue
of multiple overlapping cell nuclei in images, we propose to utilize the spikes of the outer medial axis of the cells
to detect and detach the touching cells. As a result, the centroid position of each identified cell is pinpointed.
The experimental data show that our proposed method achieves higher recognition rates than previous methods,
reducing significantly the human interaction effort involved in previous cell classification work.