Identification and classification of cells in multispectral microscopy images of lymph nodes

Abstract

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.