Building a multispectral image dataset for colorectal tumor biopsy

Abstract

Automated grading of tumor cells proves to be a great way of enhancing the rapidity and accuracy of cancer diagnostic procedures. The application of image processing and machine learning techniques on the digitized biopsy slides enables the discrimination between various cell types. Apart from using the normal RGB/grayscale imaging, multispectral images tend to provide a wide range of information that can support the classification tasks. Besides using the visible range, wavelength bands in infrared ranges can be utilized in multispectral imaging. This paper presents our multispectral image acquisition system to develop a database for the colorectal biopsy slides. The dataset comprise images in both visible and near infrared spectrum for the 4 major categories of colon cells. A preprocessing algorithm with automatic estimation of parameters for adaptive histogram equalization is also presented. With the acquired database, our preliminary experiment involving a basic feature extraction and classification algorithm yielded satisfactory results.