A survey for the automatic classification of bone tissue images

Abstract

In this chapter, a computer-assisted system aimed to assess the degree of regeneration of bone tissue from stem cells is built. We deal with phenotype and color analysis to describe a wide variety of microscopic biomedical images. Then we investigate several trained and non-parametric classifiers based on neural networks, decision trees, bayesian classifiers and association rules, whose effectiveness is analyzed to distinguish between bone and cartilage versus other existing types of tissue existing in our input biomedical images. The features selection includes texture, shape and color descriptors, among which we consider color histograms, Zernike moments and Fourier coefficients. Our study evaluates different selections for the feature vectors to compare accuracy and computational time as well as different stainings for revealing tissue properties. Overall, picrosirius reveals as the best staining and multilayer perceptron as the most effective classifier to distinguish between bone and cartilage tissue.