A Microscopic Image Classification Method Using Shearlet Transform

This paper presents a method for representation and classification of microscopic tissue images using the shear let transform. The objective is to automatically process biopsy tissue images and assist pathologists in analyzing carcinoma cells, e.g. differentiating between benign and malignant cells in breast tissues. Compared with wavelet filters such as the Gabor filter, shear let has inherent directional sensitivity which makes it suitable for characterizing small contours of carcinoma cells. By applying a multi-scale decomposition, the shear let transform captures visual information provided by edges detected at different orientations and multiple scales. Based on our approach, each image is represented using the discrete shear let coefficients and histograms of shear let coefficients and then used for classification of benign versus malignant tissue images using Support Vector Machines. Our experiments on a publically available database of hystopathological images of human breast shows that our fully automatic approach yields in good classification rates and less complexity compared to other methods.