Breast Cancer Diagnosis using SHG Laser Microscopy and Statistical Image Analysis

Abstract

Second-Harmonic Laser Microscopy promises to be a useful diagnostic modality for breast cancer. Statistical image analysis has provided key insights into the differences between images of normal, benign and malignant breast tissue. Spectral analysis of image features coupled with a support-vector machine classifier is demonstrated to accurately separate normal from tumour tissue. Further analysis of the tumour group using the multi-scale, multi-directional, steerable pyramid filter has revealed features that can be used to separate benign from malignant breast tissue. The classifier presented can serve as a prototype for devices developed to serve in a clinical setting.