An approach to extract edge maps in infrared based breast images using inverse Perona-Malik diffusion filter

Abstract

Infrared thermography is the adjunctive tool for early diagnosis of breast cancer. The infrared breast images are of low Signal to Noise Ratio (SNR) and amorphous in nature which makes analysis a challenging task. In this work, an attempt is made to extract the edge map from infrared breast images using inverse Perona-Malik (PM) model. This non-linear filter varies the diffusion near the interferences and edges using inverse gradient and new nearest-neighbour scheme. The edge maps are extracted for various gradient thresholds. The statistical features such as average gradient, contrast, entropy and variance are extracted from the edge map to find the optimal gradient threshold. The diffused image of optimal gradient threshold value is validated using SNR. Results show that the statistical features are found to have maximum value for the gradient threshold of 5. It is also observed that SNR obtained from the diffused image is improved by 30 dB compared to raw image. Inverse PM model is found to reduce the noise and enhance the edges. The integration of denoising and edge map extraction would result in accurate segmentation and aid for early diagnosis.