Salient object detection method for breast tumor in ultrasound images based on absorbing Markov chain

Automatic detection of tumor in breast ultrasound (BUS) images is important for the subsequent image processing and has been researched for decades. However, there still lacks a robust method due to poor quality of BUS images.

To propose and test a salient object detection method for BUS images.

BUS image is preprocessed by an adaptively selective replacement and speckle reducing anisotropic diffusion (SRAD) algorithm. Then, the preprocessed image is segmented into super pixels by a simple linear iterative clustering (SLIC) algorithm to form a graph model, and the saliency of the nodes in the graph is calculated by using the absorbed time of absorbing Markov chain (AMC). Finally, the initial saliency map is optimized by the recurrent time of ergodic Markov chain (EMC) and a distance weighting formula.

Results of the proposed method were compared both qualitatively and quantitatively with two saliency detection models. It was observed that the proposed method outperformed the comparison models and yielded the highest Accuracy value (97.49% vs. 86.63% and 90.33%) using a dataset of 1000 BUS images.

After the adaptively selective replacement, AMC can effectively distinguish tumors from background by random walks.