Liver Lesion Detection Based on Two-Stage Saliency Model with Modified Sparse Autoencoder
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Liver lesion detection is an important task for diagnosis and surgical planning of focal liver disease. The large numbers of images in routine liver CT studies, in addition to their high diversity in appearance, have been hurdles for detecting all lesions by visual inspection. Automated methods for lesion identification are desirable, but the results of current approaches are limited due to the diversity of the training sets and the extensive tuning of parameters. In this paper, we propose a novel saliency model for lesion detection in CT images. First, we segment the image into multi-scale patch sizes. Then, a two-stage saliency model is proposed to detect liver lesions. In the first stage, we calculate the gray level contrast saliency map based on a prior knowledge to reduce the influence of blood vessels in CT images. In the second stage, we propose a modified sparse autoencoder (SAE) with neighbourhood information to learn discriminative features directly from raw patch features and adopt Locality-constrained Linear Coding (LLC) method to encode the obtained discriminative features of each patch. Then the second saliency map is calculated based on feature uniqueness and spatial distribution of patches. Followed by an appropriate mapping fusion, the liver lesions can be detected well. With \(7\times 7\) sized patches, a 120 visual word dictionary, and 14 feature dimension, our model achieved 90.81% accuracy for lesion detection.
We appreciate valuable suggestions by Prof. Daniel Rubin and Dr. Assaf Hoogi in Stanford University.
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