A Coarse-to-Fine Morphological Approach With Knowledge-Based Rules and Self-Adapting Correction For Lung Nodules Segmentation

A light field (LF) image is composed of multiple sub-aperture images (SAIs) from slightly offset viewpoints.To denoise a noise-corrupted LF image, leveraging recent development on deep algorithm unfolding, we pursue a hybrid graph-model-based / data-driven approach.Specifically, we first connect each pixel in a target patch of an SAI to neighboring pixels within the patch, and to pixels in co-located "similar" patches in adjacent SAIs.Given graph connectivity, we formulate a maximum a posteriori (MAP) problem using graph total variation (GTV) as signal prior. We then unroll the iterations of a corresponding optimization algorithm into a sequence of neural layers. in each unrolled layer, we learn relevant features per pixel from data using a convolutional neural net (CNN) in a supervised manner, so that edge weights can be computed as functions of feature distances.Each neural layer can be interpreted as a graph low-pass filter for a 4D LF image patch. Experiments show that our proposal outperformed two model-based and two deep-learning-based implementations in numerical and visual comparisons.