Local Region Sparse Learning for Image-on-Scalar Regression
This addresses the challenge of image analysis for public health by enabling efficient ROI identification, though it appears incremental as it builds on existing regularization techniques.
The authors tackled the problem of identifying disease-related regions of interest in images by proposing a novel penalty for image-on-scalar regression that enforces sparsity and spatial contiguity, resulting in a method that scales to large images and outperforms state-of-the-art approaches in experiments.
Identification of regions of interest (ROI) associated with certain disease has a great impact on public health. Imposing sparsity of pixel values and extracting active regions simultaneously greatly complicate the image analysis. We address these challenges by introducing a novel region-selection penalty in the framework of image-on-scalar regression. Our penalty combines the Smoothly Clipped Absolute Deviation (SCAD) regularization, enforcing sparsity, and the SCAD of total variation (TV) regularization, enforcing spatial contiguity, into one group, which segments contiguous spatial regions against zero-valued background. Efficient algorithm is based on the alternative direction method of multipliers (ADMM) which decomposes the non-convex problem into two iterative optimization problems with explicit solutions. Another virtue of the proposed method is that a divide and conquer learning algorithm is developed, thereby allowing scaling to large images. Several examples are presented and the experimental results are compared with other state-of-the-art approaches.