Abstract
Image segmentation is a crucial role towards clinical diagnosis and therapy planning due to the existence of abundant noise, blurry boundaries and heterogeneity. In this work, a novel matrix factorization based approach with the ability of edge preservation is presented. Firstly, to obtain more comprehensive feature description, we use the local spectral histograms to describe the local structures formed by feature values. Secondly, the energy function is established via matrix factorization theory, which makes each pixel fall into the sub-region with the largest coverage area in its neighborhood. Then, the edge preservation is used to obtain a smoother and more accurate object boundary. Finally, a number of synthetic and natural images are performed for verification. Experiments demonstrated that our approach achieves satisfactory results and has more robust against the complex background than other methods.
Highlights
Over the past decades, many image segmentation algorithms, including wavelet transformation [1,2], graph cut [3,4], edge detection [5,6], level set [7,8], deep learning [9,10], have been presented
According to the nature of constraints, active contour models (ACMs) can be approximately categorized into two types: edge-based models [11,12,13,14] and region-based models [1518]
MATRIX FACTORIZATION BASED FITTING ENERGY COMBINING EDGE PRESERVATION After the filtered image is computed, we present a modified energy function by employing ACM for two-phase segmentation
Summary
Many image segmentation algorithms, including wavelet transformation [1,2], graph cut [3,4], edge detection [5,6], level set [7,8], deep learning [9,10], have been presented. Active contour models (ACMs) based on level set theory have become a successful branch. According to the nature of constraints, ACMs can be approximately categorized into two types: edge-based models [11,12,13,14] and region-based models [1518]. Edge-based methods rely on local edge information to evolve contour curves towards the target boundaries. Different from edge-based models, regionbased models use region statistical information to guide the motion of contours. They do not depend on the image gradients and can segment the objects with poor edges. As one of the most representative region-based ACMs, C-V [19] model utilizes the statistical information to guide the contour. Supposing that image intensities are statistically homogeneous in each region, it is extremely idealistic and cannot provide accurately segment with intensity inhomogeneity
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