Abstract
Selective image segmentation is one of the most significant subjects in medical imaging and real-world applications. We present a robust selective segmentation model based on local spatial distance utilizing a dual-level set variational formulation in this study. Our concept tries to partition all objects using a global level set function and the selected item using a different level set function (local). Our model combines the marker distance function, edge detection, local spatial distance, and active contour without edges into one. The new model is robust to noise and gives better performance for images having intensity in-homogeneity (background and foreground). Moreover, we observed that the proposed model captures objects which do not have uniform features. The experimental results show that our model is robust to noise and works better than the other existing models.
Highlights
Image segmentation plays an important role in image processing and computer vision both
To introduce our new selective and global segmentation model, for segmenting the object of interest and all objects in a given image having intensity in-homogeneity, first we shortly summarize some selective segmentation models related to this work
We suggest to use H1ζ or H2ζ because δ3ζ may not be good for the case where the features of interest is lower than two pixels aside from other features, the problem will be resolved if we adjust ζ while we lose the automatic efficiency
Summary
Image segmentation plays an important role in image processing and computer vision both. The region-based models use image intensities to guide the motion of active contours, while the edge-based models use edge information for guiding the active contours towards the object boundary These models could segment all the features in a given image, which means that these are global segmentation models. For successful segmentation and fast convergence, Nguyen et al [20] combined the geometrical constraints and the Split Bregman method [21] This model works properly if the object is smooth and well described by the weighted shortest boundary length. We propose a variational model, which ensures better performance than the state-of-the-art models [31]–[33] for in-homogeneous, objects with the same/different intensities, in-homogeneous background, inhomogeneous foreground, and noisy images.
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