Abstract
Intensity inhomogeneity and noise are two major parts in image segmentation. Aiming at these problems, this work proposes a novel hybrid active contour method which combines local and global statistical information into an improved signed pressure force (SPF) function. First, by considering the global information extracted from a region of interest, a new global-based SPF function is created that effectively adjusts the signs of the pressure force inside and outside the evolving curve. Second, a new local-based SPF function utilizes the normalized local intensity differences as the coefficients of local internal and external regions, which can segment complicated areas easily. Third, by combing the global-based SPF and the local-based SPF functions, an improved hybrid SPF function is constructed based on active contour approach. Experiments on many kinds of real and synthetic images show that our method makes superior segmentation accuracy and is more robust to initial contour and noises.
Highlights
Image segmentation is an important subject in related field of computer image analysis, image identification, object detection and other aspects [1]–[6]
The original image with red initial contours are displayed in the first column in each figure, and the corresponding results of ACM_LoG mode, ACM_LPF model, Chan–Vese model (C-V) model, SBGFRLS model, region-scalable fitting (RSF) model, ORACM model, Sun’s model and our method are shown in order from the second to last columns, respectively
From the segmentation result we find that the C-V, SBGFRLS and ORACM used the global statistical information of the images, they cannot address well the segmentation of multi-phase images
Summary
Image segmentation is an important subject in related field of computer image analysis, image identification, object detection and other aspects [1]–[6]. The aim of segmentation is to separate a given image into different non-intersecting regions with specific properties such as intensities, colors and textures. Due to the intensity inhomogeneity, noise, and obscured boundaries, it still makes a lot of errors in the process of image segmentation. Active contour methods based on evolution curves are wide application. Based on different image information, these models may be classified into edge-based [7]–[11] and region-based [12]–[16]
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