Image segmentation is a classic problem in computer vision research and has become a hot topic in the field of image understanding.
Image segmentation is a classic problem in the field of image technology. Since the 1970s, it has attracted many researchers ’research enthusiasm and has made great efforts for it, and has proposed many image segmentation algorithms. The performance of these segmentation algorithms is evaluated using the relevant image segmentation quality measures. However, since the objective criterion for the success of the algorithm segmentation has not been solved so far, the evaluation of the image segmentation algorithm segmentation quality has become a topic of considerable research significance. There have been a few preliminary discussions on the methods of image segmentation evaluation, but there is still no good summary and organization. This is not only disproportionate to the current status of research and application of image segmentation technology, but also not conducive to the development of image segmentation technology.
Image segmentation is the first step of image analysis, the foundation of computer vision, an important part of image understanding, and one of the most difficult problems in image processing. The so-called image segmentation refers to dividing the image into several disjoint areas based on grayscale, color, spatial texture, geometric shape and other features, so that these features show consistency or similarity in the same area, and between different areas Shows a clear difference. Simply put, it is to separate the target from the background in an image. For grayscale images, pixels within the area generally have grayscale similarity, while grayscale discontinuities are generally at the boundary of the area. Regarding image segmentation technology, due to the importance and difficulty of the problem itself, the image segmentation problem has attracted a lot of researchers to make great efforts for it since the 1970s. Although so far, there is no universal and perfect method of image segmentation, but the general rule of image segmentation has basically reached consensus, and considerable research results and methods have been generated.
Image segmentation refers to finding the boundary of the region of interest (ROI) in the image, so that the pixels inside and outside the boundary have similar characteristics (intensity, texture, etc.). Medical image segmentation is the basis for other subsequent processing of medical images. The accurate segmentation of the target area in the image is of great significance for computer-aided diagnosis, surgical planning, 3D reconstruction of the target, and radiotherapy evaluation. In recent decades, with the continuous improvement of medical imaging equipment, medical image segmentation algorithms have also emerged in an endless stream, but they have rarely been widely used in clinical practice. Using a comprehensive medical image data set to objectively evaluate the medical image segmentation algorithm is a key step in advancing the algorithm to clinical application
Traditional image segmentation method Based on threshold image segmentation methodThreshold segmentation method is a traditional image segmentation method. Because of its simple implementation, small amount of calculation, and relatively stable performance, it has become the most basic and widely used segmentation technology in image segmentation. The basic principle of the threshold segmentation method is to divide the image pixels into several categories of target areas and background areas with different gray levels by setting different characteristic thresholds. It is particularly suitable for images where the target and the background occupy different gray scale ranges, and is currently widely used in the field of image processing. The selection of threshold is the key technology in image threshold segmentation.
Gray-scale threshold segmentation method is one of the most commonly used parallel region technology, and it is the most applied category in image segmentation. If the image only uses the two categories of target and background, then only one threshold needs to be selected. This segmentation method is called single threshold segmentation. Single threshold segmentation is actually the following transformation of the input image f to the output image g:
In the above expression, T is the threshold, the image element g (i, j) = 1 for the target object, and g (i, j) = 0 for the background image element. But if there are multiple targets in the image that need to be extracted, a single threshold segmentation will be wrong. It is necessary to select multiple thresholds to split each target. This segmentation method is called multi-threshold segmentation.
The result of threshold split depends on the choice of threshold. This shows that the key to the threshold segmentation algorithm is to determine the threshold. After the threshold value is determined, the threshold value is compared with the gray value of the pixel point and the division of each pixel is performed in parallel. Commonly used threshold selection methods are the peak-valley method using the image gray histogram, the minimum error method, the transition area method, the threshold method using the spatial position information of the pixel points, the threshold method combined with the connected information, and the maximum correlation Principle selection threshold and maximum entropy principle automatic threshold method.
Figure 1 is the result of using a single threshold method and a local threshold method to separate cell images. The results show that in many cases, the contrast between the target object and the background is not the same at different positions in the image. This is if a unified The single threshold of the target is separated from the background, and the effect is not ideal. If the image is segmented with different thresholds according to the local characteristics of the image, that is, local threshold segmentation, the effect is much better than single threshold segmentation.
The advantage of the threshold segmentation method is that the image segmentation is fast, the calculation is simple, and the efficiency is high. However, this method only considers the characteristics of the pixel gray value itself, generally does not consider the spatial characteristics, so it is more sensitive to noise. Although various improved algorithms based on threshold segmentation have appeared, the effect of image segmentation has improved, but there is still no good solution to the setting of the threshold. If intelligent genetic algorithm is applied to threshold filtering, select The threshold of image segmentation can be optimized, which may be the development trend of image segmentation method based on threshold segmentation.
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