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