基于形态学的分水岭算法牙科X射线图像分割.doc

英文原文Watershed Algorithm Based on Morphology for Dental X-Ray Images SegmentationAbstractA new watershed algorithm based on mathematical morphology, which can be applied to dental X-ray images segmentation, is proposed in the paper. In order to separate each tooth and improve the serious problem of over-segmentation of traditional watershed algorithm, we apply the processing based on morphology to get the images enhanced before watershed algorithm. First, using the top-hat-bottom-hat transformation to amplify the contrast of foreground and background and remove noises. And then erosion procedure is used to weaken the degree of adhesion between teeth. Hole-filling can help to eliminate some unnecessary split-line which can cause the phenomenon of wrong segmentation. Finally we apply the watershed algorithm using distance transformation of the binary image to get the segmentation result. From the experiment results, we can see that this algorithm can separate teeth accurately and overcome the over-segmentation efficiently compared with traditional method.Keywords-watershed algorithm; mathematical morphology; dental X-ray image; image segmentation; distance transformationI. INTRODUCTIONDental biometrics is an appropriate method to identify deceased individuals of disasters such as criminal cases, blasts and Tsunamis, because teeth with their dental works are very resistant to modest force effects and high temperature and chemical corrosion and also possess good biometric properties. And it also provides important information in dental medical treatment for further analysis to get the diagnostic. Image segmentation as a precursor has been seen as one of the most important aspect of the entire process like pattern recognition. It gives clarity information and the levels to subdivision depend on the problem being solved.Image segmentation as an indispensable method is applied to extract quantitative information of specific tissues, also as the premise and a step to realize the processing of visualization.And it is still a challenging problem in computer vision and image processing. Areas obtained by segmentation are both independent from each other. And all the areas belong to one region will share the special consistency. The objective of segmentation is to localize the region of each tooth in dental X- ray image 3. At present, image segmentation has been broadly divided into 3 categories: cluster analysis, edge detection and region extraction. Each of those methods has got disadvantages such as the failure to get the number of clusters before segmentation when applying cluster analysis.Watershed algorithm based on morphological theory is a kind of nonlinear segmentation. It possesses great qualities in positioning the edge rapidly and accurately and behaves excellent at detecting objects with weak edges etc. But traditional watershed algorithm has serious problem of over- segmentation under the influence of noise and the fine texture of the objects. A variety of approaches on how to overcome the split have been proposed for different purposes in different applications.Watershed Segmentation Algorithm based on morphology has been applied to cellular images, as in 1, aimed at reducing the over-segmentation. It requires fewer computation and simpler parameters and gets segmentation results more accurately compared with other tradition methods.In 2 and 3, Nomir and Adbel-Mottaleb introduced a fully automated segmentation technique. It starts by applying iterative thresholding followed by adaptive thresholding to segment the teeth from both the background and the bone areas. And after adaptive thresholding, horizontal integral projection followed by vertical integral projection are applied to separate each individual tooth. And this method can achieve the position of each tooth precisely.In 4, an algorithm based on wavelet transform (WT) to segment dental X-ray images was proposed. It contains three major steps: dental X-ray preparing, panoramic radiograph segmentation using wavelet transformation and enhancement image with morphological image processing. And the segmentation result using this method is better than thresholding segmentation and adaptive thresholding segmentation.In 7, a watershed segmentation algorithm based on grayscale morphological pretreating is presented. Opening operations are applied to remove small light details before applying watershed algorithm. Therefore the phenomenon of over-segmentation was controlled and the touching objects were segmented precisely.In this paper, we introduce a new method based on the performance of watershed algorithm and the characteristics of dental X-ray images. At first, we apply the bottom-hat -top-hat transformation to enhance the dental radiographs. Then we used the erosion algorithm to weaken the degree of adhesion between teeth and remove the noises. And the imfill() function could help to eliminate the possibility of over-segmentation caused by the upcoming processing. Finally we utilize the watershed algorithm using distance transform of the binary image to get the segmentation result.II. WATERSHED ALGORITHM BASED ON MORPHOLOGYA. Top-hat-bottom-hat transformationTop-hat transformation is defined as the difference of the original image minus the image applied with the operation of imopen to remove the some points with the highest gray value of the image. And the bottom-hat transform is the difference of the image applied with the operation of imclose to remove the points with smallest gray value and then minus the original image. The definitions are as follows: Top-hat transformation：Bottom-hat transformation:Where f is the original image and b represents the structure elements.The opening of f by b , denoted f b, isThe closing of f by b , denoted f b, isWhere is the operation of erosion and represents dilation.Opening operation is used to remove regions smaller than the structure elements with relative high gray values while the closing operation can remove the small regions with relative low gray values. And these two operations possess great quality in remaining all the levels of gray value and keeping the large regions with high values relatively unchanged. One of the important usages is that these two transformations are capable of correcting uneven illumination effects. Top-hat transform can be used in situation of bright objects against a dark background, while the bottom-hat transform is applied in the opposite situation.Top-hat transformation has some certain characteristics of high pass filter that it can be used to highlight the gray peak and enhance the edge information of the targets. And the bottom-hat transformation can be applied to get the valley of the gray value and prominent the boundaries between connected targets like teeth. Therefore, these two transformations can be used in combination to get the effect of image enhancement for the foreground and background gray are further stretched as well as the objectives and details are highlighted.In this paper we can get the image with its contrast effectively improved by adding the original image with the image applied with top-hat transformation and then minus the image applied with the bottom-hat transformation.After enhancement the gray image will be transformed into a binary image by using thresholding for the next processing.B. The morphological processing to the binary imageMathematical morphology is very useful as a tool to extract image components applied in the representation and description of the boundary, the bones and the convex hull. Erosion and dilation are the basic operations of morphology. This paper mainly uses the processing of erosion which actually means using structural elements to fill the image. Erosion would shrink or refinement the objections. The mode and degree are controlled by the structural elements. We apply the structural elements of b to fill the collection A . And we can assume that the collection A is eroded by b if A still contains the structure elements b after the fillings. It is defined as follows:Where b represents the structure elements.Difficulty exists in dental segmentation because of the adhesion between teeth. We could not get the position of each tooth precisely without delete the adhesion. Therefore, we need to apply the image with erosion operation to weaken the degree of adhesion between teeth for the benefit of segmentation.Hole-filling refers to the image contour filling. Usually the inner contour area should not be bigger than the maximum area of targets. There may be some holes which should not exist when the dental X-ray images are transformed into binary images under the influence of shape characteristics of tooth and the non-uniform gray distribution. And these holes may be transformed into little areas independent of each other after erosion. This would lead to the phenomenon of over-segment after applied watershed algorithm. Therefore, the structure elements b cannot be too large for the possibility of holes generated. And it also cannot be too small for the adhesion must be deleted completely. In this paper, we apply the operation of erosion for two times and during these two procedures add the operation of imfill() to avoid the generation of over-segmentation caused by holes.C. Watershed segmentation algorithm using distance transformation of binary imageDistance transformation turns the binary image into a gray image. And the value of location ( x, y )is the distance of pixel to its nearest background pixel. It aims to distinguish the boundary pixels and the inner pixels.Watershed algorithm based on morphological theory is first proposed by S. Beucher and L. Vincent and developed rapidly in image segmentation field in recent years 6- 7. In geography, a watershed refers to a dam. The river systems in the area on either side of dam have different directions. We apply these concepts to the gray scale image processing system to solve the issues of segmentation. We need to regard the gray scale image as a topological surface. And f ( x , y )can be viewed as the height. Water always flows to the relatively low areas and would stop at the local Low-lyings called catchment basins. Eventually all the water will be divided in different water basin. And we call the dam watershed. It has the same possibility for water to flow to different catchment basins. The purpose to apply this idea to the gray image segmentation is to get all the water catchment basins and watersheds.Watershed algorithm behaves well in positioning the edge rapidly and it is capable of detecting weak edges of the targets. But it also suffers from the serious problem of over- segmentation. The proposed methods of improving this phenomenon can be divided into two categories: preprocessing such as enhancement and markers and processing applied after watershed segmentation algorithm like merging the small regions. In this paper, we take the preprocessing to improve the phenomenon of over-segmentation.III. EXPERIMENTS AND DISCUSSIONFigure 3(a) shows the original dental X-ray image. And (b) shows the result after enhancement. Apparently, the objectives (teeth) and details are highlighted obviously and the contrast of foreground and background (soft tissues) are amplified. And we get the binary image of dental X-ray image in (c). And it is evident that adhesion between each tooth is weakened after the first erosion in (d). The structure element b of this processing cannot be too big for the chance of over-segment happens. (e) shows the result of applying the function of imfill() to the image. And some holes are filled to avoid the phenomenon of over-segment caused by the second erosion. And after two erosions the adhesion are clearly eliminated as showed in (f). The spilt lines we get after applying the proposed algorithm shows in (g). From (h) that is the original image with the segment line and we can see that this algorithm can satisfactory, successfully segment each tooth with over-segmentation effectively improved.The image applied with classical watershed algorithm using distance transform is presented in Figure 4. Compared with Figure 3(c) we can see that part of those teeth cannot showed in Figure 4(a) without enhancement and this could lead to the incorrect split lines such as the one of the middle tooth. And From Figure 4(b) and (c) we can see that we cannot get the proper contour information just applying the classical watershed algorithm because the touching teeth belong to one area not isolate areas with their adhesion.Figure 5 and figure 6 show another two examples used the proposed algorithm. From the results showed in these figures we can see that the of upper jaw or to the lower jaw are segmented precisely and accurately. But the lines we have got splitting each tooth still need to be improved. And this phenomenon like Figure 5(b) shows can be explained by the structure elements of b used to delete the adhesion between teeth. The erosion cannot avoid to the reduction of the teeth areas cannot be avoided by implementing the operation of erosion. IV. CONCLUSIONS AND FUTURE WORKFor the purpose of separate each tooth, this paper presents an algorithm of watershed transformation based on morphology and distance transform. According to the results of the experiments, this algorithm can improve the phenomenon of over-segmentation and split each tooth effectively. But the results of segmentation are not perfect and it takes time to adjust the parameters like the structure elements b .Our future work will concentrate on choosing an auto- adaptive capability of the structural elements. 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