ICSE03

ICSE03

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  NEW APPLICATIONS OFWATERSHED SALIENCY TECHNIQUE TO SOLVE OVER-SEGMENTATION PROBLEM IN MEDICAL IMAGING Muhannad Al-Hasan, Mark Fisher, Moe Razaz School of Computing Sciences, University of East Anglia, Norwich, England E-mail: [email protected], [email protected],ac.uk, [email protected] Keywords: Watershed, Watershed segmentation, over- segmentation, watershed algorithms, medical imaging. Abstract This paper aims to present some practical problems arising from the watershed technique and address these by revisiting the concept of watershed saliency based on a graph of the mosaic image. Additionally, some new ideas and techniques have been included to suppress over-segmentation. Firstly we present a review of the watershed transform in the context of a morphological segmentation. We then introduce the idea behind a mosaic image, derived from an immersion process or flooding simulation analogy. A weighted watershed algorithm is introduced to overcome the problem of over-segmentation in a standard watershed transform, and some new application results are briefly presented and discussed. 1 Introduction Image segmentation is a key problem in image processing and is particularly important in medical imaging used for pre- operative planning. Segmentation is the process of spatially partitioning the pixels in any given image into homogeneous classes with respect to some property such as intensity or texture (texture refers to a pattern of closely placed elements in such a manner that the pattern somehow repeats itself). A good segmentation may be recognised from the characteristics of its output components: each component should be spatially cohesive as well as spatially accurate while different components should be dissimilar [2]. In the medical field, advances in imaging modalities such as CT and MRI have enabled multidimensional visualisation of the internal structures inside the human body. Visualisation of a particular organ requires its extraction from the image using segmentation techniques. There are many segmentation techniques, the simplest of which is thresholding; however, in many applications, choosing an accurate thresholding level is usually difficult. The watershed is a popular segmentation technique in the field of mathematical morphology [1]. In order to understand the watershed, it is necessary to consider the image as a surface, where high pixel values correspond to peaks and low pixel values correspond to valleys. Just as with actual watersheds, if a drop of water were to fall on any point of the contour it would find its way to lower ground until it reaches a local minimum. These local minima are referred to as catchment basins, and all points that drain into the same catchment basin are referred to as members of the same watershed [3]. Several techniques have been used for constructing watersheds, see for example [1,3]. 2 Watershed Segmentation Mathematical morphology provides a powerful approach for segmentation through techniques based on the watershed transform. The transform is usually applied to gradient images in order to find the crest lines between the different regional minima in the image. Although this technique was developed in 1979, it is only recently that the transform has become a popular tool for segmentation. An analysis of various techniques used in watershed segmentation has been presented in [5] by Hagyard and Razaz. In its most general form, the watershed technique can be applied to any image; however, in many applications the method produces over- segmentation of the objects in the image and their background. Marker images are then used in conjunction with the watershed transform to segment an image. Over-segmentation is particularly problematic in the segmentation of medical image data into anatomical structures, which is an important step for many medical imaging applications that are currently under development. Watershed segmentation derives its name from the manner in which the algorithm segments regions of an image into “catchment basins” corresponding to local minima. The regions surrounding these basins share boundaries with one another as shown in Figure 1. The output of the watershed algorithm is then a hierarchy of basins, which can be viewed at different scale levels. What remains is for the human to determine which basins and at what scale levels represent the anatomical structure or structures of interest. The input to the watershed algorithm is critical to the quality of its output image. The watershed algorithm expects a “height image” as input, which is defined in this context as an image where higher image intensities correspond to logical boundaries between regions of interest. The user needs to provide this input image to the
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  algorithm in orderto highlight the important image features for a given application. Thus, pre-processing the input image to produce a “height image” is a critical step, often ignored by some researchers in this field. Figure 1: Catchment basins and watershed lines in watershed segmentation. 3 Hierarchical Segmentation In some cases, the markers selection and extraction are not so easy. Some pictures may be very noisy and image processing becomes more and more complex. The mosaic image has been proposed as a possible marker-less solution to the over- segmentation problem [4]. 3.1 Suppression of Over-segmentation Although the standard watershed algorithm produces an over- segmented image, the contours in the image appear to be correct. The main problem is how to choose the “right” contours. The way we chose to tackle this problem is to by using a mosaic image, which is achieved by post processing the watershed algorithm proposed by Vincent and Soille [3]. 3.2 The Mosaic Image Consider a greyscale image f as in Figure 3(a), and its morphological gradient image g(f) shown in Figure 3(b). Let W(g) be the watershed transformation of g. Each catchment basin produced by the transform is associated with a minimum mi of the gradient g. A new function f / , the mosaic image of f, is defined by colouring each catchment basin with its minimum pixel value. The mosaic image created in this way is shown in Figure 3(c). A region adjacency graph (RAG) as in Figure 2 is then derived from the mosaic image, where the vertices of the directed graph correspond to the catchment basins and the (weighted) edges represent the saliency of watershed lines. Post-processing of this graph can reduce over-segmentation. Figure 2: Planar graph, adding vertices corresponding to the primary catchment basins to provide an intermediary connection between the neighbouring vertices of the non planar graph. 3.3 Weighted Watershed Algorithm A weighted watershed segmentation (WWS) algorithm was developed which effectively overcomes the problem of over-scgmentation. A morphological gradient image, i.e. the difference between the dilated and eroded input image is first generated. This is equivalent to finding the difference between the largest and the smallest value of the neighbours of each pixel. To this a flooding simulation algorithm is then applied to separate the image into its constituent catchment regions. A mosaic image is subsequently generated by labelling each of the catchment regions with the colour of its lowest point of catchment. This tessellated image is used to generate weighted watershed lines. The weight of each line (edge) is proportional to the difference in colour between adjacent regions. Thus the catchment regions associated with homogeneous or background will not be deep and so will have very weak watershed lines. The corresponding nodes then are merged. But watershed lines separating objects from background will have much stronger weights as they separate catchment regions with a big colour difference. This process results in watershed lines that are related to the sharpness of the boundaries and hence results in significant reduction of over-segmentation. Thresholding the weighted watershed image will remove the irrelevant segmentation lines, and will also even out the colouration of the lines around a single object. 4 Experimental Results We present some typical segmentation results using the WWS algorithm. Figure 4a shows a dental X-ray image. The image has already had some morphological operations applied to it, specifically an opening to remove some black, i.e. high intensity scratches. The bright white areas are
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  fillings in thepatient’s teeth. These fillings have a variety of shapes and can ‘blend’ into the areas on the image. This image was first dilated and eroded and the results were subtracted to produce a morphological gradient image (see Figure 4b). The weighted watershed algorithm was applied to this image to produce the results shown in Figures 4c and 4d. The mosaic image (Figure 4c) appears to be considerably less complex than the original X-ray image, with the fillings looking like one homogenous block. Figure 3a Original image Figure 3b Watershed segmented image Figure 3c Mosaic image Figure 3: MRI brain image The weighted watershed segmented image as in Figure 4d has clearly overcome the over-segmentation problem. In this image the darkest watershed lines follow the boundaries between catchment regions that have strongly differing values in the mosaic image. Thresholding can also be used to further enhance the segmented image if needed. Figure 4a Original dental X-ray image.
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  Figure 4b Morphologicalgradient image Figure 4c Mosaic image of Figure 4a References [1] Beucher, S. 1994. Watershed, Hierarchical Segmentation and Waterfall Algorithm. In: Serra,J.a.S.P. (ed). Mathematical Morphology and Its Applications to Image Processing. Kluwer Academic Publishers, pp. 69-76. [2] Haralick, R. and Shapiro L. Survey: Image segmentation techniques. 29. 1985. Computer Vision, Graphics, and Image Processing. Figure 4d Segmented dental image using Weighted Watershed Algorithm Figure 4: Weighted watershed segmentation of a dental X-ray image [3] Vincent, L. and Soille, P. 1991. Watersheds in Digital Spaces - An Efficient Algorithm Based on Immersion Simulations. Ieee Transactions on Pattern Analysis and Machine Intelligence 13: 583-598. [4] Najman,L. and Schmitt, M... 1996. Geodesic saliency of watershed contours and hierarchical segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 18: 1163-1173. [5] Hagyard, D.M.P. and Razaz, M, 1996. “Analysis of Watershed Algorithms for Greyscale Images", Proc. IEEE I.C Image Processing, Vol. I.