郭菲 翻译原文 A Comparative Study of Different Segmentation Techniques+2011年.pdf

J Nondestruct Eval 2012 31 1 16 DOI 10 1007 s10921 011 0116 6 A Comparative Study of Different Segmentation Techniques for Detection of Flaws in NDE Weld Images Vijay R Rathod R S Anand Received 2 March 2011 Accepted 6 October 2011 Published online 20 October 2011 Springer Science Business Media LLC 2011 Abstract For detecting the fl aws and their type in radio graphic images having different types of fl aws three seg mentation techniques have been applied here Every seg mentation technique has its own advantages and disadvan tages i e results obtained are of varying quality Taking this into account comparative study has been performed here to fi nd the best segmentation technique for a particular type of fl aw These methodologies are compared and concluded to be effective for all possible nine types of weld fl aws detec tion Slag Inclusion Worm Hole Porosity Incomplete pen etration Under cuts Cracks Lack of fusion Weaving fault Slag line and feature extraction after being successfully tested on more than 80 radiographic images obtained from EURECTEST International scientifi c Association Brussels Belgium and24radiographsofshipweldprovidedbyTech nic Control Co Poland were used obtained from Ioan nis Valavanis Greece and 67 radiographs of actual weld test samples obtained from Bharat Heavy Electrical Limited BHEL India The comparison with other NDT methods with application of image processing on the radiographic images of weld defects is aimed to detect the defects reli ably and make accept reject decision as per the international standard Keywords Radiographic images weld fl aws Segmentation Region growing Morphological edge detection and multistage watershed transformation Standard NDT methods V R Rathod R S Anand Department of Electrical Engineering Indian Institute of Technology Roorkee 247667 India e mail vicky7574 1 Introduction First of all edge based segmentation was used on the im ages It gave good results in few types of fl aws while few others were not clearly identifi able by this technique In this technique the edge detection is the prior step to provide the edge pixels after that these edges are modifi ed to produce the close curves representing the boundaries between adja cent regions But to convert the weak edge pixels into the close boundary is very diffi cult task of segmentation In such low quality images edge based segmentation algorithms do not identify the border accurately and the grouping process presents serious diffi culty in producing connected close con tour This makes it diffi cult to fi nd the dimensions of the fl aws accurately The complement of the boundary based method is the region based segmentation Under this category region growing method has been implemented here Region grow ing method always provides closed contour and makes use of relatively large neighborhoodfor decision making but se lection of seed point is diffi cult in this case The accuracy of the technique depends on the selection of seed point which is time consuming It suffers from pixel sorting orders for labeling and seed selection Watershed transformation based image segmentation is another technique which has been applied here It is a widely used in image processing because it is a simple intuitive method It produces complete division of image in separate regions even if contrast is poor so no con tour joining is required But it has few drawbacks also like over segmentation sensitive to noise poor detection of thin boundaries and poor detection of signifi cant areas with low contrast This can be easily overcome by markers watershed segmentation 2J Nondestruct Eval 2012 31 1 16 In 2009 Alaknanda et al proposed novel method of fl aw detection in radiographic weldment images using morpho logical watershed segmentation 1 Rafael Vilar described an automatic system of classifi cation of weld defects 2 N M Nanditha proposed comparative study on the suitabil ity of feature extraction techniques for tungsten inclusion and hotspot detection from weld thermographs 3 H Kas ban et al developed feature extraction methodology using cepstral approach 4 Ioannis Valavanis et al recently pro posed other method for detection and classifi cation of de fects in weld radiographs 5 K R Maser et al proposed novel NDE methods for quality assurance of new pave ment thickness 6 Debasish Basak proposed case study Non destructive evaluation 7 Hartmut Baumbach pro posed Non destructive testing and quality management 8 Christopher C Ferraro proposed method Detection and As sessment of Structural Flaws in Concrete Bridges with NDT Methods 9 Scott R Cumming proposed Tensile Strength Prediction in Concrete Using Nondestructive Testing Tech niques 10 2 Comparative Analysis of Segmentation Results 2 1 Segmentation Techniques Employed The segmented images obtained after processing data base by aforementioned techniques are presented here under the differentcategoriesoffl aws Discussionontheresultsisalso presented in each case 2 2 Morphological Edge Based Segmentation Technique Edge is a set of connected pixels that lie on boundary be tween two regions The edges form the outline of an object An edge is also the boundary between an object and the background and indicates the boundary between overlap ping objects Edge detection is one of the most commonly used operations in image analysis 12 13 The various steps involved in edge based segmentation are enhancement of image by contrast modifi cation and noise removal detection of edges for objects fl aws link ing and tracing of edges The detection of object or fl aw detection follows the segmentation process edges are de termined in the weld zone The edges are formed where there are abrupt changes in the intensity of the pixels There are different types of edge detectors which can be used for detecting the edges having different characteristics 12 13 The various edge detection techniques have been imple mented here to identify edges corresponding to relevant in formation in the radiography and ultrasonic NDT images The second derivative operators like Laplacian operators and Canny have been used here for better edge localiza tion These have stronger response to fi ne details such as thin lines and isolated points On the basis of performance of different edge detectors in edge based segmentation tech nique Canny operators have been preferred for determina tion of edges 14 The image obtained after the edge detection operation is informofcontoursbutcontoursobtainedthushavedisconti nuities due to cracked edges To obtain closed contour mor phological processing is performed on the binary formatted image Morphological processing involves dilation followed by erosion of edges on the basis of edge features The image is dilated fi rst and then eroding is performed which helps in disappearance of high contrast lines fi lling of the small holes gaps and narrow gulf and smoothing of the edges to preserve the original size 13 14 The output obtained after morphological operation is su perimposed on the original image to correlate the defi ned edges to different types of weld fl aws in the image 2 3 Region Growing Based Segmentation Region growing technique gives good results where borders are diffi cult to detect and to generate better results in noisy image Region growing is a boundary extraction approach Region growing is a procedure that makes group of pixels or sub regions into larger regions based on predefi ned criteria The goal of region growing is to use image characteristics to map individual pixels in an input image to sets of pixels called region An image region might correspond to a weld fl aw in present case or a meaningful part of it The region growing method relies on the homogeneity criteria These are based on fi nding parts of images which are homogeneous for a given set of properties Region based methods always provide closed contour regions and make use of relatively large neighborhoods for decision making Users select a point which is known as seed and a region grows out from this seed until some stopping growth criteria are met 15 Unlike gradient and Laplacian edge detection methods the borders of regions found by region growing are perfectly thin and connected It is stable with respect to noise while boundary tracking technique which gives connected edges is joined having no gap between them The regions produced are coherent regions allowing some fl exibility of variation within the region 2 3 1 Basic Idea of Region Growing For region growing method homogeneity is an important property which can be based on gray level average inten sity variance color texture motion shape and size 19 etc For region growing based segmentation the basic re quirement to satisfy the region similarity in an image is as follows J Nondestruct Eval 2012 31 1 163 Basic purpose of region growing is to segment on entire image R into smaller sub images Ri 1 2 N which satisfy the following conditions a R N i 1 nRi 1 b P Ri True i 1 2 N 2 Where N is the total number of regions in an image and P Ri is a binary homogeneity evaluation of the region Ri A logical statement represented by 2 gives that if pixels in the region are suffi ciently similar in terms of gray level then it is true It means P Ri TRUEif f j k f m n T FALSEotherwise 3 where j k and m n are the coordinates of neighboring pixels in region R This predicate states that a region R is uniform if and only if any two neighboring pixels differ in gray level by no more than T Using this equation a com mon misconception is involved such as it restricts the gray level variation within a region to a range of width T 7 8 A similar predicate can be used i e P Ri TRUEif f j k R T FALSEotherwise 4 where f j k is the gray level of a pixels from region R with coordinates j k and Ris mean gray level of all pixel in R except the pixel at j k The region similarity criteria shown in 3 and 4 are basically known as fi xed threshold homogeneity test The seeded region growing approach segments the image into the homogeneous regions with respect to a set of seed points The basic approach is to start with each image pix els which are taken as a set of seed points and these grow regions by appending to each seed those neighboring pix els that have properties similar to the seed The most natu ral method of region growing is to begin the growth in the raw image data where each pixel represents a single region These regions almost certainly do not satisfy the condition for the hypothesis in 2 and so regions will be merged as long as they satisfy 6 c Ri Rj i j 5 where Riand Rjare adjacent d P Ri Rj False i j 6 This approach takes into consideration of merging ad jacent regions based on the probability that they have the same statistical distribution of intensity values The region merging starts from a uniform seed region and neighbors are merged until no more neighboring regions conform to the uniformity criterion At this point the region is extracted from the image and a further seed is used to merge another region 16 The selection of an appropriate threshold is crucial to get successful region growing results In the applied algorithm the threshold value is determined by histogram The valleys and peaks of histogram help in determining the threshold values Although single threshold may be suffi cient to seg ment an image but multiple thresholds give better results in complex images 12 With the help of iteration method the threshold which gives best result can be obtained easily Existingalgorithmsappliedforregiongrowinghavemul tiple thresholds and these results are clubbed together to get best results It is a position dependent threshold method Lo cal thresholding technique is used on different parts of im age to select the most suitable threshold for merging two regions In all these cases usually the size of the region increases by using 4 adjacent or 8 adjacent neighbor When the grow ing of one region stops other seed pixel which does not be long to any region is chosen and the process of growing of another region starts This whole process is continued until all pixels belong to some region It gives good result that corresponds well to observed edges Starting with one seed gives bias results in favor of seed chosen fi rst Therefore simultaneous region growing tech nique is used A number of regions are allowed to grow si multaneously and similar regions will gradually merge with each other 2 3 2 Few Common Approaches in Region Growing Few common approaches used in region growing method are briefl y discussed here Similarity MeasuresThis is based on homogeneity criteria in which individual pixel intensifi es are compared It can be sensitive to noise also but it can be reduced by comparing neighborhood characteristics between pixels Thus in this case average intensity pixels are compared over a neighbor hood around Comparison to Original Seed PixelEach pixel is com pared back to seed point by using S p r when adding pixel r to growing region This is very sensitive to the choice of seed point and it can give different regions for the different seed point even if seed points lie in the same region Multiple SeedsIn this approach instead of starting with single pixel a set of pixels which describe the region statis tics are selected for growing the regions This helps in pro ducing identical regions under varying noise conditions 4J Nondestruct Eval 2012 31 1 16 In the present algorithm the threshold is chosen with the help of histogram The histogram of an image is analyzed to fi nd the threshold to merge region to extract the informa tion Multiple thresholds are required to produce segmenta tion of more complex images especially in industrial images because of high variation in intensity of background The multiple threshold region growing algorithm is based on po sition varied threshold in segmentation process The local threshold can be determined with the help of histogram The regions defi ned thus are superimposed on the original image to correlate the edges with the various types of fl aws in the image 2 4 Multistage Watershed Transform Based Segmentation Watershed transform is one of the methods based on region based segmentation The watershed transform comes from the fi eld of mathematical morphology It is a powerful tech nique for segmentation Watershed transform has interest ing properties that make it useful for many different image segmentation applications It is a simple and intuitive and always produces a complete division of the image 17 19 The segmentation by watersheds embodies many of the concepts of the approaches like shareholding detection of discontinuities and region processing and it produces more stable segmentation results including continuous segmented boundaries Watershed transformation is built by implemen tation of fl ooding process on a gray tone image 18 The basic conceptof watershed is based on visualizingan image in three dimensions i e two spatial coordinates versus gray levels In such a topographic interpretation three types of points are considered such as a Points belonging to a regional minimum b Points at which a drop of water if placed at the loca tion of any of those points would fall with certainty to a single minimum and c Points at which water would be equally likely to fall to more than one such minimum For a particular regional minimum the set of points satis fying condition b are called catchment basin or watershed of that minimum The points satisfying condition c form crest line on the topographic surface and are termed as wa tershed lines or divide lines The principal objective of segmentation algorithms based on these concepts is to fi nd the watershed lines The punch ing holes at each regional minimum and entire topography are fl ooded from below by letting water rise through the hole uniformly When rising water in distinct catchment basin is about to merge a dam is built to prevent the merging The fl ooding will eventually reach a stage when only the tops of dams are visible above the water line The dam boundaries correspond to the divide lines of the watersheds Therefore they are continuous boundaries extracted by a watershed segmentation algorithm In the work reported in this paper for comparative studies multistage watershed transformed is used to take care of the over segmentation In this process pre processed fi ltered weld image is used to reduce the noise level effi ciently The smoothed image is used for gradient calculation Any one gradient operator like Sobel Prewitt or Gaussian derivative is used in this segmen tation 17 Since the noise level is effi ciently reduced by fi ltering these operators perform well on fi ltered images The above obtained gradient image is partitioned in to primitive regions using image gradient magnitude An edge isobtainedbyusingintensitygradientandbygroupingthese edges contours surfaces are formed Edge detection is based on gradient processing The image pixels are labeled as edge or non edge 14 17 However there is always a possibil ity of either accepting a non edge as an edge or rejecting an edge as non edge since the labeling decision is local for each pixel The fi rst type of error corresponds to the detection of false edges while the second results in break ing of contour in to small edge groups separated by small gaps This type of error does not allow contour formation for higher level analysis These problems are solved by us ing morphological watershed transformation to the gradient image Initial watershed transformation gives many small homogeneous regions that result in over segmentation or un desired small regions in homogeneous regions Fortunately the watershed transform itself applied on another level will help to merge the fragmented regions The boundaries pro duced by the segmentation at this stage do not have same weight Those which are inside the homogeneous region are weaker In order to compare these boundaries the neigh borhood relation