Segmenting 2D Ultrasound Images using Seeded Region Growing

University of British Columbia Department of Electrical and Computer Engineering EECE 544 Medical Imaging Term Project Segmenting 2D Ultrasound Images using Seeded Region Growing David Boen #43589050 April 24, 2006

Segmenting 2D Ultrasound Images using Seeded Region Growing David Boen University of British Columbia Vancouver, British Columba david.boen@gmail.com Abstract Segmentation of ultrasound images is a difficult task, due to numerous artifacts and the presence of speckled noise. Definitions between objects are often blurred and weakened by speckling. Consequently, segmentation methods for ultrasound must be resistant to noise. In this paper, a method called Seeded Region Growing (SRG) is tested in isolating regions of interest for later processing. The SRG method involves selecting points called seeds as the starting locations for the segmentation process. Seed points may be manually or automatically selected, and both methods of seed selection are tested and compared. The resulting system is tested on simulated objects and B-mode ultrasound images of the liver with suspected lesions. 1 Introduction The growing numbers of images produced by ultrasound and other modalities require the use of computers to aid in identification. Segmentation methods automatically delineate of anatomical structures and other regions of interest for later analysis by human or automated processes. The segmented regions based on common characteristics. Once an image has been segmented, the regions can be quantified and analyzed using additional computer methods. This paper is organized in the following manner. A brief review of segmentation techniques and ultrasound image properties is discussed in Section 1. Section 2 describes the basic building blocks of an image segmentation system and the Seeded Region Growing method. Section 3 discusses the experimental results conducted using the Seeded Region Growing method. The paper is concluded in Sections 4 and 5. 1.1 Segmentation Methods Image segmentation is defined as the partitioning of an image into non-overlapping, constituent connected regions or sets with respect to some characteristic such as intensity or texture [1]. Ideally, each set corresponds to an anatomical feature or region of interest and are connected. A first step in segmentation involves pixel classification. Images pixels are sorted according to classes according sets, which are not specifically connected. Segmentation differs from classification in that the pixels comprising the sets are connected spatially. The sets are then labeled with meaningful designation. One of the simplest techniques in image segmentation involves thresholding. Threshold techniques classify pixels into a class whose values (intensity or color value etc) lie within particular range. Thresholding ignores spatial information contained in an image and is susceptible to noise or blurring effects which may change the classification values. Boundary techniques propose that pixel values change rapidly along the boundary between two regions. Filters such as the Sobel, LoG or Canny enhance gradients in image intensity, which can be further processed to produce closed boundaries. Region-based techniques postulate that pixels of a particular region have similar values, as calculated according to homogeneity criterion. If the criterion is satisfied, the pixel is assigned to the region with the highest homogeneity relative to the pixel. Seeded Region Growing, belongs to this category. Hybrid Techniques are based upon combining the techniques of boundary and region criteria. The morphological watershed method is guaranteed to produce closed regions, but may encounter difficulty in the presence of noise and indistinct boundaries between two regions. Other image segmentation techniques discussed in [1]

include active contour methods (snakes), classifiers, clustering, neural networks and deformable models. In this paper, the effectiveness of the Seeded Region Growing technique is tested in segmenting simulated and real ultrasound images. Varying levels of corrupting noise are introduced the simulated images and the segmented results compared to actual values. Additionally, a semi-automatic method of segmentation using seeded region growing is proposed and compared against the version using manually placed seeds on actual images of possible liver lesions. The results indicate the stability and robustness of seeded region growing, and the difficulty of implementing a generalized segmentation routine. a) b) Speckled noise Shadowing Figure 2: A sample ultrasound image Indefinite boundarie 1.3 Liver Hemangioma Ultrasound can be used to identify lesions or potential tumors in the liver. The tumor is often called liver hemangioma and is most often seen in adults over age 40 [5]. These lesions are often viewed without symptoms under routine ultrasound examination. They present as a well demarcated mass. They can range in size from 1 cm - 20 cm in area. Most are solitary lesions but 20% present in multiple lesion patterns. Potential lesions may appear as light regions embedded in the surround tissue. Blood vessels appear as dark regions. c) Figure 1: Examples of Segmentation Techniques a) Thresholding [2] b) Region Growing [2] c) Edge Detection [3] 1.2 Ultrasound Image Properties Ultrasonic imaging is steadily becoming one of the most popular imaging modalities, due to relative safety, and low cost. In 1993, the Wall Street Journal reported that spending in the United States on MRI units was approximately $520 million, on CT units $800 million, and on ultrasonic imaging systems $1000 million, and that sales of ultrasound systems was growing at 15% annually [4]. However, ultrasound images have reduced visual quality than those produced by CT or MRI. Artifacts and speckled noise reduce the effectiveness of many edge based techniques, blurring tissue boundaries and reducing image contrast and resolution. Shadowing is also introduced with increasing frequency. Figure 2 illustrates some of the issues faced by an ultrasound segmentation method. Figure 3: Objects to be segmented 1.4 Image Analysis The choice of image definitely impacts the segmentation approach. Figure 2 contains potential objects with indefinite edges, highly speckled noise, large white regions which may indicate organ boundaries and spots indicating potential lesions. 1.4.1 Speckled Noise The speckling pattern obscures many objects of interest. Speckled noise is a random, deterministic, interference pattern in an image formed with coherent radiation of a medium containing many sub-resolution scatterers. It is inherent in the images produced by ultrasonic devices. Available literature describes the speckle intensity distribution as Rayleigh or Rician function

[6]. The Rayleigh probability distribution function is given by the equation, 2 = I I p ( I) exp 2 σ 2σ 2 (1) where I is the pixel intensity. The noise characteristics of the image are analyzed by extracting a small sub-sample of a representative image for analysis. Plotting a histogram of the pixel intensities in Matlab yields are roughly Rayleigh shaped probability density function. 2 Segmentation System Overview and Basic Concepts of Seeded Region Growing Figure 5 describes the basic blocks of an image segmentation system. Raw Image Reading Preprocessing Manual Seed Placement Automatic Seed Placement Seeded Region Growing Region Growing 100 90 Segmented Image 80 70 Pixel Frequency 60 50 40 30 20 Figure 5: A Basic System for Segmenting Images using Seeded Region Growing 10 0 Pixel Value 0 50 100 150 200 250 Figure 4: Histogram of a typical Ultrasound Image, with fitted Rayleigh Curve 2.1 Preprocessing Due to the previously discussed properties of ultrasound images, filtering techniques are used to reduce the amplitudes of the noise fluctuations. Conventional linear filters such as low pass filters will reduce details such as lines or edges, blurring. Simple spatial filters such as mean and median filters also result in blurring and loss of resolution due to suppression of fine details. The filters discussed above ignore the behavior of image characteristics as they vary from point to point. Adaptive spatial filters utilize local characteristics to provide superior noise suppression performance. Adaptive median filters preserve detail while smoothing nonimpulse noise [7]. The basic algorithm is outlined as follows,

z min = minimum gray level value in S xy z max = maximum gray level value in S xy z med = median of gray level values in S xy z xy = gray level at coordinates (x, y) S max = maximum allows size of S xy Level A: A1 = z med - z min A2 = z med - z max If A1 > 0 AND A2 < 0, Go to level B Else increase the window size If window size S xy repeat level A Else output z xy. Level A: A1 = z xy - z min A2 = z xy - z max If B1 > 0 AND B2 < 0, output z xy. Else output z med.. The algorithm was used as provided by the in the function adpmedian.p in the Digital Image Processing Using Matlab (DIPUM) toolbox [8]. 2.2 Seeded Region Growing Seeded Region Growing (SRG) is a segmentation technique that is ideally for segmentation of images with similar intensity values. Seed points are selected from the image as the initial starting points for the segmented area. Seed points may be planted in the image, by an operator or automated methods. A priori knowledge of the image may be used to segment regions. A description of SRG follows. Now, consider the areas after the initial seed points have been assigned. Let A i, i=1 to n, represent the regions to which to which a seed pixel has been assigned. Next, the areas A i are enlarged by appending image pixels. Unallocated neighbor pixels of the pixels assigned to the regions are considered. Let N(x) represent the 8-connected neighbors of pixel x and T represent the set of unallocated pixels that border at least one of the regions, (2) The pixels that are allocated to a particular region are more similar to the pixels in that region than those contained in other regions, according to a measure of difference. The difference function of x, δ(x) may be defined in according to need and suitability. The properties may include criteria such as regional difference in statistical properties, features or textural. The simplest definition for δ(x) is, where g(x) is the pixel intensity and A i is an area to which pixel x may be attached. In the case of multiple adjoining areas A i the delta function for the pixel is chosen as the difference minimum value to the adjoining areas. (4) T After pixel x is allocated to a region (or boundary), the unallocated 8-connected neighbors of pixel x are added to the set T for consideration based upon their δ value. x Figure 6: The 8-connected neighborhood N(x) of pixel x The enlargement process continues iteratively until all image pixels are allocated to an area or a defined border region between the areas. The process of assigning only neighbors of pixels already contained within regions to a region (equation (2)) ensures connectivity, while the applying the minimum difference criteria equation (4) results in maximum regional homogeneity. The basic Seeded Region Growing algorithm follows, as defined in [9]. Initialization: Label seed points according to their initial grouping. Put neighbors of seed points (the initial T) in the SSL. Region Growing: While SSL is not empty do Remove first pixel x from the SSL. Test the neighbors of this point: if all neighbors of x which are already labeled (other than boundary label) have the same label than Set x to this label. Update running mean of corresponding region. Add neighbors of x which are neither already set nor already in the SSL to the SSL according to their value of δ else Flag y with the boundary label. (3) The basic data structure unit used in Seeded Region Growing is the SSL, or Sequential Sorted

List. The SSL allows pixels with the lowest delta value to be processed first, as new pixels from T to be added based their delta value. However, entries already in the SSL are not updated to reflect their difference from the containing A i properties. The following figures provide an example of the seeded region growing process, given the simple definition of δ(x). Figure 7 illustrates a simple pixel intensity value matrix and initial seed locations, while Figure 8-Figure 11 illustrate the δ values and labeling of the pixels at various stages of processing 5 5 5 9 9 5 5 5 9 9 3 3 3 1 1 3 7 3 1 1 3 3 3 1 1 Figure 7: The Image Pixel Intensity Matrix and Initial Seed Locations (Colored) 0 0 0 0 0 0 4 4 4 4 4 0 0 4 4 4 0 Figure 8: The Initial δ Matrix 0 0 0 0 0 0 0 0 4 4 4 0 0 4 4 0 0 4 4 4 0 Figure 9: The δ Matrix after Processing pixels with δ=0 4 4 4 4 4 4 4 4 Figure 10: The Final δ Matrix 5 5 5 9 9 5 5 5 9 9 3 3 3 1 1 3 7 3 1 1 3 3 3 1 1 Figure 11: The Segmented Image Matrix 2.2.1 Advantages Seeded Region Growing has a number of characteristics which are desirable in an ultrasound segmentation technique. SRG is more robust with respect to noise than other techniques such as edge based techniques. Speckled noise can cause large gradient values in the image and produce erroneous edges from filters such as the LoG or Sobel. Also, SRG does not depend on connecting the edges to form closed region boundaries. SRG is robust in the presence of unwanted small round objects in the image that are found in liver ultrasound images. Regions are grown outwardly from the seed areas as neighbors of pixels already in the region are added are added. SRG is adaptable to particular images. The technique may be made more robust to noise when image corrupted with noise of equal variance simply by modifying the definition of δ(x). The simple measure of homogeneity is dependent on the mean value of the area, which may initially be affected by noise. The standard deviation (SD) of the region can be added to the δ(x) to compensate. (5) Lastly, SRG is simple in concept and easy to control. In the basic form, SRG does not require tuning of parameters and is controlled only by seed location. It is ideally suited for interactive situations in which relatively unskilled users can apply a higher level knowledge of the image to successfully segment regions of interest. 2.2.2 Disadvantages Seeded Region Growing has a number of inherent weaknesses. Each segmented region must have a seed location chosen. However, it is difficult to intelligently place the seeds in the absence of a priori knowledge of the image. One version of the Seeded Region Growing algorithm involves considering all pixels as seeds and merging the regions together [10]. Also, automated methods may be used to intelligently position seeds. Seeded Region Growing is also sensitive to the number seeds chosen. Too few or many seed points chosen will result in under or over segmentation of the image The authors of a new, improved seeded region growing algorithm [11] noticed two types of order dependencies inherent order dependencies and implementation order dependencies.

In inherent order dependencies, when several pixels from T have the same values of δ, the order of pixel processing becomes critical. The first pixel chosen for processing from T influences the region mean to which it is assigned, influencing δ values of subsequent pixels. Also, when pixels from T have the same δ for multiple regions, the assignment of the pixel similarly affects the mean of the region to which it is assigned. Implementation order dependencies appear as a consequence of using an SSL as the data structure for T. The δ values of entries already in the SSL as pixels are not updated as pixels are added to regions. Adams and Bischof claim that this leads to negligible difference in results but greatly enhances speed. [12]. Consequently, pixels which are initially bordering regions upon initialization are added to the SSL according to a δ calculated dependent on the processing order and never updated. Also, subsequent neighbors added are dependent on scanning ordering. The SRG algorithm is sensitive to noise if individual seed pixels are placed on outlier values which affect the initial region mean. Modifying the algorithm to allow initial seed regions rather than individual seed pixels provides a simple solution. This seeding allows a more accurate estimation of the region mean and standard deviation upon initialization. 2k+1 Also, the Adams and Bischof s paper suggests using high level knowledge to select seed points in the lung [13]. In the case of ultrasound images automatic seeds points may be found by isolating the light and dark areas according to certain criteria. The algorithm I have devised is based on ideas found in [14] is listed as follows, a) Adaptive median filtering the image to reduce noise. b) Selecting the central portion of ultrasound image c) Calculating a histogram of the image d) Threshold masking the image at the histogram edge intensities, to produce two masks, one with a light pixel emphasis and one with a dark pixel emphasis e) Isolating connected areas of emphasized pixels with sufficient area to be of interest f) Finding the centroid of remaining connected regions g) Using the centroids as the seed starting points for the Seeded Region Growing Process. h) Adding additional seeds to segment the black areas around the image and the medium are also seeded. The process is shown in the Figure 13. Seed points are shown in yellow. 2k+1 Figure 12: Seed area, side length 2k+1 2.3 Implementation and Extensions of the Basic Seeded Region Growing algorithm The SRG implementation defined in [] does not define the order in which pixels with equivalent δ values are added to the SSL. In order to maintain consistency, I have arbitrarily chosen to add pixels with equivalent delta values in a downwards columnwise order. Thus, pixels with a closer to the left of the image will be processed first. Although the original paper also does present a different delta definition (equation (5)) it is not tested. In my implementation, I compare and contrast the SD definition (equation (5)) and simple mean version (equation (3)) for effectiveness. a) b) 900 800 700 600 500 400 300 200 100 0 0 50 100 150 200 250 d) Dark Intensities d) Light Intensities c)

e)-g) h) Figure 13: Automatic Seeding Process 3 Implementation and Experimental Results The experiments were conducted in the Matlab 7.0 environment. Toolboxes were utilized to enlarge the number of functions available, including the Image Processing Toolbox, functions in the Digital Image Processing Using Matlab Toolbox. Three experiments tested the effectiveness of the seeded region growing algorithm: Testing the ability of the SRG algorithm to segment with increasing noise Testing the effectiveness of increased seed area size in combating increase noise Visually comparing the results of the automatic versus manual seeded segmentation. 3.1 Segmentation with Increasing Noise An image of two grayscale ellipses is used to test noise stability. The ellipses are placed on a uniform background to represent lesion or vessel-like objects. Subsequently, the image was corrupted with varying levels of Rayleigh Noise and the resulting image segmented. Comparing the known, correct area to the segmented results, for a given set of seed pixels yields a figure of merit for the effectiveness of the segmentation algorithm. Greater differences between the segmented and actual areas of object indicate poorer performance of the algorithm. The SRG algorithm was tested using the simple (equation (3)) and SD (equation (5)) definitions of delta, at increasing levels of Rayleigh noise and a 3x3 seed area (k=1). The segmented images for the simple definition are shown in Figure 15-Figure 18. The numerical area results are tabulated in Table 1 and Table 2. Real No Noise σ 2 = 4 σ 2 = 16 σ 2 = 64 Area 1 723 722 521 498 493 % 0.14% 27.94% 31.12% 31.81% Area 2 674 644 674 672 591 % 2.90% 6.78% 7.05% 18.26% Table 1 : Stability of SRG (Simple Definition) in the presence of Rayleigh Noise Corruption Figure 15: SRG (Simple), No Noise Figure 16: SRG (Simple), σ 2 = 4 Figure 17: SRG (Simple), σ 2 = 16 Figure 14: Seeded Region Growing Test Objects

Figure 18: SRG (Simple), σ 2 = 64 The segmentations using simple definition SRG averaged 20 seconds. The experiments than were conducted with the SD definition of delta. Table 2 summarizes those results. Real No Noise σ 2 = 4 σ 2 = 16 σ 2 = 64 Area 1 723 722 680 713 686 % 0.14% 5.95% 1.38% 5.12% Area 2 674 644 630 671 647 % 10.93% 12.86% 7.19% 10.51% Table 2 : Stability of SRG (SD Definition) with respect to Rayleigh Noise Corruption Figure 19: SRG (SD), No Noise Figure 22: SRG (SD), σ 2 = 64 The segmentations using SD definition SRG averaged 40 seconds. 3.1.1 Discussion In each case (simple delta and SD definition), the real, actual areas are compared with the segmented regional areas. Comparing Table 1 and Table 2, the performance of the SD definition (5) of delta is generally better are larger noise levels than the simple definition (3). However, the segmentation time roughly doubled, highlighting the expense of computing the standard deviation for each region during the growing process. 3.2 Seed Size effect In order to measure the effect of seed size on stability of the algorithm, the seed size was increased from 3x3 (k=1) to 9x9 (k=4). The segmentation was conducted at Rayleigh noise levels of σ 2 = 64 and 512. Figure 20: SRG (SD), σ 2 = 4 Figure 23: Simple Definition - σ 2 = 64 Figure 21: SRG (SD), σ 2 = 16 Figure 24: Simple Definition - σ 2 = 512

Figure 25: SD Definition - σ 2 = 64 Figure 27: Manual SRG Result on an Ultrasound of Hemangioma in the Liver Figure 26: SD Definition - σ 2 = 512 k=4 (9x9) Real Simple Def. σ 2 = 64 Simple Def σ 2 = 512 SD Def. σ 2 = 64 SD Def. σ 2 = 512 Area 1 723 695 664 716 685 % 3.87% 8.16% 0.97% 5.26% Area 2 674 616 636 670 659 % 14.80% 12.03% 7.33% 8.85% Table 3: Increasing the Seed Area by 4 Comparing Table 3 to Table 1 and Table 2 for σ 2 = 64, the performance of both SRG implementations did not improve significantly with larger seed sizes for the tested images. Figure 28: The Effect of Seed Misplacement 3.3 Manual versus Automatic Seeded Region Growing Figure 27 and Figure 28 illustrate the effect of misplacing seeds within an image. The individual seed areas (white squares) in Figure 28 have been offset from their original positions in Figure 27. Clearly, the segmented results are different. The light boundaries and dark blood vessels appear better segmented in Figure 27: Manual SRG Result on an Ultrasound of Hemangioma in the Liver. Figure 29 and Figure 30 illustrate the combined automatic seed selection and seeded region growing process. The yellow rectangle in Figure 29 covers the area in Figure 27, and the seed choices can be contrasted between the two. The light lesion-like area within the rectangle seems ignored by both segmenting definitions, while the dark blood vessels are detected and segmented. Figure 29: The Automatic SRG Seed Selection

7 References Figure 30: Automatic SRG Result 4 Conclusions The Seeded Region growing method for segmenting images was implemented and tested in Matlab. The SD definition of delta for the Seeded Region Growth method proved resilient to even high levels of noise, but with added computational time. Larger seed sizes did not illustrate a marked improvement in the segmentation of the test objects. An automatic method of selecting seed points was demonstrated and proved reasonably effective. It is suitable for use in a semi-interactive application, where seed points are suggested and may be user-corrected. 5 Directions for Future Work An improved SRG algorithm described in [11] eliminates the inherent order disadvantages by processing pixels with identical δ values in parallel. Also, is possible to replace the SSL as data structure with a PQ (Priority queue) or LIFO Queues, as described in [11] Alternative version of the homogeneity or delta function exist that may prove more effective. A Vector definition was discussed in [15], combining multiple criteria in a single figure of merit. The authors of [10] use local statistic measures in the vicinity of a pixel as a measure of homogeneity and region merging to combine regions with similar characteristics. 6 Acknowledgement The author thanks Dr. Robert Rohling for providing the ultrasound images on which the tests were conducted. [1] Dzung L. Pham, Chenyang Xu, and Jerry L. Prince. Current Methods in Medical Image Segmentation. Annual Review of Biomedical Engineering. Volume 2, 2000. 315-337 [2] Dzung et al, p.323 [3] Detecting a Cell using Image Segmentation (Image Processing Toolbox Morphology Demos) http://www.mathworks.com/products/demos/image/d etect_cell/morph2.html [4] The Biomedical Engineering Handbook: Second Edition. Chapter 65, p. 23. Online edition, CRC Press 2000 [5] Liver Hemangioma http://www.novanews.org/liverhemangioma.htm [6] A beginner s guide to speckle. http://dukemil.egr.duke.edu/ultrasound/kspace/node5.html#section0051000000000000000 0 [7] R. C. Gonzalez, and R. E. Woods, Digital Image Processing, Second Edition. New Jersey: Prentice- Hall, 2001, p. 241. [8] R. C. Gonzalez, et al. Digital Image Processing with Matlab. New Jersey: Pearson Prentice-Hall, 2004. [9]R. Adams and L. Bischof. Seeded Region Growing. IEEE Transactions on Pattern Analysis and Machine Intelligence. Vol. 16, No. 6, June 1994 p. 641-647 [10] Ashish Thakur Radhey Shyam Anan. A Local Statistics Based Region Growing Segmentation Method for Ultrasound Medical Images, INTERNATIONAL JOURNAL OF SIGNAL PROCESSING Volume 1 Number 2 2004 ISSN:1304-4494, 141-146 [11] Andrew Mehnert, Paul Jackway. An improved seeded region growing algorithm, Pattern Recognition Letters, Vol. 18, 1997, pp. 1065-1071 [12] R. Adams and L. Bischof. p. 643 [13] R. Adams and L. Bischof. p. 644 [14] Hiransakolwong, N., Hua, K. A., Vu, K., and Windyga, P. S. 2003. Segmentation of ultrasound liver images: An automatic approach. IEEE Multimedia and Expo, 2003 ICME 03. Proceedings, 2003 International Conference, 1, 573-576. [15] Xiaohui Hao, Charles Bruce, Cristina Pislaru and James F. Greenleaf. A Novel Region Growing Method for Segmenting Ultrasound Images. IEEE Ultrasonics Symposium, Vol. 2, pp. 1717-1720, 2000.