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(IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 5, August 2010 Multi-Label Image Segmentation for Medical APPLICATIONS BASED ON GRAPH- CUTS R.Rajeswari, P. Anandhakumar, Research Scholar, Department of Information Technology Asst. Professor, Department of Information Technology MIT campus, Anna University MIT Campus, Anna University Chennai-44,India Chennai-44, India rajimaniphd@gmail.com anandh@annuniv.edu Abstract : Mathematical morphology is very attractive for segmentation by weighted aggregation algorithm, and apply automatic image segmentation because it efficiently deals with the technique to the task of detecting and segmenting brain geometrical descriptions such as size, area, shape, or connectivity tumor and edema in multichannel magnetic resonance (MR) that can be considered as segmentation-oriented features. This volumes.[3]. paper presents a new approach for some theoretical and practical improvements of image segmentation based on graph cuts. The The flowchart of proposed system shown in Fig.1.The tumors method is based on the use of the region adjacency graph segmentation is obtained through the computation of a produced by the watershed transform from mathematical minimal graph cut of the region adjacency graph obtained on morphology. Marker extraction identifies the presence of the watershed segmentation of the image. The user has to homogeneous regions. The combination of morphological and graph cuts segmentation permits us to speed up and define new interactively specify the location of the tumors as well as a classes of energy functions that can be minimized using graph marker to specify the background. A post-processing step is cuts. The use of region graphs gives promising results and can also proposed to smooth the segmentation result. This post- potentially become a leading method for interactive medical processing step consists in a morphological opening of the image segmentation. segmentation. Keywords- Image segmentation, marker extraction, morphology, Original Image User specified Graphs, Watershed Transform. Markers Watershed Transform I. INTRODUCTION MAGNETIC resonance imaging (MRI) provides detailed images of living tissues, and is used for both brain and body Region Adjacency Graph human studies. Data obtained from MR images is used for detecting tissue deformities such as cancers and injuries. The Tumor segmentation by key to any automatic method is that it must be robust, so that it Computation of Minimal Graph Cuts produces reliable results on every image acquired from any MR scanner using different relaxation times, slice thicknesses and fields of view. More recently, computer-assisted methods Post processing have been used for specific tasks such as extraction of MS lesions from MRI brain scans [1], [2]. A key problem in medical imaging is automatically segmenting an image into its constituent heterogeneous processes. Automatic segmentation has the potential to Fig 1.Flow Chart of proposed method positively impact clinical medicine by freeing physicians from the burden of manual labeling and by providing robust, quantitative measurements to aid in diagnosis and disease II. RELATED WORK modeling. One such problem in clinical medicine is the automatic segmentation and quantification of brain tumors. There are lot of work available related to the proposed The model-aware affinities integrated into the multilevel work, in which some of them are significant. It includes 142 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 5, August 2010 automatic and semiautomatic for segmentation of brain into overall classification accuracy compared to pixel-by-pixel different tissues, including MS lesions. These approaches approaches. include a variety of methods such as statistical, fuzzy, neural A very powerful segmentation method that has been widely networks, and fuzzy neural networks. used in image segmentation problems is the watershed Kamber et al. [4] developed a brain tissue model for transform (WT) [13]–[19]. Since its original application on segmentation of MR images of patients with MS disease. The grayscale images [13], [14], a very attractive computational offered model was three-dimensional, voxel based, which form has been derived [15] and extended to color images [16]. provided a priori probabilities for white matter, gray matter An extensive review of the watershed algorithms can be found and CSF. in [19].The watershed transform presents some advantages over other developed segmentation methods. The survey by Clarke et al. of segmentation methods for MR images [5] describes many useful image processing techniques 1) The watershed lines form closed and connected regions, and discusses the important question of validation. The where edge based techniques usually define disconnected various image processing techniques used for segmenting the boundaries that need postprocessing to produce closed regions. brain can be divided into several groups: those required to 2) The watershed lines always correspond to obvious contours perform a crude threshold-based extraction of the brain, of objects which appear in the image. followed by refinement of brain contours; statistical methods for brain segmentation, and region growing methods. The main problem of over-segmentation, can be usually overcome by the use of preprocessing or postprocessing, The work by M. Kass, A. Witkins & D. Terzopoulos [6] in producing a segmentation that better reflects the arrangement 1988 is the basis for the active contour model, the Snake of objects within the image. algorithm. The work by Mathews Jacob, Thierry Blu and Michael unser [7] presents different solutions for improving spline based snakes, and demonstrates the minimum curvature III.WATERSHED BASED IMAGE TRANSFORM interpolation property used as an argument to get rid of the The proposed system starts with the original image and explicit smoothness constraint. watershed transform of the image's gradient modulus is used The work of El Naqa et al [8] proposes a variational methods for segmentation purpose. based on multivalued level set deformable models for The validation is done comparing our segmentation with the simultaneous 2D or 3D segmentation of multimodality images physician’s delineation. Fig 1 shows the flowchart of the or multiple image sets from the same modality. proposed method. A multidimensional segmentation and filtering methodology A. Watershed Based on Dissimilarity Measures for accurate blood flow velocity field reconstruction from The watershed transform definitions can be slightly modified phase-contrast magnetic resonance imaging (PC MRI) in such a way that it produces a segmentation according to developed and validated by Kartik S. Sundareswaran, David different path based criteria. For image segmentation purposes H. Frakes et.al[9]. the watershed transform is computed on the image's gradient Yuri Boykov, Olga Veksler and Ramin Zabih [10] proposes an modulus, usually the morphological gradient. The estimation expansion algorithm that finds a labeling within a known of the gradient has thus also an importance for the quality of factor of the global minimum, while swap algorithm handles the segmentation. A first extension of the classical watershed more general energy functions. Both algorithms allow transform of the gradient image is based on the local important cases of discontinuity preserving energies for image dissimilarity between neighbor pixels. This approach, called restoration, stereo and motion. watershed by dissimilarity was originally proposed by Pard as[20] and Lotufo et.al in [21]. Saracoglu et al. [11] modeled the problem using a method consisting of three steps: image tessellation, clustering, and Given a pixel adjacency graph G = (V;E;W), we consider classification. The image was tessellated into regions with the following edges weights mapping similar properties using a region growing approach e i,j Є E , wi,j = (1/d(i,j ).|pi—pj) |+1) (tessellation step).Based on the “average” color information of the regions, clustering is performed. where i and j are two nodes of the graph, pi and pj are the grey level values of neighbor pixels of the image and d(i, j) is the A novel method for segmentation and classification of M- distance between the two pixels. FISH chromosome images is presented by Petros S. Karvelis, Alexandros T. Tzallas et al.[12]. The segmentation is based on These edges weights represent the local estimation of the the multichannel watershed transform in order to define image's gradient modulus. Instead of computing the regions of similar spatial and spectral characteristics. Then, a morphological gradient of the image at each pixel, the gradient Bayes classifier task-specific on region classification, is is here estimated "between" the pixels. This weight map applied. The combination of the multichannel segmentation especially provides a better detection of thin contrasted and the region-based classification is found to improve the objects. 143 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 5, August 2010 This weight map has the main advantage to achieve a better B. Region Adjacency Graph detection of small details compared to the classical A region adjacency graph can be obtained from low-level morphological gradient. segmentation of the image by numerous methods. It can be obtained from a first unsupervised clustering of the image, for IV. GRAPHS IN IMAGE SEGMENTATION instance one can apply the watershed transform [22, 23], flat This section presents the graphs commonly encountered in zones labeling [24, 25], or k-means clustering [26] to obtain imaging applications. A first level is the so-called "pixel such low-level segmentations. adjacency graph". In a second level, an unsupervised low level The minimal cut problem on region adjacency graph is used segmentation of the image (i.e. a segmentation that provides instead of pixel adjacency graph since it is advantageously much more regions than objects in the image) is used to build used to speed up and extend the presented techniques. a "region adjacency graph”. V. IMPLEMENTATION AND RESULTS A. Pixel Adjacency Graphs The region adjacency graph is obtained from the watershed In this case, the set of nodes of the graph is the set of transform computed from all minima of the morphological pixels of the image, and the edges link neighboring pixels. For each image and an adjacency system is needed to build the gradient of the original MRI image using Meyer's algorithm. corresponding pixel adjacency graph. Different common [27]. Then a region adjacency graph is extracted and used for adjacency systems are illustrated in figure. the next optimization steps. The user specified markers are used to compute a minimal graph cut separating the markers specifying the myocardium and the external tissues. The markers that specify the myocardium denoted as the set of regions Mm. The markers specifying the tissues surrounding the myocardium are denoted by M ext. F(ri , rj) is defined as the set of edges of the pixel graph connecting two regions ri and rj of the low-level watershed segmentation. Figure 1. 4 neighborhood adjacency system (V4). F(ri , rj)={e m,n Є E | m Є ri , n Є rj } (1) The strictly positive and decreasing function g used as an edge indicator for the a minimal surface is g(||I(p)||)= (1/(1+||I(p)||)k (2) where the k parameter was set to 2. The edges weights of the region adjacency graph GR= (VR, E R, W R ) are then set such that the weight of a graph cut equals Figure 2. 8 neighborhood adjacency system (V8). the energy function of a surface defined as w r i , , rj = Σ (g( || I(p)||)) (3) The myocardium boundaries are finally extracted by computing a minimal graph cut of the region adjacency graph with weights given by (3). The minimal cut is obtained on the region adjacency graph with two additional nodes s and t, respectively connected to the markers of the myocardium and the markers of the external tissues. The edges weights of the graph are summarized in Table I. Figure 3. 6 neighborhood adjacency system for 3D 144 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 5, August 2010 TABLE I. EDGE WEIGHTS OF GRAPH Edge Weight for w s , ri +∞ ri Є M m w ri,t +∞ rj Є M ext w r i , , rj Σ (g( || I(p)||)) ri Є V R , rj Є N r Figure 4c. Minimal surface computed by proposed (e m,n Є F(ri , rj)) method A slice of a 3D Heart MRI superposed with a hand made segmentation is shown in figure 4a. The segmentation A. Dataset highlights the aorta (in red), the superior vena cave (in dark The imaging modality in this application is an isotropic 3D blue), the pulmonary artery, the right and left chamber (light MRI. This imaging technology provides 50 to 120 slices blue) and the heart muscle (green and light red). covering the whole heart. The whole myocardium is not always visible on 3D MRI datasets, especially along the right C. Segmentation chamber, because the myocardium is too thin in this area. For each image, a reference segmentation done by an Thus the left part of the myocardium which is outlined in experienced radiologist is available. green in Fig is segmented in our proposed method. The other parts of the myocardium have been obtained with manual segmentations. Figure 5a. Liver tumors segmentation. (a) User specfied markers. Figure 4a. A slice of a 3D Heart MRI Figure 5b. Results of proposed segmentation strategy In order to evaluate the accuracy of the developed method, four scores are computed from five different evaluation measures. 1) Volumetric overlap: This score is computed as the Figure 4b. MRI heart image superposed with number of voxels in the intersection of segmentation and markers reference, divided by the number of voxels in the union of segmentation and reference. This value is equal to 1 for a perfect segmentation and is equal to 0 as the lowest possible 145 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 5, August 2010 value, when there is no overlap at all between the creation for surgery planning and simulation. The challenging segmentation and the reference. problems presented by medical applications forced us to 2) Relative absolute volume difference: The total volume imagine innovative methods that can tackle the problems of the segmentation is divided by the total volume of the linked with clinical applications. The method presented have reference. From this number 1 is subtracted, the absolute value all the characteristics to be used in real life applications: is taken and the result is multiplied by 100. robustness, speed and precision. 3) Average symmetric absolute surface distance: These are defined as those voxels in the object that have at least one VII FUTURE ENHANCEMENT neighbor that does not belong to the object. For each voxel in The minimal cuts can be extended to provide constrained these sets, the closest voxel in the other set is determined. All segmentation models. These new constrained problems are these distances are stored, for boundary voxels from both solved in a linear programming framework. The major reference and segmentation. Taking the average of all these limitation of linear programming is the current inefficiency of distances symmetric absolute surface distance is determined. generic solvers. An efficient linear program solver that can 4) Symmetric RMS surface distance: The squared handle graphs representing large images can be developed. distances between the two sets of border voxels is taken and the root is extracted and gives the symmetric RMS surface REFERENCES distance. [1] Johnston, M. S. Atkins, B. Mackiewich, and M. Anderson, “Segmentation of multiple sclerosis lesions in intensity corrected D. Comparison of Results: multispectral MRI,” IEEE Trans. Med. Imag., vol. 15. pp. 154–169, Apr. 1996. 1) Training: The evaluation scores of our method [2] A. P. Zijdenbos, B. M. Dawant, R. A. Margolin, and A. C. calculated on a set of MRI images presenting different tumors Palmer,“Morphometric analysis of white matter lesions in MR images: with known handmade segmentations. The evaluation scores Method and validation,” IEEE Trans. Med. Imag., vol. 13, p. 4, pp. 716– 724,Dec. 1994. compare our results with the radiologists segmentations. The [3] Jason J. 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Beucher, Morphological segmentation, Journal of Definitions,algorithms and parallelization strategies,” Fundamenta Visual Communication and Image Representation 1 (1990), no. 1, 21-46. Informaticae,vol. 41, pp. 187–228, 2001. [19] M. Pardas, Segmentacion morfologica de secuencias de imagenes: Aplicacion a la codificacion, PhD thesis, Universitat Politcnica de Catalunya,). Barcelona, Spain. (1995). [20] Lotufo and A. Falcao, The ordered queue and the optimality of the watershed approaches, Proceedings of the International Symposium on Mathematical Morphology 2000, Palo Alto, CA, Jun 2000. (2000), 341- 350. [21] N. Biggs, E. Lloyd, and R. Wilson, Graph theory, 1736-1936, Oxford University Press, 1986. AUTHORS PROFILE Dr.P.AnandhaKumar is Currently working as AssistantProfessor in the Departm- ent of Information Technology at Madras Institute of Technology, Anna University, Chennai,India. He has published several papers in international, national journals and conferences. His areas of interests includes content-based image indexing techniques and frameworks, image processing and analysis, video analysis ,fuzzy logic, pattern recognition, knowledge management and semantic analysis R.Rajeswari is currently doing Ph.D in the Department of Information Technology, MIT Campus,AnnaUniversity Chennai,India 147 http://sites.google.com/site/ijcsis/ ISSN 1947-5500

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