In this work, a novel framework for automated, spatially-adaptive adjustment of active contour regularization and data fidelity parameters is proposed and applied for medical image segmentation. The proposed framework is tailored upon the isomorphism observed between these parameters and the eigenvalues of diffusion tensors. Since such eigenvalues reflect the diffusivity of edge regions, we embed this information in regularization and data fidelity parameters by means of entropy-based, spatially-adaptive `heatmaps'. The latter are able to repel an active contour from randomly directed edge regions and guide it towards structured ones. Experiments are conducted on endoscopic as well as mammographic images. The segmentation results demonstrate that the proposed framework bypasses iterations dedicated to false local minima associated with noise, artifacts and inhomogeneities, speeding up contour convergence, whereas it maintains a high segmentation quality

Maroulis, Dimitris

Mylona, Eleftheria A.

Savelonas, Michalis A.

Skodras, Athanassios N.

Digital Repository of Hellenic Managing Authority of the Operational Programme &quot;Education and Lifelong Learning&quot; (EDULLL)

Autopilot Spatially-Adaptive Active Contour Parameterization for Medical Image Segmentation   Eleftheria A. Mylona1, Michalis A. Savelonas1, Dimitris Maroulis1, Athanassios N. Skodras2,   1Department of Informatics and Telecommunications, University of Athens, Greece 2Department of Electrical and Computer Engineering, University of Patras, Greece 1{emylona, msavel, dmaroulis}@di.uoa.gr 2skodras@upatras.gr  Abstract  In this work, a novel framework for automated, spatially-adaptive adjustment of active contour regularization and data fidelity parameters is proposed and applied for medical image segmentation. The proposed framework is tailored upon the isomorphism observed between these parameters and the eigenvalues of diffusion tensors.  Since such eigenvalues reflect the diffusivity of edge regions, we embed this information in regularization and data fidelity parameters by means of entropy-based, spatially-adaptive ‘heatmaps’. The latter are able to repel an active contour from randomly directed edge regions and guide it towards structured ones. Experiments are conducted on endoscopic as well as mammographic images. The segmentation results demonstrate that the proposed framework bypasses iterations dedicated to false local minima associated with noise, artifacts and inhomogeneities, speeding up contour convergence, whereas it maintains a high segmentation quality.   1. Introduction  Across the field of active contour segmentation, nearly all models rely on parameters which need to be tuned on a trial and error basis [1]-[5]. Researchers propose contradictory claims for empirically optimized parameterization, dependent on each dataset to be tested. A generally applicable active contour parameter configuration is still an open issue. Region-based active contours have been proposed in image analysis literature as suitable for several medical imaging modalities [6]-[9]. They are topologically adaptable and cope well with regions of weak edges, such as human tissues. Like most active contour models, they rely on parameters weighting the so-called regularization and data fidelity energy terms, which significantly affect their performance. Despite this significance, in most existing methods, both regularization and data fidelity terms are manually set and kept intact over the image. As a result, segmentation results are suboptimal and subjective. In addition, manual parameter setting asks for technical skills from the end-user, who is very often a Medical Doctor (MD).  Tracing the state-of-the-art methods presented in literature trying to tackle the issue of region-based active contour empirical parameterization, two key elements are mainly considered: a) the trade-off between regularization and data fidelity terms as well as, b) the spatial variance of regularization and data fidelity parameters. Ma and Yu [10] try to solve the balance between energy terms by utilizing a morphological approach. However, their method does not adjust each individual parameter separately. McIntosh and Hamarneh [11] adapt regularization weights across a set of images. Although an optimal regularization weight can be found for a single image in a set, the same weight may not be optimal for all regions of that image. In addition, the data fidelity term is still empirically determined. Erdem and Tari [12] utilize data-driven local cues focusing on edge consistency and texture cues. Nevertheless, this method requires technical skills from the end user. Pluempitiwiriyaweg et al. [13] and Tsai et al. [14] update parameters during the iterative process of evolution. Nonetheless, this dependency may yield to the propagation of early errors in the latter stages of contour evolution. Dong et al. [15] attempt to vary the regularization term based on the surface curvature of a pre-segmented vessel. However, the regularization weight does not rely on image properties. On the contrary, it depends upon the shape of the target region thus, limiting the general applicability of the method on different image modalities.  978-1-4799-1053-3/13/$31.00 c©2013 IEEE268 CBMS 2013In this work, a novel framework spatially-adaptive adjustment of regularfidelity parameters of active contours iapplied for the segmentation of medicproposed framework aims to relieve tedious and time-consuming procesparameterization of active contours, enrich segmentation results with reproducibility, in order to support earlclinical evaluation. It is based on the that regularization and data fidelity pthe same orthogonal directions with thediffusion tensors. These eigenvaluesinformation associated with locAccordingly, we embed this inforregularization and data fidelity parametentropy-based, spatially-adaptive ‘hlatter are able to drive the contour awaydirected edge regions and guide it towones. Hence, iterations dedicated to falare bypassed, speeding up contour coproposed framework does not requirefrom the end-user, is applicable to vimage modalities and is not affected bthe shape of target regions. In additsimplicity, it can be embedded into based active contour variations. The remainder of this paper is organSection 2 presents the proposed framew3 demonstrates the experimental conclusions of this study are summarize  2. Proposed framework  The proposed framework takes inconcept of anisotropic diffusion pattlatter allow the mapping of spatially-vaaccordance with their directionality. Fischematic representation of this ideellipsoids consisting of different difTwo of them correspond to a single dthus associated with structured targewhereas the next two correspond to muand are therefore associated with noregions of image artifacts. It should bemedical images consist of target regionweak edges however; these images are artifacts and noise as well as heteryields to inhomogeneous background. Aiming at capturing the nature of emedical image, i.e. whether it correspedge or noisy region, the Informሺܫܧሻ  measure is utilized. The latter obtfor automated, ization and data s presented and al images. The MDs from the s of empirical as well as to objectivity and y diagnosis and key observation arameters share  eigenvalues of  hold essential alized edges. mation in the ers by means of eatmaps’. The  from randomly ards structured se local minima nvergence. The  technical skills arious medical y alterations on ion, due to its several region-ized as follows: ork and Section results. The d in Section 4.  to account the erns [16]. The rying regions in g. 1(a) depicts a a with several fusion patterns. irection and are t edge regions, ltiple directions isy regions or  mentioned that, s which exhibit also plagued by ogeneity which ach region in a onds to a target ation Entropy ains high values in cases of regions characterized bof multiple directions. On the contrlower values in cases of regions diffusion pattern of one unique depicts the entropy behavior on eaof Fig. 1(a).   (a) Figure 1. A schematic represendiffusion patterns, (a) ellipsoids cand multiple direction diffusion pbehavior ܫܧ on each diffus In the context of the proposed fris considered as a grid of qxq sampmust be sufficiently large to presethe main structures of the target regdecomposed to the finest and secomeans of a multi-scale, muanisotropic filtering descriptor suctransform [17]. For each subbandmeasures are calculated accordinequations:  ),(log),(1 1nmpnmpIE jkNnMmjkjkjk jk⋅−= ∑∑= =∑∑= ==jk jkNnMmjkjkjknmInmInmp1 122)],([|),(|),(          where  ܫܧ௝௞  is the information entrimage ܫ௝௞  in the ݇௧௛ direction and the row size and ௝ܰ௞ the column image. As a result, spatially‘heatmaps’ are formulated in odiffusivity along each region. Considering once more the illustrated in Fig. 1(a), each ellipsby two axes, the principal anddescribe its length and width, rperpendicular to each other. Theseprincipal and minor eigenvectors aprincipal eigenvalue ߣଵ  reflects thy a diffusion pattern ary, entropy obtains characterized by a direction. Fig. 1(b) ch diffusion pattern (b) tation of distinct onsisting of single atterns, (b) entropy ion pattern. amework, the image les. The sample size rve the direction of ion. Each sample is nd finest scales by lti-directional and h as the contourlet  image  ܫ௝௞ , entropy g to the following                           (1)                            (2) opy of the subband the ݆௧௛ level, ܯ௝௞ is size of the subband -adaptive entropy rder to reflect the diffusion patterns oid is characterized  the minor, which espectively and are  axes correspond to nd eigenvalues. The e diffusivity along CBMS 2013 269the principal eigenvector and is lorespect to the principal axis, whereigenvalue ߣଶ  reflects the diffusivity aeigenvector and is vertical with respectaxis. Fig. 2(a) depicts principal and miof an ellipsoid. Region-based active contours evolvethe following energy functional:  dfdfregreg EwEwE ⋅+⋅=                 where  ܧ௥௘௚  and  ܧௗ௙ are associated witand data fidelity energy terms, respecݓ௥௘௚  and  ݓௗ௙  are weighting paramete(a) Figure 2.(a) Eigenvalues of ellipregularization and data fidelity paramcontour.  Fig. 2(b) depicts regularization anparameters of a contour. Regularizfidelity forces are tangent and verticalthe principal axis of the contour, retempting to notice that the regulacorresponds to the same direction aeigenvalue of the ellipsoid. This is alsthe data fidelity weight, which correspodirection as the minor eigenvalue. Thindicates a link between the regularifidelity parameters and the eigenvalupatterns.  As eigenvalues reflect the diffusivityof an ellipsoid, regularization andparameters reflect the diffusivity alongcontour by means of spatially-ad‘heatmaps’ and are calculated accfollowing equations:  marg,)/1( Idfdfreg wMNww jk=××∝The key notion is that, by appropriadata fidelity forces in noisy, high-entrocontour will be repelled. Hence, iteratierroneous local minima will be bypasscontour convergence towards target edgnoticed that the proposed framework acspatially-adaptive parameterization.  ngitudinal with eas the minor long the minor  to the principal nor eigenvalues  by minimizing                     (3) h regularization tively, whereas rs.  (b) soid, (b) eters of active d data fidelity ation and data  with respect to spectively. It is rization weight s the principal o the case with nds to the same is isomorphism zation and data es of diffusion  along the axes  data fidelity  the axes of the aptive entropy ording to the ))(ax( jkjk IIE (4) tely amplifying py regions, the ons dedicated to ed, speeding up es. It should be hieves autopilot 3. Results  The proposed framework has bthe region-based Chan-Vese moevaluate the segmentation performaversion versus the manually fineexperimental results are evaluateregion overlap measure, known Coefficient (TC) [18], which is defi )()(BANBANTC∪∩=                               where A is the region identified bmethod under evaluation, B is the and N() indicates the number of piregion. Experiments are conducted on images. The first set consists of 32containing polyps, provided by theAthens “Laiko”, Medical SchoAthens. All polyps were investigawho provided ground truth segillustrates segmentation results of Vese model, using the proposed fra (a) (b) (a1) (b1) (a2) (b2) (a3) (b3) een integrated into del [5] so as to nce of the autopilot -tuned version. The d by means of the as the Tannimoto ned by:                            (5) y the segmentation ground truth region xels of the enclosed two sets of medical  endoscopic images  General Hospital of ol, University of ted by MD experts, mentations. Fig. 3 the autopilot Chan-mework.   (c)  (c1)  (c2)  (c3) 270 CBMS 2013Figure 3.(a)-(c) Endoscopy images con(a1)-(c1) ground truth images, (a2)-(c2)obtained by the manually fine-tuned vsame iteration that the autopilot version(a3)-(c3) segmentation results of the auVese model.  Leaned on the results it is evidemanually fine-tuned version which spatially-uniform parameters, contour delayed. On the contrary, the resuautopilot version, where forces gevolution are appropriately amplifiedhigh-entropy regions, accelerating convThe second set consists of 50 images randomly obtained by thDatabase [19] containing fatty, well-defined, benign and malignant masses. segmentation results of the autopimodel, using the proposed framework.evident that the autopilot version segmentation quality at a much reducrate.  The autopilot version achieves an avof 82.9±1.6%, which is comparable to 80.7±1.8% obtained by the manuversion, with regards to both emammographic images tested. Howevversion converges in 10-20 times lessoriginal version achieves a TC value ofthe same iteration that the autopilconverged.    (a) (b)   (a1) (b1)   (a2) (b2) taining polyps,  segmentations ersion, in the  has converged, topilot Chan-nt that, in the utilizes fixed, convergence is lts support the uiding contour  in non-target, ergence.  mammographic e Mini-MIAS defined and ill-Fig. 4 illustrates lot Chan-Vese  It is once more yields a high ed convergence erage TC value the TC value of ally fine-tuned ndoscopic and er, the autopilot  iterations. The  52.4±11.3%, in ot version has (c) (c1) (c2) (a3) (b3) Figure 4.(a)-(c) Mammographiabnormalities, (a1)-(c1) ground trusegmentations obtained by the mversion, in the same iteration that thas converged, (a3)-(c3) segmentaautopilot Chan-Vese   4. Conclusions  In this work, a novel framewspatially-adaptive adjustment of regfidelity parameters is presented asegmentation of medical imagframework is based on an isomorpparameters and the eigenvalues oSince eigenvalues of diffusion information associated with localizthis information in the amount orthogonal directions by means oentropy ‘heatmaps’, which are abevolution. The proposed framework has bevaluated on endoscopic as wellimages. The experimental resuautopilot version is capable of aconvergence as well as masegmentation quality, comparable by the manually fine-tuned versionthat, the latter is a cumbersome aprocess which requires technical user and generates segmentation objectivity. Future directions of investigation of the proposed framtypes of medical image modalities.   Acknowledgements  We would like to thank the GAthens “Laiko”, Medical School endoscopic data as well as Prof. research group for the provision oThis research has been co-financeUnion (European Social Fund-ESFfunds through the Operational ProgLifelong Learning” of the NationalFramework (NSRF)-Research  (c3) c images containing th images, (a2)-(c2) anually fine-tuned he autopilot version tion results of the model. ork for automated, ularization and data nd applied for the es. The proposed hism between these f diffusion patterns. tensors hold vital ed edges, we embed of diffusion along f spatially-adaptive, le to guide contour een experimentally  as mammographic lts show that the ccelerating contour intaining a high to the one obtained . It should be noted nd time-consuming skills from the end results which lack this work include ework on different eneral Hospital of for the provision of M. Tzivras and his f the ground truth. d by the European ) and Greek national ram “Education and  Strategic Reference Funding Program: CBMS 2013 271Heracleitus II. Investing in knowledge society through the European Social Fund.   5. References  [1] Y. Rathi, N. Vaswani, A. Tannenbaum and A. Yezzi, “Tracking Deformable Objects Using Particle Filtering for Geometric Active Contours,” IEEE Transactions on Pattern Analysis and Machine Intelligence,vol. 29, no. 8, 2007, pp. 1470-1475. [2] G. Sundaramoorthi, A. Yezzi and A. Mennucci, “Coarse-to-Fine Segmentation and Tracking Using Sobolev Active Contours,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, no. 5, 2008, pp. 851-864. [3] K. Zhang, H. Song and L. Zhang,“Active Contours Driven by Local Image Fitting Energy,” Pattern Recognition, vol. 43, no. 4, 2010, pp. 1199-1206. [4] P.Horváth, I.H. Jermyn, Z. Kato and J. Zerubia, “A Higher-Order Active Contour Model of a ‘Gas of Circles’ and its Application to Tree Crown Extraction, Pattern Recognition, vol. 42, 2009, pp. 699-709. [5] T.F. Chan and L.A. Vese, “Active Contours Without Edges,” IEEE Transactions on Image Processing, vol. 10, no. 2, 2001, pp. 266-277. [6] Y. Shang, X. Yang, L. Zhu, R. Deklerck and E. Nyssen, “Region Competition Based Active Contour for Medical Object Extraction,” Computerized Medical Imaging and Graphics, vol. 32, no. 2, 2008, pp. 109-117. [7] L. Wang, C. Li, Q. Sun, D. Xia and C.-Y. Kao, “Active Contours Driven by Local and Global Intensity Fitting Energy with Application to Brain MR Image Segmentation,” Computerized Medical Imaging and Graphics, vol. 33, 2009, pp. 520-531. [8] J. Xu, J.P. Monaco and A. Madabhushi, “Markov Random Field Driven Region-Based Active Contour Model (MaRACel): Application to Medical Image Segmentation,” in Proc. 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Autopilot spatially-adaptive active contour parameterization for medical image segmentation

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Autopilot spatially-adaptive active contour parameterization for medical image segmentation

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Digital Repository of Hellenic Managing Authority of the Operational Programme "Education and Lifelong Learning" (EDULLL)

Autopilot spatially-adaptive active contour parameterization for medical image segmentation

Abstract

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Digital Repository of Hellenic Managing Authority of the Operational Programme &quot;Education and Lifelong Learning&quot; (EDULLL)

Digital Repository of Hellenic Managing Authority of the Operational Programme "Education and Lifelong Learning" (EDULLL)