VascularizationFeaturesforPolypLocalizationinCapsule Endoscopy V. B. Surya Prasath1, Hiroharu Kawanaka2 1Computational Imaging and Visualization Analysis (CiVA) Lab, Department of Computer Science, University of Missouri-Columbia, USA 1Graduate School of Engineering, Mie University, Japan prasaths@missouri.edu Contribution: Capsule Endoscopy Polyp Localization • Polyps in gastrointestinal (GI) tract can be precursors to cancer and detecting them early is important in determining their malignancy. • We consider polyp localization from capsule endoscopy imagery using vascularization features [1]. • We use features computed from principle curvatures of the image surface, and multiscale directional vesselness stamping we obtain localization of polyps. Vascularization Features • Principle curvatures: Let the principle curvatures of the image surface (x, y, I(x, y)) be κ2 1, κ2 1, the curvatures based texture feature is, Vκ(⃗x) = q κ2 1(⃗x) + κ2 2(⃗x). (1) • Multiscale directional vesselness stamping (MDVS): (a) Directional stamping [2]: Vd(⃗x) = G(⃗x) · I(⃗x) where, G(⃗x) = 1 2π p |L| exp  −1 2⃗xT L−1⃗x  , L =  cos(δ) −sin(δ) sin(δ) cos(δ)   λ1 0 0 λ2   cos(δ) −sin(δ) sin(δ) cos(δ) T with (λ1, λ2) are the eigenvalues and the angle of rotation δ = atan2(v2y, v2x), with v2 = (v2x, v2y) the smallest eigenvector of the structure tensor. (b) Frangi’s ﬁlter [3] is based on multiscale Hessian eigen-analysis and let the output be Vf. We combine the directional stamping Vd(·) with the multiscale vesselness Vf(·) to obtain multiscale directional vesselness stamping (MDVS): Vm(⃗x) = Vd(⃗x) · Vf(⃗x). (2) We use maximum response of both vascularization features Vκ in Eqn. (1), Vm in Eqn. (2) that can provide a good indicator of vascularization in polyps. (a) Polyp (b) Eigenvalues (λ1, λ2) (c) Angle δ (d) Vd(⃗x) (e) Vf(⃗x) IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Washington, DC, USA, 2015. Localization Results Example polyp localization using vascularization features for diﬀerent polyps imaged using Pillcam® cap- sules. Our approach obtains good localization and clearly reveals feature diﬀerences in adenoma and hyper- plasia polyps. (a) Malignant (b) Benign Vascularization features can be used to localize colorectal polyps (circled here) from VCE frames. (a) Malignant polyps obtained maximum responses for principle curvature based texture features whereas (b) benign polyps has smaller values. Currently, we are evaluating the method with expert segmentations and developing a classiﬁcation scheme based on the detected vessel features similar to [4]. References [1] V. B. S. Prasath. Polyp detection and segmentation from video capsule endoscopy: A review, Journal of Imaging, 2015. Special issue on Image and Video Processing in Medicine (Eds.: G. P. Martinsanz, P. Morrow, K. Suzuki). [2] J. Bredno et al. Algorithmic solutions for live device-to-vessel match. Proc. SPIE Med. Imag., Vol. 5370, pp. 1486–1497, 2004. [3] A. Frangi et al. Multiscale vessel enhancement ﬁltering. MICCAI, Springer LNCS Vol. 1496, pp. 130–137, 1998. [4] V. B. S. Prasath, R. Delhibabu. Automatic image segmentation for video capsule endoscopy. CIHD, Springer Briefs in Forensic and Medical Bioinformatics (Eds. N. B. Muppalaneni, V. K. Gunjan), pp. 73–80, 2015. Website and More Information • Download the poster @ ﬁgshare.com: http://dx.doi.org/10.6084/m9.figshare.1585847 • Project page: http://goo.gl/ZtlrY1