Retinal OCT Segmentation Using Fuzzy Region Competition and Level Set Methods

Optical coherence tomography (OCT) is a noninvasive imaging modality that provides in-depth images of the retina. Properties of individual layers on OCT have become important markers for diagnosing and tracking medication of various eye diseases in current ophthalmology. Manual segmentation of OCT scans posed many challenges (errors, inconsistency), which can be addressed by automated segmentation methods. Level set method is one of the most popular methods in the literature used for this purpose. Although level set methods have a fundamental way of handling topological changes, the weak boundaries and noise in addition to inhomogeneity in OCT images make it difficult to segment the layers accurately. Inspired by the concept of region competition, we incorporate prior knowledge of the retinal structure to segment nine (9) layers of the retina. Mainly, we establish a specific region of interest, then use selected components from fuzzy C-Means for initialisation. The clustering in the initialisation stage is also used to guide the evolution through; a Mumford-Shah (MS) selective region competition force and a Hamilton-Jacobi (HJ) balloon force. The forces ensure evolution close to actual retinal boundaries. Finally, the convergence of the method is based on an improved HJ object indication function influenced by the fuzzy membership to prevent leakages at weak boundaries. Experimental results are promising based on 200 OCT images.

[1]  Tony F. Chan,et al.  Active Contours without Edges for Vector-Valued Images , 2000, J. Vis. Commun. Image Represent..

[2]  Tony F. Chan,et al.  Active contours without edges , 2001, IEEE Trans. Image Process..

[3]  Xiaohui Liu,et al.  Graph-Cut Segmentation of Retinal Layers from OCT Images , 2018, BIOIMAGING.

[4]  Hamid R. Tizhoosh,et al.  Locally Adaptive Fuzzy Image Enhancement , 1997, Fuzzy Days.

[5]  Gábor Márk Somfai,et al.  Real-Time Automatic Segmentation of Optical Coherence Tomography Volume Data of the Macular Region , 2015, PloS one.

[6]  Peter A. Calabresi,et al.  Multi-layer fast level set segmentation for macular OCT , 2018, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).

[7]  Shijian Lu,et al.  Automated layer segmentation of optical coherence tomography images , 2010, 2010 5th IEEE Conference on Industrial Electronics and Applications.

[8]  Xiaoming Liu,et al.  Semi-Supervised Automatic Segmentation of Layer and Fluid Region in Retinal Optical Coherence Tomography Images Using Adversarial Learning , 2019, IEEE Access.

[9]  Xiaohui Liu,et al.  Retinal OCT Image Segmentation Using Fuzzy Histogram Hyperbolization and Continuous Max-Flow , 2017, 2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS).

[10]  Mona Kathryn Garvin,et al.  Automated 3-D segmentation and analysis of retinal optical coherence tomography images , 2008 .

[11]  Jerry L Prince,et al.  Retinal layer segmentation of macular OCT images using boundary classification , 2013, Biomedical optics express.

[12]  J. Schuman,et al.  Optical coherence tomography. , 2000, Science.

[13]  Yue Wu,et al.  Deep-Learning Based, Automated Segmentation of Macular Edema in Optical Coherence Tomography , 2017, bioRxiv.

[14]  Sim Heng Ong,et al.  Integrating spatial fuzzy clustering with level set methods for automated medical image segmentation , 2011, Comput. Biol. Medicine.

[15]  Xiaohui Liu,et al.  Level Set Segmentation of Retinal OCT Images , 2019, BIOIMAGING.

[16]  Jelena Novosel,et al.  Loosely coupled level sets for retinal layer segmentation in optical coherence tomography , 2013, 2013 IEEE 10th International Symposium on Biomedical Imaging.

[17]  D. Mumford,et al.  Optimal approximations by piecewise smooth functions and associated variational problems , 1989 .

[18]  X Liu,et al.  Retina layer segmentation using kernel graph cuts and continuous max-flow. , 2015, Optics express.

[19]  Yue Zhao,et al.  3D Automatic Segmentation Method for Retinal Optical Coherence Tomography Volume Data Using Boundary Surface Enhancement , 2015, ArXiv.

[20]  W. Clem Karl,et al.  A fast level set method without solving PDEs [image segmentation applications] , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

[21]  Joseph A. Izatt,et al.  Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation , 2010, Optics express.

[22]  J. Sethian,et al.  Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations , 1988 .

[23]  Zidong Wang,et al.  Automated Layer Segmentation of 3D Macular Images Using Hybrid Methods , 2015, ICIG.

[24]  Edsger W. Dijkstra,et al.  A note on two problems in connexion with graphs , 1959, Numerische Mathematik.

[25]  Xuelong Li,et al.  Selective Level Set Segmentation Using Fuzzy Region Competition , 2016, IEEE Access.