Multi-proportion channel ensemble model for retinal vessel segmentation

OBJECTIVE A novel supervised method that is based on the Multi-Proportion Channel Ensemble Model (MPC-EM) is proposed to obtain more vessel details with reduced computational complexity. METHODS Existing Retinal Vessel Segmentation (RVS) algorithms only work using the single G channel (Green Channel) of fundus images because that channel normally contains the most details with the least noise, while the red and blue channels are usually saturated and noisy. However, we find that the images that are composed of the αG-channel and (1-α) R-channel (Red Channel) with different values of α produce multiple particular global features. This enables the model to detect more local vessel details in fundus images. Therefore, we provide a detailed description and evaluation of the segmentation approach based on the MPC-EM for the RVS. The segmentation approach consists of five identical submodels. Each submodel can capture various vessel details by being trained using different composition images. These probabilistic maps that are produced by five submodels are averaged to achieve the final refined segmentation results. RESULTS The proposed approach is evaluated using 4 well-established datasets, i.e., DRIVE, STARE, HRF and CHASE_DB1, with accuracies of 95.74%, 96.95%, 96.31%, and 96.54%, respectively. Additionally, quantitative comparisons with other existing methods and cross-training results are included. CONCLUSION The segmentation results showed that the proposed algorithm based on the MPC-EM with simple submodels can achieve state-of-the-art accuracy with reduced computational complexity. SIGNIFICANCE Compared with other existing methods that are trained using only the G channel and raw images, the proposed approach based on the MPC-EM, submodels of which are trained using different proportional compositions of R and G channels, obtains better segmentation accuracy and robustness. Additionally, the experimental results show that the R channel of fundus images can also produce performance gains for RVS.

[1]  Tianfu Wang,et al.  A Cross-Modality Learning Approach for Vessel Segmentation in Retinal Images , 2016, IEEE Transactions on Medical Imaging.

[2]  Max A. Viergever,et al.  Ridge-based vessel segmentation in color images of the retina , 2004, IEEE Transactions on Medical Imaging.

[3]  A.D. Hoover,et al.  Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response , 2000, IEEE Transactions on Medical Imaging.

[4]  Roberto Marcondes Cesar Junior,et al.  Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification , 2005, IEEE Transactions on Medical Imaging.

[5]  Bram van Ginneken,et al.  Comparative study of retinal vessel segmentation methods on a new publicly available database , 2004, SPIE Medical Imaging.

[6]  Xiaoyi Jiang,et al.  Adaptive Local Thresholding by Verification-Based Multithreshold Probing with Application to Vessel Detection in Retinal Images , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Francis K. H. Quek,et al.  A review of vessel extraction techniques and algorithms , 2004, CSUR.

[8]  Temitope Mapayi,et al.  Adaptive Thresholding Technique for Retinal Vessel Segmentation Based on GLCM-Energy Information , 2015, Comput. Math. Methods Medicine.

[9]  Elli Angelopoulou,et al.  Retinal vessel segmentation by improved matched filtering: evaluation on a new high-resolution fundus image database , 2013, IET Image Process..

[10]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[11]  M. Sonka,et al.  Retinal Imaging and Image Analysis. , 2010, IEEE transactions on medical imaging.

[12]  Frédéric Zana,et al.  Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation , 2001, IEEE Trans. Image Process..

[13]  Shankar M. Krishnan,et al.  Detection and measurement of retinal vessels in fundus images using amplitude modified second-order Gaussian filter , 2002, IEEE Transactions on Biomedical Engineering.

[14]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[15]  Ali Mahlooji Far,et al.  Retinal Image Analysis Using Curvelet Transform and Multistructure Elements Morphology by Reconstruction , 2011, IEEE Transactions on Biomedical Engineering.

[16]  Oerip S. Santoso,et al.  Color retinal image enhancement using CLAHE , 2013, International Conference on ICT for Smart Society.

[17]  Ana Maria Mendonça,et al.  Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction , 2006, IEEE Transactions on Medical Imaging.

[18]  Krzysztof Krawiec,et al.  Segmenting Retinal Blood Vessels With Deep Neural Networks , 2016, IEEE Transactions on Medical Imaging.

[19]  Hong Yan,et al.  A Novel Vessel Segmentation Algorithm for Pathological Retina Images Based on the Divergence of Vector Fields , 2008, IEEE Transactions on Medical Imaging.

[20]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[21]  Bunyarit Uyyanonvara,et al.  Blood vessel segmentation methodologies in retinal images - A survey , 2012, Comput. Methods Programs Biomed..

[22]  Matthew B. Blaschko,et al.  A Discriminatively Trained Fully Connected Conditional Random Field Model for Blood Vessel Segmentation in Fundus Images , 2017, IEEE Transactions on Biomedical Engineering.

[23]  Boris Polyak,et al.  Acceleration of stochastic approximation by averaging , 1992 .

[24]  Sonam Singh,et al.  A fully convolutional neural network based structured prediction approach towards the retinal vessel segmentation , 2016, 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017).

[25]  George Azzopardi,et al.  Trainable COSFIRE filters for vessel delineation with application to retinal images , 2015, Medical Image Anal..

[26]  Alireza Osareh,et al.  AUTOMATIC BLOOD VESSEL SEGMENTATION IN COLOR IMAGES OF RETINA , 2009 .

[27]  Yanning Zhang,et al.  Multiscale Network Followed Network Model for Retinal Vessel Segmentation , 2018, MICCAI.

[28]  Vasileios Megalooikonomou,et al.  Discriminative vessel segmentation in retinal images by fusing context-aware hybrid features , 2014, Machine Vision and Applications.

[29]  Yoshua. Bengio,et al.  Learning Deep Architectures for AI , 2007, Found. Trends Mach. Learn..

[30]  Bunyarit Uyyanonvara,et al.  An Ensemble Classification-Based Approach Applied to Retinal Blood Vessel Segmentation , 2012, IEEE Transactions on Biomedical Engineering.

[31]  José Manuel Bravo,et al.  A New Supervised Method for Blood Vessel Segmentation in Retinal Images by Using Gray-Level and Moment Invariants-Based Features , 2011, IEEE Transactions on Medical Imaging.

[32]  Paul Y. S. Cheung,et al.  Vessel Extraction Under Non-Uniform Illumination: A Level Set Approach , 2008, IEEE Transactions on Biomedical Engineering.

[33]  Alan Wee-Chung Liew,et al.  General Retinal Vessel Segmentation Using Regularization-Based Multiconcavity Modeling , 2010, IEEE Transactions on Medical Imaging.