Ensemble Learning Optimization for Diabetic Retinopathy Image Analysis

Ensemble Learning has been proved to be an effective solution to learning problems. Its success is mainly dependent on diversity. However, diversity is rarely evaluated and explicitly used to enhance the ensemble performance. Diabetic Retinopathy (DR) automatic detection is one of the important applications to support the health care services. In this research, some existing statistical diversity measures were utilized to optimize ensembles used to detect DR related signs. Ant Colony Optimization (ACO) algorithm is adopted to select the ensemble base models using various criteria. This paper evaluates several optimized and nonoptimized ensemble structures used for vessel segmentation. The results demonstrate the necessity of adopting the ensemble learning and the advantage of ensemble optimization to support the DR related signs detection.

[1]  Frédéric Zana,et al.  A multimodal registration algorithm of eye fundus images using vessels detection and Hough transform , 1999, IEEE Transactions on Medical Imaging.

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

[3]  Yannis A. Tolias,et al.  A fuzzy vessel tracking algorithm for retinal images based on fuzzy clustering , 1998, IEEE Transactions on Medical Imaging.

[4]  Ying Sun,et al.  Recursive tracking of vascular networks in angiograms based on the detection-deletion scheme , 1993, IEEE Trans. Medical Imaging.

[5]  Xin Yao,et al.  Diversity creation methods: a survey and categorisation , 2004, Inf. Fusion.

[6]  Jonathan Goh,et al.  The reading of diabetic retinopathy images - an evolutionary approach , 2011 .

[7]  M. Preethi,et al.  Review of retinal blood vessel detection methods for automated diagnosis of Diabetic Retinopathy , 2012, IEEE-International Conference On Advances In Engineering, Science And Management (ICAESM -2012).

[8]  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.

[9]  Meindert Niemeijer.  Automatic Detection of Diabetic Retinopathy in Digital Fundus Photographs , 2006 .

[10]  Vincent Lepetit,et al.  KernelBoost: Supervised Learning of Image Features For Classification , 2013 .

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

[12]  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..

[13]  Lei Zhang,et al.  Retinal vessel extraction by matched filter with first-order derivative of Gaussian , 2010, Comput. Biol. Medicine.

[14]  M. Goldbaum,et al.  Detection of blood vessels in retinal images using two-dimensional matched filters. , 1989, IEEE transactions on medical imaging.

[15]  Anwar Mohd. Mansuri,et al.  A Review of Retinal Vessel Segmentation Techniques And Algorithms , 2011 .

[16]  Nen Huynh.  A filter bank approach to automate vessel extraction with applications , 2013 .

[17]  Elisa Ricci,et al.  Retinal Blood Vessel Segmentation Using Line Operators and Support Vector Classification , 2007, IEEE Transactions on Medical Imaging.

[18]  Lili Xu,et al.  A novel method for blood vessel detection from retinal images , 2010, Biomedical engineering online.

[19]  Evangelos Dermatas,et al.  Multi-scale retinal vessel segmentation using line tracking , 2010, Comput. Medical Imaging Graph..

[20]  Kostas Delibasis,et al.  Automatic model-based tracing algorithm for vessel segmentation and diameter estimation , 2010, Comput. Methods Programs Biomed..

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