Detection and phenotyping of retinal disease using AM-FM processing for feature extraction

We present the application of an Amplitude-Modulation Frequency-Modulation (AM-FM) method for extracting potentially relevant features towards the classification of diseased retinas from healthy retinas. In terms of AM-FM features, we use histograms of the instantaneous amplitude, the angle of the instantaneous frequency and the magnitude of the instantaneous frequency extracted over different frequency scales. To classify the AM-FM features, we use a combination of a clustering method and Partial Least Squares (PLS). Using 18 images from each of the four risk levels, three experiments were performed to test the algorithm's ability to differentiate the controls (Risk 0) from each of the three levels of pathology, i.e. Risk 1, Risk 2, and Risk 3. For Risk 0 versus Risk 3 an area under the receiver operating system (AROC) of 0.99 was achieved with a best sensitivity of 100% and a specificity of 95%. For Risk 0 versus Risk 2, the AROC was 0.96 with 94% sensitivity and 85% specificity. For Risk 0 versus Risk 1, the AROC was 0.93 and a sensitivity/specificity of 94%/67%.

[1]  M. Abràmoff,et al.  Web-based screening for diabetic retinopathy in a primary care population: the EyeCheck project. , 2005, Telemedicine journal and e-health : the official journal of the American Telemedicine Association.

[2]  B. Thomas,et al.  Automated identification of diabetic retinal exudates in digital colour images , 2003, The British journal of ophthalmology.

[3]  Al-RawiMohammed,et al.  An improved matched filter for blood vessel detection of digital retinal images , 2007 .

[4]  Marios S. Pattichis,et al.  Ieee Transactions on Pattern Analysis and Machine Intelligence 1 Analyzing Image Structure by Multidimensional Frequency Modulation Ieee Transactions on Pattern Analysis and Machine Intelligence 2 , 2006 .

[5]  C. Pattichis,et al.  An AM-FM model for Motion Estimation in Atherosclerotic Plaque Videos , 2007, 2007 Conference Record of the Forty-First Asilomar Conference on Signals, Systems and Computers.

[6]  Gwénolé Quellec,et al.  Optimal Wavelet Transform for the Detection of Microaneurysms in Retina Photographs , 2008, IEEE Transactions on Medical Imaging.

[7]  P F Sharp,et al.  An image-processing strategy for the segmentation and quantification of microaneurysms in fluorescein angiograms of the ocular fundus. , 1996, Computers and biomedical research, an international journal.

[8]  B. van Ginneken,et al.  Automated detection and differentiation of drusen, exudates, and cotton-wool spots in digital color fundus photographs for diabetic retinopathy diagnosis. , 2007, Investigative ophthalmology & visual science.

[9]  Bram van Ginneken,et al.  Automatic detection of red lesions in digital color fundus photographs , 2005, IEEE Transactions on Medical Imaging.

[10]  M. Cree,et al.  A comparison of computer based classification methods applied to the detection of microaneurysms in ophthalmic fluorescein angiograms , 1998, Comput. Biol. Medicine.

[11]  Marios S. Pattichis,et al.  AM-FM texture segmentation in electron microscopic muscle imaging , 1999, IEEE Transactions on Medical Imaging.

[12]  M.S. Pattichis,et al.  M-mode echocardiography image and video segmentation based on am-fm demodulation techniques , 2003, Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439).

[13]  Thomas Walter,et al.  Segmentation of Color Fundus Images of the Human Retina: Detection of the Optic Disc and the Vascular Tree Using Morphological Techniques , 2001, ISMDA.

[14]  Victor Manuel,et al.  AM-FM methods for image and video processing , 2009 .

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

[16]  M. Larsen,et al.  Automated detection of fundus photographic red lesions in diabetic retinopathy. , 2003, Investigative ophthalmology & visual science.

[17]  J. Olson,et al.  Automated detection of exudates for diabetic retinopathy screening , 2007, Physics in medicine and biology.

[18]  P. Soliz,et al.  New AM-FM analysis methods for retinal image characterization , 2008, 2008 42nd Asilomar Conference on Signals, Systems and Computers.

[19]  K. Namuduri,et al.  Automated detection and classification of vascular abnormalities in diabetic retinopathy , 2004, Conference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers, 2004..

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

[21]  Mohammed Al-Rawi,et al.  An improved matched filter for blood vessel detection of digital retinal images , 2007, Comput. Biol. Medicine.