Multiscale AM-FM methods for diabetic retinopathy lesion detection

Abstract

In this paper, we propose the use of multiscale amplitude-modulation-frequency-modulation (AM-FM) methods for discriminating between normal and pathological retinal images. The method presented in this paper is tested using standard images from the early treatment diabetic retinopathy study. We use 120 regions of 40 x 40 pixels containing four types of lesions commonly associated with diabetic retinopathy (DR) and two types of normal retinal regions that were manually selected by a trained analyst. The region types included microaneurysms, exudates, neovascularization on the retina, hemorrhages, normal retinal background, and normal vessels patterns. The cumulative distribution functions of the instantaneous amplitude, the instantaneous frequency magnitude, and the relative instantaneous frequency angle from multiple scales are used as texture feature vectors. We use distance metrics between the extracted feature vectors to measure interstructure similarity. Our results demonstrate a statistical differentiation of normal retinal structures and pathological lesions based on AM-FM features. We further demonstrate our AM-FM methodology by applying it to classification of retinal images from the MESSIDOR database. Overall, the proposed methodology shows significant capability for use in automatic DR screening.

Figures

Fig. 1

(a)Image from the ETDRS standard database. Lesions encased in the boxes are examples of A) Neovascularization, B) Cottonwool spots, C) Hemorrhages, D) Exudates, and E) Microaneurysms; (b) Examples of retinal structures on ROIs of 40 × 40 pixels.

Fig. 2

Filterbank for Multi-Scale AM-FM Decomposition. The discretespectrum is decomposed using 25 bandpass filters. Each scale (see Table 1).

Fig. 3

(a) Original Image from ETDRS; (b) Instantaneous Amplitude using medium, low and very low frequencies; and (c) Thresholded Image of (b).

Fig. 4

(a) Original Image from ETDRS; (b) Instantaneous Amplitude using low frequencies; and (c) Thresholded Image of (b).

Fig. 5

(a) Original Image from ETDRS; (b) Instantaneous Frequency Magnitude using low pass filter.

Fig. 6

Procedure to find the Mahalanobis distance between lesions for each estimate and each CoS. First the features for the regions are extracted per estimate (IA, |IF| and relative angle). Then a reduction of dimensionality method (PCA) is applied for each feature estimate of the regions. After that, the mean of 20 regions corresponding to a specific lesion is found. Using the information of the 6 means, the Mahalanobis distance is found for each estimate. This process is repeated for each CoS given as a result 27 different distances between lesions.

Fig. 7

Procedure to classify retinal images. First the features are extracted using AM_FM. Then a reduction of dimensionality method (PCA) is applied for each CoS. After that an unsupervised method called hierarchical clustering is applied in order to reduce the dimensionality. Finally, the PLS is applied to obtain the estimated class for each image.

Fig. 8

Comparison of the mean of the CDFs between Microaneurysm (MA) and Retinal Background (RB). (a) CDFs of the IA for low and very low frequencies, (b) CDFs of the |IF| for low and very low frequencies.

Fig. 9

(a)Comparison of the mean of the IA CDFs between Microaneurysm (MA) and Hemorrhage (HE) for the low pass filter, (b) Comparison of the mean of the angle CDFs between Microaneurysm (MA) and Vessels (VE) for medium, low and very low frequencies.

Fig. 10

Comparison of the mean of the CDFs between Exudates (EX) and Retinal Background (RB). (a) CDFs of the IA for medium and low frequencies, (b) CDFs of the |IF| for medium and low frequencies,

Fig. 11

(a)Comparison of the mean of the IA CDFs between Neovascularization (NV) and Retinal Background(RB) for medium, low and very low frequencies, (b) Comparison of the mean of the |IF| CDFs between Neovascularization (NV) and Vessels (VE) for medium, low and very low frequencies.

Fig. 12

Comparison of the mean of the CDFs between Neovascularization (NV) and Hemorrhage (HE). (a) CDFs of the IA for medium, low and very low frequencies, (b) CDFs of the θ for low frequencies.

Fig. 13

ROC curve for the classification of: Risk 3,2 and 1 vs. Risk 0. Area under the ROC = 0,84. A best sensitivity/specificity of 92%/54% was obtained.

Fig. 14

ROC curve for the classification of the third experiment: IR vs. Risk 0. A best sensitivity/specificity of 100%/88% was obtained.