Multi-scale AM-FM for lesion phenotyping on age-related macular degeneration

Abstract

Age-related macular degeneration (AMD) is the most common cause of visual loss in the United States and is a growing public health problem. The presence and severity of AMD in current epidemiological studies is detected by the grading of color stereoscopic fundus photographs. The purpose of this study was to show that a mathematical technique, amplitude-modulation frequency modulation (AM-FM) can be used to generate multi-scale features for classifying pathological structures, such as drusen, on a retinal image. AM-FM features were calculated for N=120 40x40 regions from 5 retinal images presenting with age-related macular degeneration. The results show that with this technique, drusen can be differenced from normal retinal structures by more than three standard deviations using the AM-FM histograms. In addition, by using different color spaces highly accurate classification of structures of the retina is achieved. These results are the first step in the development of an automated AMD grading system.

Adaptive Super-Candidate Based Approach for Detection and Classification of Drusen on Retinal Fundus Images

“…5(b). The optimal performance points occur at [5] drusen detection -, 23 Sens, Spec -> 96% except 1 image (88%) Brandon et al [2] drusen detection STARE,113 Sens -86%, Spec -93%, Acc -87% discrimination (size) STARE, 113 Acc -71% Mora et al [6] drusen detection -, 22 Sens -74%, Spec -94% discrimination (area) -, 22 Sens -63%, Spec -96% Smith et al [1] discrimination (area) -, 358 Sens -87.3%, Spec -95.3% Wong et al [11] drusen detection THALIA,350 Precision -95.46 ± 0.94 Kose et al [7] discrimination ( [12] drusen detection CAPT Sens -82%, Spec -75%, Acc -80% Barriga et al [8] HD/SD discrimination -, 5 Acc -96% for 120 patches (5 images) Proposed method drusen detection STARE, ARIA, 214 Sens -80%, Spec -90%; Sens -95%, Spec -70%,AUC -0.9257 HD/SD discrimination STARE, ARIA, 214 Sens -80%, Spec -92%; Sens -95%, Spec -70%, AUC -0.915 OP DET1 (sens−80%, spec−90%), OP DET2 (sens−95%, spec−70%) for drusen detection at the confidence score threshold value of 0.4, 0.25 and OP DISC1 (sens − 80%, spec − 92%), OP DISC1 (sens − 95%, spec − 70%) for HD and SD discrim- Table 1, our method gives good performance for drusen detection and discrimination.…”

“…However, reliability of size and area measurement is limited by factors such as angle of acquisition (magnification), inaccurate segmentation and clumped drusen being misclassified as isolated drusen. Apart from drusen size, discrimination between hard and soft drusen using texture based AM-FM features has been done on localized 40×40 patches [8].…”

Image Classification for Age-related Macular Degeneration Screening Using Hierarchical Image Decompositions and Graph Mining

“…For comparison we also used the leave-one-out testing method used by them. The reported results in [2] were generated using the drusen classification technique described in [1]. For this experiment, the proposed approach utilised the Naïve Bayes classifier with σ = 10% and λ = 20% (K = 3671).…”

“…The earliest work [24] used a morphological mechanism to localise drusen. Other image processing techniques that have been applied include: (i) histogram-based adaptive local thresholding [22] , (ii) region growing [19,20]; (iii) wavelet based feature identification coupled with multilevel classification [3]; (iv) anomaly detection based approaches, that employ Support Vector Data Description (SVDD), to segment anomalous pixels [11]; and (v) signal based approaches, namely amplitude-modulation frequency-modulation (AM-FM), to generate multi-scale features for drusen classification [1,2]. Content-Based Image Retrieval (CBIR) techniques have also been applied.…”

ACHIKO-D350: A dataset for early AMD detection and drusen segmentation

“…The second category consists of the local threshold based methods, e.g., Histogram based Adaptive Local Thresholding (HALT) [9], and Otsu method based adaptive threshold [10]. The third category includes the ones from frequency domain, e.g., wavelet [3], Fourier transform [12], and amplitude-modulation frequency modulation (AM-FM) [1].…”