Tagged: Image Processing

Automatic Segmentation of Drusen in Fundus Image Using Non–Negative Matrix Factorization

Purpose: : The segmentation and quantitation of drusen is important for diagnosis and monitoring of retinal disease, particularly age–related macular degeneration (AMD). Many approaches to drusen segmentation have been based on heuristics, such as morphological analysis, image thresholding, etc. and require significant parameter tuning. Here we describe an unsupervised method for segmenting drusen in fundus images. Methods: : Color fundus images were acquired using the Topcon50EX fundus camera and digitized on the Nikon 2000 Coolscan. Two sets of images were considered a) leveled (using a published quadratic and spline model of green (G) channel background (R. T. Smith, Arch. Ophthalmol., 2005; 123:200–6.)) and b) unleveled. A “gold–standard” was constructed by manual segmentation of the drusen by a retinal specialist. Data from the three channels (R,G,B) were processed using non–negative matrix factorization (NMF, D. Lee, NIPS, 2001; 13:556–62). NMF decomposes a multi–variant data set (X) into two matrices: a matrix of spectral signatures (S) and their corresponding spatial distribution (A). The spatial distribution matrix (A) was analyzed using K–means clustering to label all pixels in the image into one of three classes. The entire method is unsupervised and does not require manual intervention. This method has been previously demonstrated for use in NMR based metabolomics (S. Du, Proc. EMBS, Sept., 2005). Results: : Visual inspection of the labellings produced by the algorithm tended to correspond to drusen, blood vessels and normal retinal tissue. Comparison of segmentation with the manually defined gold standard showed a range of sensitivity for detection of drusen (leveled data 51%–88%, unleveled 70–75%), with a specificity of (leveled data 78–97%, unleveled 71–97%) across four cases. Interestingly, in many cases false negatives produced by the algorithm were along the borders of the gold–standard defined drusen, indicating that individual drusen were detected by the algorithm, though their size was underestimated. Conclusions: : The NMF method is able to recover spectral signatures of drusen, as well as other anatomical structures in the retina, using only the three bands in color fundus images. Related work of our group is exploring the use of hyperspectral imaging which provides richer spectral signatures and is in fact even better suited for an NMF–based decomposition/segmentation.

Multi-resolution neural networks for mammographic mass detection

We have previously presented a hierarchical pyramid/neural network (HPNN) architecture which combines multi-scale image processing techniques with neural networks. This coarse-to- fine HPNN was designed to learn large-scale context information for detecting small objects. We have developed a similar architecture to detect mammographic masses (malignant tumors). Since masses are large, extended objects, the coarse-to-fine HPNN architecture is not suitable for the problem. Instead we constructed a fine-to- coarse HPNN architecture which is designed to learn small- scale detail structure associated with the extended objects. Our initial result applying the fine-to-coarse HPNN to mass detection are encouraging, with detection performance improvements of about 30%. We conclude that the ability of the HPNN architecture to integrate information across scales, from fine to coarse in the case of masses, makes it well suited for detecting objects which may have detail structure occurring at scales other than the natural scale of the object.