Please use this identifier to cite or link to this item:
|Title:||Peripapillary atrophy detection by sparse biologically inspired feature manifold|
please supply index terms/keywords for your paper. To download the IEEE Taxonomy go to http://www.ieee.org/documents/2009Taxonomy-v101.pdf
|Citation:||Cheng, J., Tao, D., Liu, J., Wong, D.W.K., Tan, N.-M., Wong, T.Y., Saw, S.M. (2012). Peripapillary atrophy detection by sparse biologically inspired feature manifold. IEEE Transactions on Medical Imaging 31 (12) : 2355-2365. ScholarBank@NUS Repository. https://doi.org/10.1109/TMI.2012.2218118|
|Abstract:||Peripapillary atrophy (PPA) is an atrophy of pre-existing retina tissue. Because of its association with eye diseases such as myopia and glaucoma, PPA is an important indicator for diagnosis of these diseases. Experienced ophthalmologists are able to determine the presence of PPA using visual information from the retinal images. However, it is tedious, time consuming and subjective to examine all images especially in a screening program. This paper presents biologically inspired feature (BIF) for the automatic detection of PPA. BIF mimics the process of cortex for visual perception. In the proposed method, a focal region is segmented from the retinal image and the BIF is extracted. As BIF is an intrinsically low dimensional feature embedded in a high dimensional space, it is not suitable to measure the similarity between two BIFs directly based on the Euclidean distance. Therefore, it is necessary to obtain a suitable mapping to reduce the dimensionality. In this paper, we explore sparse transfer learning to transfer the label information from ophthalmologists to the sample distribution knowledge contained in all samples. Selective pair-wise discriminant analysis is used to define two strategies of sparse transfer learning: negative and positive sparse transfer learning. Experimental results show that negative sparse transfer learning is superior to the positive one for this task. The proposed BIF based approach achieves an accuracy of more than 90% in detecting PPA, much better than previous methods. It can be used to save the workload of ophthalmologists and thus reduce the diagnosis costs. © 2012 IEEE.|
|Source Title:||IEEE Transactions on Medical Imaging|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Mar 18, 2019
WEB OF SCIENCETM
checked on Mar 18, 2019
checked on Mar 1, 2019
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.