Peripapillary atrophy detection by sparse biologically inspired feature manifold

Jun Cheng, Dacheng Tao, Jiang Liu, Damon Wing Kee Wong, Ngan Meng Tan, Tien Yin Wong, Seang Mei Saw

Research output: Journal PublicationArticlepeer-review

24 Citations (Scopus)


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.

Original languageEnglish
Article number6298012
Pages (from-to)2355-2365
Number of pages11
JournalIEEE Transactions on Medical Imaging
Issue number12
Publication statusPublished - 2012
Externally publishedYes


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ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering


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