Splat feature classification with application to retinal hemorrhage detection in fundus images

Li Tang; Meindert Niemeijer; Joseph M Reinhardt; Mona K Garvin; Michael D Abràmoff

doi:10.1109/TMI.2012.2227119

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Splat feature classification with application to retinal hemorrhage detection in fundus images

Journal article

Open access

Peer reviewed

Splat feature classification with application to retinal hemorrhage detection in fundus images

Li Tang, Meindert Niemeijer, Joseph M Reinhardt, Mona K Garvin and Michael D Abràmoff

IEEE transactions on medical imaging, Vol.32(2), pp.364-375

02/2013

DOI: 10.1109/TMI.2012.2227119

PMID: 23193310

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Published (Version of record) Open Access

Abstract

A novel splat feature classification method is presented with application to retinal hemorrhage detection in fundus images. Reliable detection of retinal hemorrhages is important in the development of automated screening systems which can be translated into practice. Under our supervised approach, retinal color images are partitioned into nonoverlapping segments covering the entire image. Each segment, i.e., splat, contains pixels with similar color and spatial location. A set of features is extracted from each splat to describe its characteristics relative to its surroundings, employing responses from a variety of filter bank, interactions with neighboring splats, and shape and texture information. An optimal subset of splat features is selected by a filter approach followed by a wrapper approach. A classifier is trained with splat-based expert annotations and evaluated on the publicly available Messidor dataset. An area under the receiver operating characteristic curve of 0.96 is achieved at the splat level and 0.87 at the image level. While we are focused on retinal hemorrhage detection, our approach has potential to be applied to other object detection tasks.

Algorithms

Retinal Vessels - pathology

Reproducibility of Results

Retinal Hemorrhage - pathology

Humans

Image Interpretation, Computer-Assisted - methods

Sensitivity and Specificity

Image Enhancement - methods

Retinoscopy - methods

Pattern Recognition, Automated - methods

Details

Title: Subtitle: Splat feature classification with application to retinal hemorrhage detection in fundus images
Creators: Li Tang - Department of Ophthalmology and Visual Sciences, University of Iowa, Iowa City, IA 52242, USA. li-tang-1@uiowa.edu
Meindert Niemeijer
Joseph M Reinhardt
Mona K Garvin
Michael D Abràmoff
Resource Type: Journal article
Publication Details: IEEE transactions on medical imaging, Vol.32(2), pp.364-375
DOI: 10.1109/TMI.2012.2227119
PMID: 23193310
NLM abbreviation: IEEE Trans Med Imaging
ISSN: 0278-0062
eISSN: 1558-254X
Publisher: Institute of Electrical and Electronics Engineers; United States
Grant note: EY017066 / NEI NIH HHS
Language: English
Date published: 02/2013
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Radiology; Electrical and Computer Engineering; Ophthalmology and Visual Sciences
Record Identifier: 9983806388702771

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