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.
journal_name
IEEE Trans Med Imagingjournal_title
IEEE transactions on medical imagingauthors
Tang L,Niemeijer M,Reinhardt JM,Garvin MK,Abràmoff MDdoi
10.1109/TMI.2012.2227119subject
Has Abstractpub_date
2013-02-01 00:00:00pages
364-75issue
2eissn
0278-0062issn
1558-254Xjournal_volume
32pub_type
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