Automated Detection of Red Lesions Using Superpixel Multichannel Multifeature

Computational and Mathematical Methods in Medicine
Wei ZhouWenyou Du

Abstract

Red lesions can be regarded as one of the earliest lesions in diabetic retinopathy (DR) and automatic detection of red lesions plays a critical role in diabetic retinopathy diagnosis. In this paper, a novel superpixel Multichannel Multifeature (MCMF) classification approach is proposed for red lesion detection. In this paper, firstly, a new candidate extraction method based on superpixel is proposed. Then, these candidates are characterized by multichannel features, as well as the contextual feature. Next, FDA classifier is introduced to classify the red lesions among the candidates. Finally, a postprocessing technique based on multiscale blood vessels detection is modified for removing nonlesions appearing as red. Experiments on publicly available DiaretDB1 database are conducted to verify the effectiveness of our proposed method.

References

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Jan 2, 2004·The New England Journal of Medicine·Robert N Frank
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Sep 10, 2008·IEEE Transactions on Medical Imaging·Gwénolé QuellecChristian Roux
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May 30, 2012·IEEE Transactions on Pattern Analysis and Machine Intelligence·Radhakrishna AchantaSabine Süsstrunk
Jun 28, 2014·Medical Image Analysis·Xiwei ZhangAli Erginay
Apr 20, 2017·Computational and Mathematical Methods in Medicine·Wei ZhouWenyou Du

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Methods Mentioned

BETA
feature extraction
PCA

Software Mentioned

Fisher Discriminant Analysis ( FDA )
Extreme
SLIC
CMYK
Windows

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