Abstract：
:Cataract is the clouding of lens, which affects vision and it is the leading cause of blindness in the world's population. Accurate and convenient cataract detection and cataract severity evaluation will improve the situation. Automatic cataract detection and grading methods are proposed in this paper. With prior knowledge, the improved Haar features and visible structure features are combined as features, and multilayer perceptron with discrete state transition (DST-MLP) or exponential DST (EDST-MLP) are designed as classifiers. Without prior knowledge, residual neural networks with DST (DST-ResNet) or EDST (EDST-ResNet) are proposed. Whether with prior knowledge or not, our proposed DST and EDST strategy can prevent overfitting and reduce storage memory during network training and implementation, and neural networks with these strategies achieve state-of-the-art accuracy in cataract detection and grading. The experimental results indicate that combined features always achieve better performance than a single type of feature, and classification methods with feature extraction based on prior knowledge are more suitable for complicated medical image classification task. These analyses can provide constructive advice for other medical image processing applications.
journal_name
IEEE Trans Med Imagingjournal_title
IEEE transactions on medical imagingauthors
Zhou Y,Li G,Li Hdoi
10.1109/TMI.2019.2928229subject
Has Abstractpub_date
2020-02-01 00:00:00pages
436-446issue
2eissn
0278-0062issn
1558-254Xjournal_volume
39pub_type
杂志文章abstract：:The quality and quantitative accuracy of iteratively reconstructed SPECT images improves when better point spread function (PSF) models of the gamma camera are used during reconstruction. Here, inclusion in the PSF model of photon crosstalk between different slices caused by limited gamma camera resolution and scatter...
journal_title：IEEE transactions on medical imaging
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doi：10.1109/42.511752
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journal_title：IEEE transactions on medical imaging
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journal_title：IEEE transactions on medical imaging
pub_type： 杂志文章
doi：10.1109/42.796281
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journal_title：IEEE transactions on medical imaging
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journal_title：IEEE transactions on medical imaging
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journal_title：IEEE transactions on medical imaging
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journal_title：IEEE transactions on medical imaging
pub_type： 杂志文章
doi：10.1109/TMI.2013.2284966
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journal_title：IEEE transactions on medical imaging
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journal_title：IEEE transactions on medical imaging
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doi：10.1109/TMI.2016.2529500
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pub_type： 杂志文章
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journal_title：IEEE transactions on medical imaging
pub_type： 杂志文章
doi：10.1109/42.887834
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journal_title：IEEE transactions on medical imaging
pub_type： 杂志文章
doi：10.1109/42.293916
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journal_title：IEEE transactions on medical imaging
pub_type： 杂志文章
doi：10.1109/42.746626
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journal_title：IEEE transactions on medical imaging
pub_type： 杂志文章
doi：10.1109/42.222665
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journal_title：IEEE transactions on medical imaging
pub_type： 杂志文章
doi：10.1109/TMI.2016.2640180
更新日期：2017-04-01 00:00:00
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journal_title：IEEE transactions on medical imaging
pub_type： 杂志文章
doi：10.1109/TMI.2004.832656
更新日期：2004-10-01 00:00:00
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journal_title：IEEE transactions on medical imaging
pub_type： 杂志文章
doi：10.1109/TMI.1982.4307550
更新日期：1982-01-01 00:00:00
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journal_title：IEEE transactions on medical imaging
pub_type： 杂志文章
doi：10.1109/TMI.2002.808367
更新日期：2003-02-01 00:00:00
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journal_title：IEEE transactions on medical imaging
pub_type： 杂志文章
doi：10.1109/TMI.2007.900322
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journal_title：IEEE transactions on medical imaging
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doi：10.1109/TMI.2017.2774814
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journal_title：IEEE transactions on medical imaging
pub_type： 杂志文章
doi：10.1109/42.14520
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journal_title：IEEE transactions on medical imaging
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journal_title：IEEE transactions on medical imaging
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journal_title：IEEE transactions on medical imaging
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doi：10.1109/42.310886
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journal_title：IEEE transactions on medical imaging
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doi：10.1109/TMI.2002.801171
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journal_title：IEEE transactions on medical imaging
pub_type： 杂志文章
doi：10.1109/42.57761
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journal_title：IEEE transactions on medical imaging
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doi：10.1109/42.192684
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journal_title：IEEE transactions on medical imaging
pub_type： 杂志文章
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