Abstract：
:A confocal microscope can produce gray-scale images of the different layers of the cornea. We have addressed the problem of classifying these images, i.e. recognizing the layer displayed, using the shape of the cells contained, which is uniquely related to each specific layer. A first method was designed, based first on the binarization of the image and then on the description of the cell shape by means of Hu variables (central moments). An artificial neural network was used to classify each image according to the values assumed by these variables. A Matlab prototype of the classification system was developed, considering images of three corneal layers (Bowman membrane, stroma, endothelium) in normal subjects. The system was tested on 46 images, and good results were obtained. To avoid the critical step of binarization, an alternative cell shape description was investigated, based on Zernike moments, and a new network was developed and trained. The results achieved were better than those obtained with the previous technique, and also, no binarization was necessary.
journal_name
Comput Methods Programs Biomedjournal_title
Computer methods and programs in biomedicineauthors
Ruggeri A,Pajaro Sdoi
10.1016/s0169-2607(01)00153-5subject
Has Abstractpub_date
2002-04-01 00:00:00pages
25-35issue
1eissn
0169-2607issn
1872-7565pii
S0169260701001535journal_volume
68pub_type
杂志文章abstract：:A dose-response meta-analysis of epidemiological studies can encounter different types of confidence intervals (floated vs. conventional). This paper illustrates how to back calculate conventional confidence intervals from a set of relative risks reported with floated confidence intervals or floated standard errors. F...
journal_title：Computer methods and programs in biomedicine
pub_type： 杂志文章,meta分析
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abstract：BACKGROUND AND OBJECTIVE:For cancer detection from microscopic biopsy images, image segmentation step used for segmentation of cells and nuclei play an important role. Accuracy of segmentation approach dominate the final results. Also the microscopic biopsy images have intrinsic Poisson noise and if it is present in th...
journal_title：Computer methods and programs in biomedicine
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journal_title：Computer methods and programs in biomedicine
pub_type： 杂志文章
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journal_title：Computer methods and programs in biomedicine
pub_type： 杂志文章
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journal_title：Computer methods and programs in biomedicine
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journal_title：Computer methods and programs in biomedicine
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journal_title：Computer methods and programs in biomedicine
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journal_title：Computer methods and programs in biomedicine
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journal_title：Computer methods and programs in biomedicine
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journal_title：Computer methods and programs in biomedicine
pub_type： 杂志文章
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journal_title：Computer methods and programs in biomedicine
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journal_title：Computer methods and programs in biomedicine
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journal_title：Computer methods and programs in biomedicine
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doi：10.1016/s0169-2607(01)00132-8
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journal_title：Computer methods and programs in biomedicine
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doi：10.1016/j.cmpb.2020.105755
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journal_title：Computer methods and programs in biomedicine
pub_type： 杂志文章
doi：10.1016/j.cmpb.2005.03.001
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journal_title：Computer methods and programs in biomedicine
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journal_title：Computer methods and programs in biomedicine
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journal_title：Computer methods and programs in biomedicine
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journal_title：Computer methods and programs in biomedicine
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journal_title：Computer methods and programs in biomedicine
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journal_title：Computer methods and programs in biomedicine
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journal_title：Computer methods and programs in biomedicine
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journal_title：Computer methods and programs in biomedicine
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journal_title：Computer methods and programs in biomedicine
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journal_title：Computer methods and programs in biomedicine
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