A reproducibility-based evaluation procedure for quantifying the differences between MS/MS peak intensity normalization methods

One of the factors that affects the detection system or face recognition is lighting. Image color processing can help the face recognition system in poor lighting conditions. In this study, homomorphic filtering and intensity normalization methods used to help improve the accuracy of face image detection. The experimental results show that the non-uniform of the illumination of the face image can be uniformed using the intensity normalization method with the average value of Peak Signal to Noise Ratio (PSNR) obtained from the whole experiment is 22.05314 and the average Absolute Mean Brightness Error (AMBE) value obtained is 6.147787. The results showed that homomorphic filtering and intensity normalization methods can be used to improve the detection accuracy of a face image.

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Correction: Comparison between Different Intensity Normalization Methods in 123I-Ioflupane Imaging for the Automatic Detection of Parkinsonism

PLoS ONE ◽

10.1371/journal.pone.0135107 ◽

2015 ◽

Vol 10 (7) ◽

pp. e0135107 ◽

Cited By ~ 3

Author(s):

A. Brahim ◽

J. Ramírez ◽

J. M. Górriz ◽

L. Khedher ◽

D. Salas-Gonzalez

Keyword(s):

Automatic Detection ◽

Normalization Methods ◽

Intensity Normalization

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Quantitative Histopathological Analysis of Cervical Intra-Epithelial Neoplasia Sections: Methodological Issues

Analytical Cellular Pathology ◽

10.1155/2004/238769 ◽

2004 ◽

Vol 26 (1-2) ◽

pp. 31-43

Author(s):

Martial Guillaud ◽

Dennis Cox ◽

Anais Malpica ◽

Gregg Staerkel ◽

Jasenka Matisic ◽

...

Keyword(s):

Intermediate Layer ◽

Staining Intensity ◽

Diagnostic Information ◽

Discriminatory Power ◽

Histopathological Analysis ◽

Observer Variability ◽

Methodological Issues ◽

Normalization Methods ◽

Inter Observer Variability ◽

Intensity Normalization

Objectives: As part a Program Project to evaluate emerging optical technologies for cervical neoplasia, our group is performing quantitative histopathological analysis of biopsies from 1800 patients. Several methodological issues have arisen with respect to this analysis: (1) Finding the most efficient way to compensate for staining intensity variation with out losing diagnostic information; (2) Assessing the inter‐ and intra‐observer variability of the semi‐interactive data collection; and (3) the use of non‐overlapping cells from the intermediate layer only. Methods: Non‐overlapping quantitatively stained nuclei were selected from 280 samples with histopathological characteristics of normal (199), koilocytosis (37), CIN 1 (18), CIN 2 (10) and CIN 3 (16). Linear discriminant analysis was used to assess the diagnostic information in three different feature sets to evaluate and compare staining intensity normalization methods. Selected feature values and summary scores were used to evaluate intra‐ and inter‐observer variability. Results: The features normalized by the internal subset of the imaged cells had the same discriminatory power as those normalized by the control cells and by both normalization methods seem to have additional discriminatory power over the set of features which do not require normalization. The use of the internal subset decreased the image acquisition time by ∼50% at each center, respectively. The intra‐ and inter‐observer variability was of a similar size. Good performance was obtained by measuring the intermediate layer only. Conclusion: The use of intensity normalization from a subset of the imaged non‐overlapping intermediate layer cells works as well as or better than any of the other methods tested and provides a significant timesaving. Our intra‐ and inter‐observer variability do not seem to affect the diagnostic power of the data. Although this must be tested in a larger data set, the use of intermediate layer cells only may be acceptable when using quantitative histopathology.

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Comparison between Different Intensity Normalization Methods in 123I-Ioflupane Imaging for the Automatic Detection of Parkinsonism

PLoS ONE ◽

10.1371/journal.pone.0130274 ◽

2015 ◽

Vol 10 (6) ◽

pp. e0130274 ◽

Cited By ~ 10

Author(s):

A. Brahim ◽

J. Ramírez ◽

J. M. Górriz ◽

L. Khedher ◽

D. Salas-Gonzalez

Keyword(s):

Automatic Detection ◽

Normalization Methods ◽

Intensity Normalization

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Intensity normalization methods in brain FDG-PET quantification

NeuroImage ◽

10.1016/j.neuroimage.2020.117229 ◽

2020 ◽

Vol 222 ◽

pp. 117229 ◽

Cited By ~ 2

Author(s):

Francisco J. López-González ◽

Jesús Silva-Rodríguez ◽

José Paredes-Pacheco ◽

Aida Niñerola-Baizán ◽

Nikos Efthimiou ◽

...

Keyword(s):

Fdg Pet ◽

Pet Quantification ◽

Normalization Methods ◽

Intensity Normalization

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FT‐ICR‐MS Peak Intensity Normalization for Complex Mixture Analyses

Rapid Communications in Mass Spectrometry ◽

10.1002/rcm.9068 ◽

2021 ◽

Author(s):

Allison M. Thompson ◽

Kelly G. Stratton ◽

Lisa M. Bramer ◽

Nicole S. Zavoshy ◽

Lee Ann McCue

Keyword(s):

Complex Mixture ◽

Peak Intensity ◽

Intensity Normalization ◽

Ft Icr Ms ◽

Ft Icr

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Impact of Preprocessing and Harmonization Methods on the Removal of Scanner Effects in Brain MRI Radiomic Features

Cancers ◽

10.3390/cancers13123000 ◽

2021 ◽

Vol 13 (12) ◽

pp. 3000

Author(s):

Yingping Li ◽

Samy Ammari ◽

Corinne Balleyguier ◽

Nathalie Lassau ◽

Emilie Chouzenoux

Keyword(s):

Brain Tumors ◽

Healthy Volunteers ◽

Image Acquisition ◽

Brain Mri ◽

Bias Field ◽

Bias Field Correction ◽

Normalization Methods ◽

Intensity Normalization

In brain MRI radiomics studies, the non-biological variations introduced by different image acquisition settings, namely scanner effects, affect the reliability and reproducibility of the radiomics results. This paper assesses how the preprocessing methods (including N4 bias field correction and image resampling) and the harmonization methods (either the six intensity normalization methods working on brain MRI images or the ComBat method working on radiomic features) help to remove the scanner effects and improve the radiomic feature reproducibility in brain MRI radiomics. The analyses were based on in vitro datasets (homogeneous and heterogeneous phantom data) and in vivo datasets (brain MRI images collected from healthy volunteers and clinical patients with brain tumors). The results show that the ComBat method is essential and vital to remove scanner effects in brain MRI radiomic studies. Moreover, the intensity normalization methods, while not able to remove scanner effects at the radiomic feature level, still yield more comparable MRI images and improve the robustness of the harmonized features to the choice among ComBat implementations.

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Resolution Attainable With the Present Day STEM

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100071594 ◽

1973 ◽

Vol 31 ◽

pp. 230-231 ◽

Cited By ~ 1

Author(s):

J. S. Wall ◽

J. P. Langmore ◽

H. Isaacson ◽

A. V. Crewe

Keyword(s):

Spherical Aberration ◽

Focal Length ◽

Incident Beam ◽

Scanning Transmission Electron Microscope ◽

Aperture Size ◽

Magnetic Lens ◽

Peak Intensity ◽

Transmission Electron ◽

Scanning Transmission ◽

Half Angle

The scanning transmission electron microscope (STEM) constructed by the authors employs a field emission gun and a 1.15 mm focal length magnetic lens to produce a probe on the specimen. The aperture size is chosen to allow one wavelength of spherical aberration at the edge of the objective aperture. Under these conditions the profile of the focused spot is expected to be similar to an Airy intensity distribution with the first zero at the same point but with a peak intensity 80 per cent of that which would be obtained If the lens had no aberration. This condition is attained when the half angle that the incident beam subtends at the specimen, 𝛂 = (4𝛌/Cs)¼

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An Analysis of Dissociation of Molecules in a High Resolution Electron Microscope

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100072873 ◽

1973 ◽

Vol 31 ◽

pp. 486-487

Author(s):

Mihir Parikh

Keyword(s):

Electron Microscope ◽

High Resolution ◽

Cross Sections ◽

Resolving Power ◽

Peak Intensity ◽

Molecular Film ◽

Resolution Electron ◽

Dissociation Cross Section ◽

High Resolution Electron Microscope ◽

The Stability

It is well known that the resolution of bio-molecules in a high resolution electron microscope depends not just on the physical resolving power of the instrument, but also on the stability of these molecules under the electron beam. Experimentally, the damage to the bio-molecules is commo ly monitored by the decrease in the intensity of the diffraction pattern, or more quantitatively by the decrease in the peaks of an energy loss spectrum. In the latter case the exposure, EC, to decrease the peak intensity from IO to I’O can be related to the molecular dissociation cross-section, σD, by EC = ℓn(IO /I’O) /ℓD. Qu ntitative data on damage cross-sections are just being reported, However, the microscopist needs to know the explicit dependence of damage on: (1) the molecular properties, (2) the density and characteristics of the molecular film and that of the support film, if any, (3) the temperature of the molecular film and (4) certain characteristics of the electron microscope used

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