Feature selection algorithm for classification of multispectral MR images using constrained energy minimization

Lin, Geng-Cheng; Wang, Wen-June; Wang, Chuin-Mu

doi:10.1109/his.2010.5604768

Cited by 7 publications

(2 citation statements)

References 24 publications

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“…Consequently, matching the target spectrum with impulse response maximizes signal‐to‐noise ratio. Similarly, CEM is a form of MF that uses an operator to pass target spectra while constraining output energy from unwanted background sources . Both CEM and MF are advantageous for separating autofluorescence due to the potential for undersampling all the components present in tissue.…”

Section: Methodsmentioning

confidence: 99%

Label‐free spectroscopic tissue characterization using fluorescence excitation‐scanning spectral imaging

Favreau

Deal

Harris

et al. 2019

Journal of Biophotonics

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A false‐colored and merged image of fresh, ex vivo rat kidney acquired using an excitation‐scanning hyperspectral imaging system. The spectral image was acquired using excitation wavelengths from 360 to 550 nm. Colors represent principal components extracted from a spectral image cube featuring no added labels or markers. Further details can be found in the article by Peter F. Favreau, Joshua A. Deal, Bradley Harris, et al. (https://onlinelibrary.wiley.com/doi/10.1002/jbio.201900183).

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Section: Methodsmentioning

confidence: 99%

Label‐free spectroscopic tissue characterization using fluorescence excitation‐scanning spectral imaging

Favreau

Deal

Harris

et al. 2019

Journal of Biophotonics

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“…It extracts the object of interest while minimizing the interfering effects induced from unknown sources including the image background. Lin et al [20] have reported a great success to classify and segment three major tissues which are gray matter, white matter, and cerebral spinal fluid automatically from brain MR images extending CEM.…”

Section: Literature Reviewmentioning

confidence: 99%

Retinal Spectral Image Analysis Methods Using Spectral Reflectance Pattern Recognition

Rahaman

Parkkinen

Hauta-Kasari

et al. 2013

Lecture Notes in Computer Science

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Conventional 3-channel color images have limited information and quality dependency on parametric conditions. Hence, spectral imaging and reproduction is desired in many color applications to record and reproduce the reflectance of objects. Likewise RGB images lack sufficient information to successfully analyze diabetic retinopathy. In this case, spectral imaging may be the alternative solution. In this article, we propose a new supervised technique to detect and classify the abnormal lesions in retinal spectral reflectance images affected by diabetes. The technique employs both stochastic and deterministic spectral similarity measures to match the desired reflectance pattern. At first, it classifies a pixel as normal or abnormal depending on the probabilistic behavior of training spectra. The final decision is made evaluating the geometric similarity. We assessed several multispectral object detection methods developed for other applications. They could not proof to be the solution. The results were interpreted using receiver operating characteristics (ROC) curves analysis.

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Target spectrum based feature selection (TSFS): A new method based on chain coding for target detection problems

Houshyaripour

Momeni

2020

Infrared Physics & Technology

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Feature selection algorithm for classification of multispectral MR images using constrained energy minimization

Cited by 7 publications

References 24 publications

Label‐free spectroscopic tissue characterization using fluorescence excitation‐scanning spectral imaging

Label‐free spectroscopic tissue characterization using fluorescence excitation‐scanning spectral imaging

Retinal Spectral Image Analysis Methods Using Spectral Reflectance Pattern Recognition

Target spectrum based feature selection (TSFS): A new method based on chain coding for target detection problems

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