Hyperspectral imaging in medicine: image pre‐processing problems and solutions in Matlab

Koprowski, Robert

doi:10.1002/jbio.201400133

Cited by 6 publications

(6 citation statements)

References 27 publications

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“… The three methods described in this article: fast analysis of emissivity curves (SKE), 3D segmentation (S3D), hierarchical segmentation (SH), The three known segmentation methods (for quantitative comparison of the results obtained): method based on brightness thresholding (SPJ)—the binarization threshold is selected manually and automatically using Otsu’s formulas [ 18 ], watershed method (SWS) preceded by filtration with an averaging filter whose mask size is in the range from 3 × 3 pixels to 9 × 9 pixels, method based on mathematical morphology (SMM), especially erosion and conditional dilation, Manual method of object selection considered further as the benchmark (SP). …”

Section: Experimental and Discussionmentioning

confidence: 99%

“…method based on brightness thresholding (SPJ)—the binarization threshold is selected manually and automatically using Otsu’s formulas [ 18 ],…”

Section: Experimental and Discussionmentioning

confidence: 99%

“…As it was mentioned in the “ Background ” section, numerous similar segmentation methods are known from the literature. Hence, the method utilizing emissivity curves of a given object (SKE method) is used in many applications such as specim spectral imaging [ 30 , 31 ], hyperspectral imaging system [ 5 , 18 ] or imageJ [ 32 ]. However, in none of such applications the time of preliminary segmentation was obtained taking into account the emissivity curve at the level of 2.3 ms. For example in [ 33 ] the authors obtain the time of analysis equal to 100 ms using Intel ® Core i5 CPU M460 @2.5 GHz 4 GB RAM.…”

Section: Experimental and Discussionmentioning

confidence: 99%

“…The second element is the lack of dependence of segmentation on the reference (expected) spectral spectrum. Therefore, in comparison to the segmentation of images in grey levels or colour images, hyperspectral imaging gives much more opportunities [ 18 ]. However, this excess of data is not, in every case, used by the authors of the algorithm (the authors of the article).…”

Section: Introductionmentioning

confidence: 99%

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Segmentation in dermatological hyperspectral images: dedicated methods

Koprowski

Olczyk

2016

BioMed Eng OnLine

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BackgroundSegmentation of hyperspectral medical images is one of many image segmentation methods which require profiling. This profiling involves either the adjustment of existing, known image segmentation methods or a proposal of new dedicated methods of hyperspectral image segmentation. Taking into consideration the size of analysed data, the time of analysis is of major importance. Therefore, the authors proposed three new dedicated methods of hyperspectral image segmentation with special reference to the time of analysis.MethodsThe segmentation methods presented in this paper were tested and profiled to the images acquired from different hyperspectral cameras including SOC710 Hyperspectral Imaging System, Specim sCMOS-50-V10E. Correct functioning of the method was tested for over 10,000 2D images constituting the sequence of over 700 registrations of the areas of the left and right hand and the forearm.ResultsAs a result, three new methods of hyperspectral image segmentation have been proposed: fast analysis of emissivity curves (SKE), 3D segmentation (S3D) and hierarchical segmentation (SH). They have the following features: are fully automatic; allow for implementation of fast segmentation methods; are profiled to hyperspectral image segmentation; use emissivity curves in the model form, can be applied in any type of objects not necessarily biological ones, are faster (SKE—2.3 ms, S3D—1949 ms, SH—844 ms for the computer with Intel® Core i7 4960X CPU 3.6 GHz) and more accurate (SKE—accuracy 79 %, S3D—90 %, SH—92 %) in comparison with typical methods known from the literature.ConclusionsProfiling and/or proposing new methods of hyperspectral image segmentation is an indispensable element of developing software. This ensures speed, repeatability and low sensitivity of the algorithm to changing parameters.

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Section: Experimental and Discussionmentioning

confidence: 99%

“…method based on brightness thresholding (SPJ)—the binarization threshold is selected manually and automatically using Otsu’s formulas [ 18 ],…”

Section: Experimental and Discussionmentioning

confidence: 99%

Section: Experimental and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Segmentation in dermatological hyperspectral images: dedicated methods

Koprowski

Olczyk

2016

BioMed Eng OnLine

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“…38,53 Removal of noise, dead pixels, spiked points, and data compression is performed after that. 54…”

Section: Hsi Data Processingmentioning

confidence: 99%

New perspectives of hyperspectral imaging for clinical research

et al. 2021

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New developments in instrumentation and data analysis have further improved the perspectives of hyperspectral imaging in clinical use. Thus, hyperspectral imaging can be considered as “Next Generation Imaging” for future clinical research. As a contactless, non-invasive method with short process times of just a few seconds, it quantifies predefined substance classes. Results of hyperspectral imaging may support the detection of carcinomas and the classification of different tissue structures as well as the assessment of tissue blood flow. Taken together, this method combines the principle of spectroscopy with imaging using conventional visual cameras. Compared to other optical imaging methods, hyperspectral imaging also analyses deeper layers of tissue.

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Image Acquisition

Koprowski¹

2016

Processing of Hyperspectral Medical Images

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Hyperspectral imaging in medicine: image pre‐processing problems and solutions in Matlab

Cited by 6 publications

References 27 publications

Segmentation in dermatological hyperspectral images: dedicated methods

Segmentation in dermatological hyperspectral images: dedicated methods

New perspectives of hyperspectral imaging for clinical research

Image Acquisition

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