Towards real-time medical diagnostics using hyperspectral imaging technology

Bjørgan, Asgeir; Randeberg, Lise Lyngsnes

doi:10.1117/12.2184155

Cited by 4 publications

(2 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The spectral properties of an object are closely related to its intrinsic physicochemical properties, and the differences in the composition and structure of substances result in the selective absorption and emission of photons of different wavelengths within the substance. Presently, HSI serves as a non-destructive and expeditious analytical tool across various domains, including medical diagnosis ( Bjorgan and Randeberg, 2015 ), food industry ( Ma et al., 2019 ), fruit damage and disease detection ( Tian et al., 2020 ; Yadav et al., 2022 ; Jiang et al., 2023 ), and plant seed analysis ( Zhu et al., 2019 ). HSI has proven to be an effective technique for non-destructive seed quality testing by many scholars.…”

Section: Introductionmentioning

confidence: 99%

Modeling of flaxseed protein, oil content, linoleic acid, and lignan content prediction based on hyperspectral imaging

Zhu,

Han,

Liu

et al. 2024

Front. Plant Sci.

View full text Add to dashboard Cite

Protein, oil content, linoleic acid, and lignan are several key indicators for evaluating the quality of flaxseed. In order to optimize the testing methods for flaxseed’s nutritional quality and enhance the efficiency of screening high-quality flax germplasm resources, we selected 30 flaxseed species widely cultivated in Northwest China as the subjects of our study. Firstly, we gathered hyperspectral information regarding the seeds, along with data on protein, oil content, linoleic acid, and lignan, and utilized the SPXY algorithm to classify the sample set. Subsequently, the spectral data underwent seven distinct preprocessing methods, revealing that the PLSR model exhibited superior performance after being processed with the SG smoothing method. Feature wavelength extraction was carried out using the Successive Projections Algorithm (SPA) and the Competitive Adaptive Reweighted Sampling (CARS). Finally, four quantitative analysis models, namely Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), Multiple Linear Regression (MLR), and Principal Component Regression (PCR), were individually established. Experimental results demonstrated that among all the models for predicting protein content, the SG-CARS-MLR model predicted the best, with and of 0.9563 and 0.9336, with the corresponding Root Mean Square Error Correction (RMSEC) and Root Mean Square Error Prediction (RMSEP) of 0.4892 and 0.5616, respectively. In the optimal prediction models for oil content, linoleic acid and lignan, the Rp2 was 0.8565, 0.8028, 0.9343, and the RMSEP was 0.8682, 0.5404, 0.5384, respectively. The study results show that hyperspectral imaging technology has excellent potential for application in the detection of quality characteristics of flaxseed and provides a new option for the future non-destructive testing of the nutritional quality of flaxseed.

show abstract

Section: Introductionmentioning

confidence: 99%

Modeling of flaxseed protein, oil content, linoleic acid, and lignan content prediction based on hyperspectral imaging

Zhu,

Han,

Liu

et al. 2024

Front. Plant Sci.

View full text Add to dashboard Cite

show abstract

“…Hyperspectral imaging captures a three-dimensional cubic data with the detail spectral distribution at each pixel location for a scene, and can greatly benefits the characterization of the observed scene for improving analysis performance in different application fields ranging from remote sensing [1,2], vision inspection, medical diagnosis [3,4], digital forensics and so on. For capturing the large number of spectral bands, hyperspectral imaging sensors require multiple times of exposure measurement with 1D or 2D detectors, and leads to This work was supported in part by MEXT under the Grant No.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Reconstruction Using Auxiliary Rgb Learning From A Snapshot Image

Yamawaki

Yorimoto

Han

2022

2022 IEEE International Conference on Image Processing (ICIP)

View full text Add to dashboard Cite

To solve the low spatial and temporal resolution issue in the conventional hyperspectral (HS) imaging sensors, coded aperture snapshot HS imaging, which encodes the 3D HS image into a 2D compressive snapshot and then adopts computational technique to recover the latent HS image, has attracted remarkable attention in recent year. This study aims to reconstruct the latent HS image with the detail spectral distribution from its compressive snapshot using the deep convolution neural network (DCNN). Due to the ill-posed nature, the HS image reconstruction is a challenge task, and the spectral distortion is unavoidably produced even with the powerful learning capability of the DCNN. To alleviate this limitation, we leverage an auxiliary RGB learning task to reconstruct the corresponding RGB image from the snapshot image in training phase, and incorporate the learned features of the auxiliary task to assist the more difficult reconstruction of the latent HS image. Specifically, we design the DCNN architecture with two branches for both reconstruction learnings of a small number of spectral image (such as RGB) and the full-spectral HS image, and then integrate the intermediate features in the auxiliary RGB branch to the HS reconstruction branch for augment the spectral learning capability. Experimental results demonstrate our proposed method with the auxiliary learning can achieve comparable performance with the state-of-the-art methods while enable the reduction of the model size.

show abstract