KEYWORDS: Hyperspectral imaging, RGB color model, Elasticity, Education and training, Tissues, Image quality, Collagen, Digital imaging, Data modeling, Data conversion
SignificanceQuantification of elastic fiber in the tissue specimen is an important aspect of diagnosing different diseases. Though hematoxylin and eosin (H&E) staining is a routinely used and less expensive tissue staining technique, elastic and collagen fibers cannot be differentiated using it. So, in conventional pathology, special staining technique, such as Verhoeff’s van Gieson (EVG), is applied physically for this purpose. However, the procedure of EVG staining is very expensive and time-consuming.AimThe goal of our study is to propose a deep-learning-based computerized method for the generation of RGB EVG stained tissue from hyperspectral H&E stained one to save the time and cost of conventional EVG staining procedure.ApproachH&E stained hyperspectral image and EVG stained RGB whole slide image of human pancreatic tissue have been leveraged for this experiment. CycleGAN-based deep learning model has been proposed for digital stain conversion while images from source and target domains are of different modalities (hyperspectral and RGB) with different channel dimensions. A set of three basis functions have been introduced for calculating one of the losses of the proposed method, which retains the relevant features of EVG stained image within the reduced channel dimension of the H&E stained one.ResultsThe experimental results showed that a set of three basis functions including linear discriminant function and transmittance spectrum of eosin and hematoxylin better retained the essential properties of the elastic fiber to be discriminated from collagen fiber within the reduced dimension of the hyperspectral H&E stained image. Also, only a smaller number of paired training data with our proposed training method contributed significantly to the generation of more realistic EVG stained image with more precise identification of elastic fiber.ConclusionsRGB EVG stained image is generated from hyperspectral H&E stained image for which our model has performed two types of image conversion simultaneously: hyperspectral to RGB and H&E to EVG. The experimental results show that the intentionally designed set of three basis functions contains more relevant information and prove the effectiveness of our proposed method in generating realistic RGB EVG stained image from hyperspectral H&E stained one.
Quantifying elastic fiber in the tissue specimen is an important aspect of diagnosing different diseases. In conventional pathology, special staining technique such as EVG (Verhoeff’s Van Gieson) is applied physically for this purpose which is expensive and time-consuming procedure. Though H&E (Hematoxylin and Eosin) staining is routinely used, less expensive and most common tissue staining technique, elastic and collagen fibers cannot be differentiated using it. This study proposes a modified CycleGAN based unsupervised method for the computerized generation of RGB EVG stained tissue from hyperspectral H&E stained one to save the time and cost of conventional EVG staining procedure. Our proposed method is designed to utilize the sufficient spectral information provided by the H&E hyperspectral image (HSI) without reducing the spectral dimension. For doing so, we have faced challenges to calculate one of the training losses (identity loss) of CycleGAN that requires reducing the channel dimension of H&E HSI to be the same as RGB EVG stained image. We have addressed the issue by adopting intentionally designed three basis functions that can reduce the channel dimension of HSI into three without losing the essential color of elastic fibers. The set of this function includes Linear Discriminant Function (LDF) and the transmittance spectrum of Eosin and Hematoxylin which has proved to best preserve the underlying important features of EVG stained image while reducing the dimensionality of hyperspectral H&E. The experimental result proves the feasibility of our proposed method to generate realistic EVG stained image from its corresponding H&E stained one.
In our previous study, we proposed a hand-waving finger vein authentication system, in which finger region extraction
from captured images was effective to verify the finger vein patterns with high accuracy. However, it is not easy to
extract the correct finger region from grayscale images that are taken with a near infra-red LED, because the background
condition of the captured image changes complicatedly according to the location of the waving hand. In order to
overcome this limitation, we propose an alternative finger region extraction method that takes color images with an RGB
camera and a white light source and identifies the finger region based on the skin color information.
Evaluation of tissue margins and hemodynamics is necessary during macropathology of skin lesions. This study aims to produce saliency maps of skin chromophores from ex-vivo specimens and observe the effect of formalin fixation on the maps. We used a multi-spectral imaging system with narrow-band illumination to capture various skin lesions. Saliency maps were produced with three different methods adapted from the literature by utilizing spectral absorption and absorption slope. Saliency maps derived from fixed and unfixed tissue were registered and subsequently compared in terms of correlation and histogram similarity. Preliminary results show high dissimilarity between maps of fixed and unfixed tissue, highlighting the influence of formalin fixing on hemodynamics, while relative distribution of melanin remained mostly unaffected.
Non-contact measurement of pulse wave velocity (PWV) using red, green, and blue (RGB) digital color images is proposed. Generally, PWV is used as the index of arteriosclerosis. In our method, changes in blood volume are calculated based on changes in the color information, and is estimated by combining multiple regression analysis (MRA) with a Monte Carlo simulation (MCS) model of the transit of light in human skin. After two pulse waves of human skins were measured using RGB cameras, and the PWV was calculated from the difference of the pulse transit time and the distance between two measurement points. The measured forehead-finger PWV (ffPWV) was on the order of m/s and became faster as the values of vital signs raised. These results demonstrated the feasibility of this method.
Double Random Phase Encoding (DRPE), which is a typical optical encryption technique, has been reported to be
vulnerable to Known Plaintext-Attacks (KPAs) using a Phase Retrieval Algorithm (PRA). But the reported case in which
the encryption key is successfully estimated was that the plain image was rather simple such as the image of a character.
In addition, although Phase Only DRPE (PO-DRPE) was proposed to achieve more resistance to the KPA than Complex
DRPE (C-DRPE) in which both amplitude and phase components are used as an encrypted image, no quantitative results
about the relationship between the vulnerability and the plaintext image. In this paper, we show the result of quantitative
analysis on KPA by PRA to C-DRPE and PO-DRPE, for the plaintext images of different characteristics. As a result of
experiment, KPA to C-DRPE succeeded to estimate the correct key while the probability of success became lower when
the number of non-zero pixel increases in the plaintext image. However, KPA to PO-DRPE enabled to estimate only
"singular" keys, which are effective for no more than given plaintext/ciphertext image pair and far different from the
correct encryption key. We also conducted KPA using two plaintext-ciphertext image pairs for KPA. In the case when
two plaintext-ciphertext image pairs were given to KPA, the cryptanalysis succeeded with higher probability than the
case of one. Moreover, the probability of success in the KPA was high even in PO-DRPE.
Fingerprint verification for smart card holders is one of the methods which are able to identify smart card holders with a high level of security. However, an ingenious implementation is needed to execute it in the embedded processor quickly and safely, because of its computational burden and the limitation of the smart card performance. For this purpose, we propose a hybrid method which is a combination of personal identification number (PIN) verification with a smart card and an optical fingerprint verification method. The result of a preliminary computer simulation to evaluate the proposed system shows that false acceptance rate is completely zero, though false rejection rate is a little inferior to the conventional figerprint verification system.
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