Published papers

Background: Skin barrier function is significantly impacted by skin moisture. Most non-invasive evaluation techniques to measure skin surface hydration relying on its electrical properties, which are limited in scope and have unstable operations. Applying image processing for skin hydration assessment is uncommon, with an emphasis on skin-capacitive pictures and near-infrared images in general, which demand a certain spectrum. As a result, there is an increasing need for wide-area skin hydration evaluation and mapping. Objective: The study aims to propose a quantitative evaluation algorithm for skin surface hydration from visible-light images. Materials and methods: Three devices were applied to measure skin hydration: skin image capture device and two recognized commercial skin devices. A digital image processing system creates a new index, called GVR, to symbolize skin surface moisture. The CLAHE algorithm was applied to enhance the contrast of skin image, and after calculating it with the monochrome image, the skin reflectance image was segmented. The GVR was estimated using the values of the individual sites and the entire skin. The correlation coefficient between the three methods was examined using statistical analysis to assess the performance of GVR. Results: Skin hydration estimated from visible-light images is influenced by the entire facial structure in addition to specific areas. The electrical and visible image evaluations showed a strong association with a significant difference. Conclusion: It was discovered that reflecting measures from visible images provide a quick and efficient way to quantify the moisture of the skin's surface.

Lung cancer is one of the cancers with the highest morbidity and mortality in the world. Recurrence often occurs even after complete resection of early-stage lung cancer, and prediction of recurrence after resection is clinically important. However, the pathological characteristics of the recurrence of pathological stage IB lung adenocarcinoma (LAIB) have not yet been elucidated. Therefore, the problem is what type of histological image of lung adenocarcinoma recurs, and it is important to examine the histological image of recurrence. We attempted to predict recurrence of early lung adenocarcinoma after resection on the basis of digital pathological images of hematoxylin and eosin-stained specimens and machine learning applying a convolutional neural network. We constructed a model that extracts the features of two-color spaces and a switching model that automatically switches between our extraction model and one that extracts the features of one-color space for each image. We then developed a tumor-identification method for predicting the presence or absence of LAIB recurrence using these models. We conducted an experiment involving 55 patients with LAIB who underwent surgical resection to evaluate the proposed method. The proposed method determined LAIB recurrence with an accuracy of 84.8%. The use of digital pathology and machine learning can be used for highly accurate prediction of LAIB recurrence after surgical resection. The proposed method has the potential for objective postoperative follow-up observation.

Background: Skin is the first line of defense against harmful external environmental factors. Skin flora living on the skin surface impact skin health and skin disease. Bacteria, form part of the unique and complex skin micro-ecological system. For example, Propionibacterium acnes (P. acnes) is a member of the anaerobic organisms and is involved in the induction of skin acne. It produces porphyrins that absorb ultraviolet light and emit red fluorescence in response. As a result, fluorescence surveillance of the skin can be important in both the diagnosis of skin acne and the evaluation of therapeutic effects. Many different measurement methods for single skin biophysical properties have been reported. This study focused on the age-dependent changes in porphyrins for normal skin, and developed a novel algorithm to evaluate porphyrins using the fluorescence images by image processing quantitatively. Materials and methods: An extraction algorithm was proposed for the segmentation of porphyrin fluorescence images in OpenCV. The algorithm consisted primarily of preprocessing, conversion from RGB color space to HSV color space, and classification of fluorescence. There are 3595 healthy Japanese aged 16–85 years enrolled in the study and fluorescence images were acquired from their cheek sites under 375 nm UV-LED excitation. Age-related fluorescence variation was conducted applying the algorithm implemented. Results: A new extraction algorithm has been proposed with fluorescence image input and three indexes output, including the number of fluorescence, area of fluorescence, and mean intensity of fluorescence. Proposed algorithm was verified by three parameters, the accuracy, sensitivity, and precision, which refer to the ability of algorithm to detect the number of fluorescence correctly and repeatedly. The verification results were 71%, 72%, and 88% respectively, taking a validly fundamental step for skin health record and analysis. Furthermore, large-scale fluorescence image segmentation results revealed that similar trends were coming out for all three indexes in cheek as people get older. All the fluorescence number, area and mean intensity arrived at the highest at 30 years old and fell off since then. Conclusion: The number, area, and fluorescence intensity of porphyrins can be extracted well from fluorescence images with the proposed algorithm in the study, which has the potential to aid in thediagnosis of skin acne and predict skin conditions as an assisted tool. It is implicated that fluorescence status is influenced by age, which rises to the peak around 30 years old for normal cheek's skin.

Background: Skin micro-relief has been researched by a variety of devices and methods, which usually are expensive or complicated. On the other hand, skin micro-relief relates to quite a few parameters, and it is hard to evaluate all of them at the same time. In the study, all parameters related to skin micro-relief are extracted and evaluated by image analysis. Materials and Methods: Skin micro-relief evaluation was divided into four aspects: (a) Tamura features method was used to evaluate skin surface. (b) Morphological transform was applied to extract skin pores. (c) Watershed transform was applied to extract skin furrows. (d) labeling operation was used to evaluate the number, area and average area of skin closed polygons. Then, cheek images from 163 healthy Japanese females (0-70 years old) are analyzed to explore the age-dependent changes. Results: Most parameters increased as age went on with significant differences, such as skin surface coarseness, contrast, skin pore number, area, average area, skin furrow width, skin closed polygon area and skin closed polygon average area. Skin coarseness has a strong correlation with pore area. Conclusion: The method proposed in the study provided a comprehensive and effective assessment of skin micro-relief.

© 2020 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd Background: Skin color is an important physiological index of human skin that has essential significance in cosmetology and dermatological diagnoses and aesthetics, especially in East Asian culture, where the beauty standard is the whiter the skin, the more beautiful a person is. However, there has been a lack of objective comparisons of facial skin color in East Asian females. Materials and Methods: Skin color at the cheek site was measured in 445 healthy females, including 362 Chinese women in Beijing and 83 Japanese women in Tokyo, all aged 18-50 years. The parameters consisted of L*, a*, b*, individual typology angle (ITA°), and hue angle and were determined using noninvasive skin measurement instruments. Results: The skin color categories in East Asians ranged from very light (I) to brown (V), and the dark (VI) category was not observed; the main categories were light (II), intermediate (III), and tan (IV). The facial skin color of Chinese individuals was brighter, more reddish, and less yellowish than that of Japanese individuals. With age, L* values decreased, and a* values and hue angle increased in East Asians with significant differences. Chinese women showed the same differences as Japanese women for all parameters in all age groups. Conclusions: This study provides objective skin color measurements and examines significant differences with respect to geographic location and age.

© 2017, Springer Science+Business Media, LLC, part of Springer Nature. We experimentally demonstrate see-through detection and 3D reconstruction using terahertz leaky-wave radar based on sparse signal processing. The application of terahertz waves to radar has received increasing attention in recent years for its potential to high-resolution and see-through detection. Among others, the implementation using a leaky-wave antenna is promising for compact system integration with beam steering capability based on frequency sweep. However, the use of a leaky-wave antenna poses a challenge on signal processing. Since a leaky-wave antenna combines the entire signal captured by each part of the aperture into a single output, the conventional array signal processing assuming access to a respective antenna element is not applicable. In this paper, we apply an iterative recovery algorithm “CoSaMP” to signals acquired with terahertz leaky-wave radar for clutter mitigation and aperture synthesis. We firstly demonstrate see-through detection of target location even when the radar is covered with an opaque screen, and therefore, the radar signal is disturbed by clutter. Furthermore, leveraging the robustness of the algorithm against noise, we also demonstrate 3D reconstruction of distributed targets by synthesizing signals collected from different orientations. The proposed approach will contribute to the smart implementation of terahertz leaky-wave radar.

© 2019 IEEE. In this research, we focus on positioning in places where radio waves from satellites can not reach, such as indoors. We aim at establishment and accuracy improvement of positioning using sensor data of smartphone. The number of steps, stride, and direction are important factors in this research. The previous research has a large error and can not estimate an accurate position. Therefore, we propose position estimation method by adding the newly studied method to the conventional method.

Background: Cancer tissue is composed of both a cancer cell component and a stromal component. The aim of this study was to investigate if the component ratio predicts a prognosis for lung squamous cell carcinoma (SqCC) patients by using a machine learning method. Methods: A total of 135 peripheral SqCC cases (tumor size: 3−5 cm) were enrolled in this study. The areas of the cancer cell component, the necrotic component, and the stromal component were accurately measured via a machine learning method. Each case was divided into the following three subtypes: 1) predominant cancer cell, 2) predominant necrosis, and 3) predominant stroma. The study examined if a particular subtype had prognostic significance. Results: The number of cases per subtype of predominant cancer cell, predominant necrosis, and predominant stroma was 59, 6, and 70, respectively. Patients with the predominant stroma subtype had a significantly shorter recurrence free survival (RFS) than did those with the predominant cancer cell subtype (5-yr RFS: 42.3 % vs. 84.3 %,p < 0.01). Also, in pathological stage I patients, the 5-year RFS rate for the predominant stroma subtype was significantly shorter (5-yr RFS: 64.3 % vs. 88.4 %, p < 0.01). In the multivariate analysis of p-stage I patients, the predominant stroma subtype was confirmed to be an independent prognostic factor for RFS (p < 0.01). Conclusion: Using machine learning, the study confirmed that the predominant stroma subtype was an independent factor for RFS, suggesting that the ratio of the stromal component correlates with the malignant potential of SqCC.