The Application Progress of Skin Imaging Technology in Psoriasis

Skin imaging technologies such as dermoscopy, high-frequency ultrasound, reflective confocal microscopy and optical coherence tomography are developing rapidly in clinical application. Skin imaging technology can improve clinical diagnosis rate, and its non-invasiveness and repeatability make it occupy an irreplaceable position in clinical diagnosis. With the “booming development” of medical technology, skin imaging technology can improve clinical diagnosis rate. Researchers have made significant advances in assisting clinical diagnosis, prediction, and treatment of disease. This article reviews the application and progress of skin imaging in the diagnosis of psoriasis.

3D depth-coded photoacoustic microscopy with a large field of view for human skin imaging

Photoacoustic （PA） microscopy comes with high potential for human skin imaging, since it allows noninvasively high-resolution imaging of the natural hemoglobin at depths of several millimeters. Here, we developed a PA microscopy to achieve high-resolution, high-contrast, and large field of view imaging of skin. A three-dimensional （3D） depth-coding technology was used to encode the depth information in PA images, which is very intuitive for identifying the depth of blood vessels in a two-dimensional image, and the vascular structure can be analyzed at different depths. Imaging results demonstrate that the 3D depth-coded PA microscopy should be translated from the bench to the bedside.

In vivo skin imaging prototypes ＂made in Latvia＂

This paper briefly reviews the operational principles and designs of portable in vivo skin imaging prototypes developed at the Biophotonics Laboratory of the Institute of Atomic Physics and Spectroscopy, University of Latvia. Four types of imaging devices are presented. Multi-spectral imagers ensure distant mapping of specific skin parameters （e.g., distribution of skin chromophores）. Autofluorescence photobleaching rate imagers show potential for skin tumor assessment and margin delineation. Photoplethysmography video-imagers remotely detect cutaneous blood pulsations and provide real-time information on the human cardiovascular state. Multimodal skin imagers perform the above-mentioned functions by acquiring several spectral and video images using the same image sensor.

Multi-Class Skin Cancer Detection Using Fusion of Textural Features Based CAD Tool

Skin cancer has been recognized as one of the most lethal and complex types of cancer for over a decade.The diagnosis of skin cancer is of paramount importance,yet the process is intricate and challenging.The analysis and modeling of human skin pose significant difficulties due to its asymmetrical nature,the visibility of dense hair,and the presence of various substitute characteristics.The texture of the epidermis is notably different from that of normal skin,and these differences are often evident in cases of unhealthy skin.As a consequence,the development of an effective method for monitoring skin cancer has seen little progress.Moreover,the task of diagnosing skin cancer from dermoscopic images is particularly challenging.It is crucial to diagnose skin cancer at an early stage,despite the high cost associated with the procedure,as it is an expensive process.Unfortunately,the advancement of diagnostic techniques for skin cancer has been limited.To address this issue,there is a need for a more accurate and efficient method for identifying and categorizing skin cancer cases.This involves the evaluation of specific characteristics to distinguish between benign and malignant skin cancer occurrences.We present and evaluate several techniques for segmentation,categorized into three main types:thresholding,edge-based,and region-based.These techniques are applied to a dataset of 200 benign and melanoma lesions from the Hospital Pedro Hispano(PH2)collection.The evaluation is based on twelve distinct metrics,which are designed to measure various types of errors with particular clinical significance.Additionally,we assess the effectiveness of these techniques independently for three different types of lesions:melanocytic nevi,atypical nevi,and melanomas.The first technique is capable of classifying lesions into two categories:atypical nevi and melanoma,achieving the highest accuracy score of 90.00%with the Otsu(3-level)method.The second technique also classifies lesions into two categories:common nevi and melanoma,achieving a score of 90.80%with the Binarized Sauvola method.

A Comprehensive Systematic Review: Advancements in Skin Cancer Classification and Segmentation Using the ISIC Dataset

The International Skin Imaging Collaboration(ISIC)datasets are pivotal resources for researchers in machine learning for medical image analysis,especially in skin cancer detection.These datasets contain tens of thousands of dermoscopic photographs,each accompanied by gold-standard lesion diagnosis metadata.Annual challenges associated with ISIC datasets have spurred significant advancements,with research papers reporting metrics surpassing those of human experts.Skin cancers are categorized into melanoma and non-melanoma types,with melanoma posing a greater threat due to its rapid potential for metastasis if left untreated.This paper aims to address challenges in skin cancer detection via visual inspection and manual examination of skin lesion images,processes historically known for their laboriousness.Despite notable advancements in machine learning and deep learning models,persistent challenges remain,largely due to the intricate nature of skin lesion images.We review research on convolutional neural networks(CNNs)in skin cancer classification and segmentation,identifying issues like data duplication and augmentation problems.We explore the efficacy of Vision Transformers(ViTs)in overcoming these challenges within ISIC dataset processing.ViTs leverage their capabilities to capture both global and local relationships within images,reducing data duplication and enhancing model generalization.Additionally,ViTs alleviate augmentation issues by effectively leveraging original data.Through a thorough examination of ViT-based methodologies,we illustrate their pivotal role in enhancing ISIC image classification and segmentation.This study offers valuable insights for researchers and practitioners looking to utilize ViTs for improved analysis of dermatological images.Furthermore,this paper emphasizes the crucial role of mathematical and computational modeling processes in advancing skin cancer detection methodologies,highlighting their significance in improving algorithmic performance and interpretability.

FEATURE EXTRACTION OF BONES AND SKIN BASED ON ULTRASONIC SCANNING

In the prosthetic socket design, aimed at the high cost and radiation deficiency caused by CT scanning which is a routine technique to obtain the cross-sectional image of the residual limb, a new ultrasonic scanning method is developed to acquire the bones and skin contours of the residual limb. Using a pig fore-leg as the scanning object, an overlapping algorithm is designed to reconstruct the 2D cross-sectional image, the contours of the bone and skin are extracted using edge detection algorithm and the 3D model of the pig fore-leg is reconstructed by using reverse engineering technology. The results of checking the accuracy of the image by scanning a cylinder work pieces show that the extracted contours of the cylinder are quite close to the standard circumference. So it is feasible to get the contours of bones and skin by ultrasonic scanning. The ultrasonic scanning system featuring no radiation and low cost is a kind of new means of cross section scanning for medical images.

Construction of an artificial intelligence system in dermatology: effectiveness and consideration of Chinese Skin Image Database (CSID)

After more than 60 years of development,artificial intelligence(AI)has been widely used in various fields.Especially in recent years,with the development of deep learning,AI has made many remarkable achievements in the medical field.Dermatology,as a clinical discipline with morphology as its main feature,is particularly suitable for the development of AI.The rapid development of skin imaging technology has helped dermatologists to assist in the diagnosis of diseases and has greatly improved the accuracy of diagnosis.Skin imaging data have natural big data attributes,which is important for AI research.The establishment of the Chinese Skin Image Database(CSID)has solved many problems such as isolated data islands and inconsistent data quality.Based on the CSID,many pioneering achievements have been made in the research and development of AI-assisted decision-making software,the establishment of expert organizations,personnel training,scientific research,and so on.At present,there are still many problems with AI in the field of dermatology,such as clinical validation,medical device licensing,interdisciplinary,and standard formulation,which urgently need to be solved by joint efforts of all parties.

Design and implementation of the galvanometer scanning system for reflectance confocal and stimulated Raman scattering microscopy

We report on two strategies to design and implement the galvanometer-based laser-scanning mechanisms for the realization of reflectance confocal microscopy(RCM) and stimulated Raman scattering(SRS) microscopy systems. The RCM system uses a resonant galvanometer scanner driven by a home-built field-programmable gate array circuit with a novel dual-trigger mode and a home-built high-speed data acquisition card. The SRS system uses linear galvanometers with commercially available modules. We demonstrate video-rate high-resolution imaging at 11 frames per second of in vivo human skin with the RCM system and label-free biomolecular imaging of cancer cells with the SRS system. A comparison of the two strategies for controlling galvanometer scanners provides scientific and technical reference for future design and commercialization of various laser-scanning microscopes using galvanometers.

Real-time monitoring of tumor vascular disruption induced by radiofrequency assisted gadofullerene

The anti-vascular therapy has been extensively studied for high performance tumor therapy by suppressing the tumor angiogenesis or cutting off the existing tumor vasculature. We have previously reported a novel anti-tumor treatment technique using radiofrequency （RF）-assisted ga- dofullerene nanocrystals （GFNCs） to selectively disrupt the tumor vasculature. In this work, we further revealed the changes on morphology and functionality of the tumor vas-culature during the high-performance RF-assisted GFNCs treatment in vivo. Here, a dearly evident mechanism of this technique in tumor vascular disruption was elucidated. Based on the H22 tumor bearing mice with dorsal skin flap chamber （DSFC） mode] and the dynamic contrast enhanced magnetic resonance imaging （DCE-MRI） technique, it was revealed that the GFNCs would selectively inset in the gaps of tumor vas-culature due to the innately incomplete structures and unique microenvironment of tumor vasculature,＇ and they damaged the surrounding endothelia cells excited by the RF to induce a phase transition accompanying with size expansion. Soon afterwards, the blood flow of the tumor blood vessels was permanently shut off, causing the entire tumor vascular net- work to collapse within 24 h after the treatment. The RF-as- sistant GFNCs technique was proved to aim at the tumor vasculatnre precisely, and was harmless to the normal vascu- lature. The current studies provide a rational explanation on the high efficiency anticancer activity of the RF-assisted GFNCs treatment, suggesting a novel technique with potent clinical application.