Bioimaging (Bristol. Print)最新文献

{"title":"Skin Tone via Device-Independent Colour Space","authors":"Leah DeVos, Gennadi Saiko, A. Douplik","doi":"10.5220/0011748100003414","DOIUrl":"https://doi.org/10.5220/0011748100003414","url":null,"abstract":"","PeriodicalId":162397,"journal":{"name":"Bioimaging (Bristol. Print)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122314810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dense 3D Reconstruction of Endoscopic Polyp","authors":"A. Deka, Y. Iwahori, M. Bhuyan, Pradipta Sasmal, K. Kasugai","doi":"10.5220/0006720701590166","DOIUrl":"https://doi.org/10.5220/0006720701590166","url":null,"abstract":"This paper proposes a model for 3D reconstruction of polyp in endoscopic scene. 3D shape of polyp enables better understanding of the medical condition and can help predict abnormalities like cancer. While there has been significant progress in monocular shape recovery, the same hasn’t been the case with endoscopic images due to challenges like specular regions. We take advantage of the advances in shape recovery and suitably apply these with modifications to the scenario of endoscopic images. The model operates on 2 nearby video frames. ORB features are detected and tracked for computing camera motion and initial rough depth estimation. This is followed by a dense pixelwise operation which gives a dense depth map of the scene. Our method shows positive results and strong correspondence with the ground truth.","PeriodicalId":162397,"journal":{"name":"Bioimaging (Bristol. Print)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124065989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rendering Medical Images using WebAssembly","authors":"S. Jodogne","doi":"10.5220/0010833300003123","DOIUrl":"https://doi.org/10.5220/0010833300003123","url":null,"abstract":"The rendering of medical images is a critical step in a variety of medical applications from diagnosis to therapy. Specialties such as radiotherapy and nuclear medicine must display complex images that are the fusion of several layers. Furthermore, the rise of artificial intelligence applied to medical imaging calls for viewers that can be used in research environments and that can be adapted by scientists. However, desktop viewers are often developed using technologies that are totally different from those used for Web viewers, which results in a lack of code reuse and shared expertise between development teams. In this paper, we show how the emerging WebAssembly standard can be used to tackle these issues by sharing the same code base between heavyweight viewers and zero-footprint viewers. Moreover, we propose a full Web viewer developed using WebAssembly that can be used in research projects or in teleradiology applications. The source code of the developed Web viewer is available as free and open-source software.","PeriodicalId":162397,"journal":{"name":"Bioimaging (Bristol. Print)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129091441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Voronoi Diagrams and Perlin Noise for Simulation of Irregular Artefacts in Microscope Scans","authors":"Atef Alreni, G. Momcheva, Stoyan P. Pavlov","doi":"10.5220/0010833000003123","DOIUrl":"https://doi.org/10.5220/0010833000003123","url":null,"abstract":"Artefacts are a common occurrence in microscopic images and scans used in life science research. The artefacts may be regular and irregular and arise from different sources: distortions of the illumination field, optical aberrations, foreign particles in the illumination and optical path, errors, irregularities during the processing and staining phases, et cetera. While several computational approaches for dealing with patterned distortions exist, there is no universal, efficient, reliable, and facile method for removing irregular artefacts. This leaves life scientists within cumbersome predicaments, wastes valuable time, and may alter the analysis results. In this article, the authors outline a systematic way to introduce synthetic irregular artefacts in microscopic scans via Perlin Noise and Voronoi Diagrams. The reasoning behind such a task is to produce pairs of “successful” and manufactured “failed” image counterparts to be used as training pairs in an artificial neural network tuned for artefact removal. At the moment, the outlined method only works for grayscale","PeriodicalId":162397,"journal":{"name":"Bioimaging (Bristol. Print)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128331531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Video-based Patient Monitoring System - Application of the System in Intensive Care Unit","authors":"V. Kublanov, K. Purtov, M. Kontorovich","doi":"10.5220/0006598101320139","DOIUrl":"https://doi.org/10.5220/0006598101320139","url":null,"abstract":": The paper presents the video-based monitoring system to assess the physiological parameters and patient state in intensive care unit. It allows to measure thoracic and abdominal breathing movements, remote plethysmography signals, tissue perfusion, patient activity and changes in psycho-emotional state. Thus, the system provides a comprehensive assessment of patient state without contact. The system works in usual illumination conditions of intensive care unit and consists of a personal computer with specialized software and two low-cost Logitech C920 webcams with RGB sensors (8 bit per channel), 30 Hz sampling frequency and 640x480 pixel resolution. The webcams were placed at a distance of 80 cm above the patient’s body. The software provides automatic assessment of psychophysiological parameters and determination the following patterns: heart rate, heart rate variability, asystole and arrhythmias, breathing rate, spontaneous breathing recovery, breathing muscle tone and patient consciousness recovery, motor activity and control of ventilation parameters. The proposed system can be used as an additional diagnostic tool of anesthesia equipment for non-invasive patient monitoring in intensive care unit.","PeriodicalId":162397,"journal":{"name":"Bioimaging (Bristol. Print)","volume":"357 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122714005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Developing a Robust Estimator for Remote Optical Erythema Detection","authors":"Maksym Ptakh, Gennadi Saiko","doi":"10.5220/0010192901150119","DOIUrl":"https://doi.org/10.5220/0010192901150119","url":null,"abstract":"Introduction: Erythema is redness of the skin or mucous membranes, which is symptomatic for any skin injury, infection, or inflammation. In some cases, it can be indicative of certain medical conditions (e.g., nonblanchable erythema in Stage I pressure injuries), and its detection can facilitate intervention at an earlier timepoint. The most common and effective means of erythema detection is a visual inspection of the skin. However, in many cases (especially for people with darkly pigmented skin), erythema can be masked by melanin. Moreover, it would be useful to have an automated delineation and measurement of erythema using consumer-grade devices, e.g., smartphones. It would facilitate automated symptom detection and measuring healing progress in various settings, including the patient's home. Aims: This study aims to evaluate and compare several algorithms that can be used for automated erythema detection using a smartphone's camera in clinical settings. Methods: We have compared three potential estimators, which can be derived from an RGB image: a) log(R/G), b) R-G, and c) a* channel in CIELAB color space. Here, R and G are red and green channels of an RGB image, respectively. Imaged skin was divided into two classes: erythema and nonerythema. The \"erythema\" class was seeded with pixels with E>mean(E)+z*st.dev(E), where E is the value of the estimator for a particular pixel, z is a model parameter (z-score). The erythema cluster was then grown by gradually adding nearby regions with an estimator E closer to the estimator’s mean of erythema cluster than the mean of the estimator for the normal skin area (K-Mean (K=2)). The segmentation algorithm was tested on a subset of labeled images from the Swift Medical proprietary wound imaging database. To evaluate algorithm performance, the results of segmentation were compared with ground truth, manually labeled images. To quantify results, sensitivity, specificity, and ROC curves were used. Results: We have found that all investigated estimators could provide reasonable sensitivity (>0.8) and specificity (>0.78). However, a* based estimator offers slightly better performance (0.86/0.84). Discussion: The preliminary data shows that smartphone cameras can delineate erythema with reasonable sensitivity and specificity. Further studies are required to correlate the accuracy with the skin type (melanin concentration in the skin).","PeriodicalId":162397,"journal":{"name":"Bioimaging (Bristol. Print)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129404584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimising Graphical Techniques Applied to Irreversible Tracers","authors":"Yasser Alzamil, Yulia Hicks, Xin Yang, Christopher Marshall","doi":"10.5220/0006513700170026","DOIUrl":"https://doi.org/10.5220/0006513700170026","url":null,"abstract":"Graphical analysis techniques are often applied to positron emission tomography (PET) images to estimate \u0000physiological parameters. Patlak analysis is primarily used to obtain the rate constant (Ki) that indicates the \u0000transfer of a tracer from plasma to the irreversible compartment and ultimately describes how the tracer \u0000binds to the targeted tissue. One of the most common issues associated with Patlak analysis is the \u0000introduction of statistical noise that affects the slope of the graphical plot and causes bias. In this study, \u0000several statistical methods are proposed and applied to PET time activity curves (TACs) for both reversible \u0000and irreversible regions that are involved in the equation. A dynamic PET imaging simulator for the Patlak \u0000model was used to evaluate the statistical methods employed to reduce the bias introduced in the acquired \u0000data.","PeriodicalId":162397,"journal":{"name":"Bioimaging (Bristol. Print)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129387626","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated Analysis of Collagen Histology in Ageing Skin","authors":"O. Osman, Joanne L. Selway, Parvathy E. Harikumar, S. Jassim, K. Langlands","doi":"10.5220/0004786600410048","DOIUrl":"https://doi.org/10.5220/0004786600410048","url":null,"abstract":"Traditionally, expert analysis is required to evaluate pathological changes manifested in tissue biopsies. This is a highly-skilled process, notwithstanding issues of limited throughput and inter-operator variability, thus the application of image analysis algorithms to this domain may drive innovation in disease diagnostics. There are a number of problems facing the development of objective, unsupervised methods in morphometry that must be overcome. In the first instance, we decided to focus on one aspect of skin histopathology, that of collagen structure, as changes in collagen organisation have myriad pathological sequelae, including delayed wound healing and fibrosis. Methods to quantify incremental loss in structure are desirable, particularly as subclinical changes may be difficult to assess using existing criteria. For example, collagen structure is known to change with age, and through the calculation of foci distances in ellipses derived from the Fourier scatter, we were able to measure a decrease in collagen bundle thickness in picrosirius stained skin with age. Another key indicator of skin physiology is new collagen synthesis, which is necessary to maintain a healthy integument. To investigate this phenomenon, we developed a colourbased image segmentation method to discriminate newly-synthesised from established collagen revealed by Herovici’s polychrome staining. Our scheme is adaptive to variations in hue and intensity, and our use of K-means clustering and intensity-based colour filtering informed the segmentation and quantification of red (indicating old fibres) and blue pixels (indicating new fibres). This allowed the determination of the ratio of young to mature collagen fibres in the dermis, revealing an age-related reduction in new collagen synthesis. These automated colour and frequency domain methods are tractable to high-throughput analysis and are independent of operator variability.","PeriodicalId":162397,"journal":{"name":"Bioimaging (Bristol. Print)","volume":"203 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115839065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Low-density EEG for Source Activity Reconstruction using Partial Brain Models","authors":"A. Soler, E. Giraldo, M. Molinas","doi":"10.5220/0008972500540063","DOIUrl":"https://doi.org/10.5220/0008972500540063","url":null,"abstract":": Brain mapping studies have shown that the source reconstruction performs with high accuracy by using high-density EEG montages, however, several EEG devices in the market provide low-density configurations and thus source reconstruction is considered out of the scope of those devices. In this work, our aim is to use a few numbers of electrodes to reconstruct the neural activity using partial brain models, therefore, we presented a pipeline to estimate the brain activity using a low-density EEG on brain regions of interest, the partial brain model formulation and several criteria for channel selection. Two regions have been considered to be studied, the occipital region and motor cortex region. For the presented study synthetic EEG signals were generated simulating the activation of sources with a frequency in the beta range at the occipital region, and mu rhythm range at the motor cortex areas. Novel methods for electrode reduction and models for specific brain areas are presented. We assessed the quality of the reconstructions by measuring the localization error, obtaining a mean localization error below 7 mm and 16 mm with sLORETA and MSP methods respectively, by using a low-density EEG with eight channels and partial brain models.","PeriodicalId":162397,"journal":{"name":"Bioimaging (Bristol. Print)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116715695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Impact of the Wound Shape on Wound Healing Dynamics: Is it Time to Revisit Wound Healing Measures?","authors":"Gennadi Saiko","doi":"10.5220/0010337601820187","DOIUrl":"https://doi.org/10.5220/0010337601820187","url":null,"abstract":"Introduction: Wound healing is a multifaceted process, which can be impacted by many endogenous (e.g., compromised immune system, limited blood supply) or exogenous (e.g., dressing, presence of infection) factors. An essential step in wound management is to track wound healing progress. It typically includes tracking the wound size (length, width, and area). The wound area is the most often used indicator in wound management. In particular, wound closure is the single parameter used by the FDA to measure wound therapeutics' efficiency. Here, we present some arguments on why the wound area alone is insufficient to predict wound healing progress. Methods: We have developed an analytical approach to characterize an epithelization process based on the wound's area and perimeter. Results: We have obtained the explicit results for wound healing trajectory for several wound shapes: round (2D), elongated cut (1D), and rectangular. The results can be extended to complex shapes. Conclusions: From geometrical considerations, the wound healing time is determined by the shortest dimension (the width) of the wound. However, within that time, the wound healing trajectory can be different. Our calculations show that the shape of the wound may have significant implications on a wound healing trajectory. For example, in the middle of the wound healing process (t/T=0.5), the 1D wound model predicts 50% closure, while the 2D model predicts 75% closure (25% remaining). These considerations can be helpful while analyzing clinical data or designing clinical or pre-clinical experiments.","PeriodicalId":162397,"journal":{"name":"Bioimaging (Bristol. Print)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115832111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}