Objective Assessment of Area and Erythema of Psoriasis Lesion Using Digital Imaging and Colourimetry

Psoriasis is a non-contagious skin disease which typically consists of red plaques covered by silvery-white scales. It affects about 3% of world population. During treatment, dermatologists monitor the extent of psoriasis continuously to ascertain treatment efficacy. Psoriasis Area and Severity Index (PAS!) is the current gold standard method used to assess the extent of psoriasis. In PAS!, there are four parameters to be scored i.e., the surface area affected, erythema (redness), thickness and scaliness of the plaques. Determining PAS! score is a tedious task and thus it is not used in daily clinical practice. In addition, the PAS! parameters are visually determined and may result in intra-observer and inter-observer variations, even by experienced dermatologists. Objective methods in assessing area and erythema of psoriasis lesion have been developed in this thesis. Psoriasis lesion can be recognized by its colour dissimilarity with normal skin. Colour dissimilarity is represented by colour difference in CIELAB colour space, a widely used colour space to measure colour dissimilarity. Each pixel in CIELAB colour space can be represented by its lightness (L'), hue (hob), and chroma (Cab). Colour difference between psoriasis lesion and normal skin is analyzed in hue-chroma plane of CIELAB colour space. Centroids of normal skin and lesion in hue-chroma space are obtained from selected samples. Euclidean distances between all pixels with these two centroids are then calculated. Each pixel is assigned to the class of the nearest centroid. The erythema of psoriasis lesion is affected by degree of severity and skin pigmentation. In order to assess the erythema objectively, patients are grouped according to their skin pigmentation level. The L* value of normal skin which represents skin pigmentation level is utilized to group the patient into the three skin types namely fair, brown and dark skin types. Light difference (t.L*), hue difference (t.hab), and chroma difference (t.C'ab) of CIELAB colour space between reference lesions and the surrounding normal skin are analyzed. It is found that the erythema score of a lesion can be determined by their hue difference (t.hab) value within a particular skin type group. Out of 30 body regions, the proposed method is able to give the same PAS! area score as reference for 28 body regions. The proposed method is able to determine PAS! erythema score of 82 lesions obtained from 22 patients objectively without being influenced by other characteristic of the lesion such as area, pattern, and boundary

Smartphone screening for neonatal jaundice via ambient-subtracted sclera chromaticity

Jaundice is a major cause of mortality and morbidity in the newborn. Globally, early identification and home monitoring are significant challenges in reducing the incidence of jaundice-related neurological damage. Smartphone cameras are promising as colour-based screening tools as they are low-cost, objective and ubiquitous. We propose a novel smartphone method to screen for neonatal jaundice by imaging the sclera. It does not rely on colour calibration cards or accessories, which may facilitate its adoption at scale and in less economically developed regions. Our approach is to explicitly address three confounding factors in relating colour to jaundice: (1) skin pigmentation, (2) ambient light, and (3) camera spectral response. (1) The variation in skin pigmentation is avoided by imaging the sclera. (2) With the smartphone screen acting as an illuminating flash, a flash/ no-flash image pair is captured using the front-facing camera. The contribution of ambient light is subtracted. (3) In principle, this permits a device- and ambient-independent measure of sclera chromaticity following a one-time calibration. We introduce the concept of Scleral-Conjunctival Bilirubin (SCB), in analogy with Transcutaneous Bilirubin (TcB). The scleral chromaticity is mapped to an SCB value. A pilot study was conducted in the UCL Hospital Neonatal Care Unit (n = 37). Neonates were imaged using a specially developed app concurrently with having a blood test for total serum bilirubin (TSB). The better of two models for SCB based on ambient-subtracted sclera chromaticity achieved r = 0.75 (p250μmol/L (area under receiver operating characteristic curve, AUROC, 0.86), and 92% (specificity 67%) in identifying newborns with TSB>205μmol/L (AUROC 0.85). These results are comparable to modern transcutaneous bilirubinometers

Chromatic filters for color vision deficiencies

Dissertação de mestrado em Optometria AvançadaAbout 10% of the population have some form of color vision deficiency. One of the most sever deficiencies is dichromacy. Dichromacy impairs color vision and impoverishes the discrimination of surface colors in natural scenes. Computational estimates based on hyperspectral imaging data from natural scenes suggest that dichromats can discriminate only about 7% of the number of colors discriminated by normal observers on natural scenes. These estimates, however, assume that the colors are equally frequent. Yet, pairs of color confused by dichromats may be rare and thus have small impact on the overall perceived chromatic diversity. By using an experimental setup that allows visual comparation between different spectra selected form hyperspectral images of natural scenes, it was estimated that the number of pairs that dichromats could discriminate was almost 70% of those discriminated by normal observers, a fraction much higher than anticipated from estimates of the number of discernible colors on natural scenes. Therefore, it may be rare for a dichromat to encounter two objects of different colors that he confounds. Thus, chromatic filters for color vision deficiencies intended to improve all colors in general may constitute low practical value. On this work it is proposed a method to compute filters specialized for a specific color-detection task, by taking into account the user’s color vision type, the local illuminant, and the reflectance spectra of the objects intended to be distinguished during that task. This method was applied on a case of a medical practitioner with protanopia to idealize a filter to improve detection of erythema on the skin of its patients. The filter improved the mean color difference between erythema and normal skin by 44%.Cerca de 10% da população possui alguma forma de deficiência de visão de cor. Uma das deficiências mais severas é a dicromacia. Dicromacia prejudica a visão das cores e empobrece a discriminação de superficies coloridas em cenas naturais. Estimativas computacionais baseadas em dados de imagens hiperespectrais de cenas naturais sugerem que dicromatas só pode discriminar cerca de 7% do número de cores discriminadas por observadores normais em cenas naturais. Estas estimativas, no entanto, assumem que todas as cores são igualmente frequentes. Contudo, pares de cores confundidos por dichromats podem ser raros e, portanto, têm pequeno impacto na diversidade cromática global percebida. Ao usar uma montagem experimental que permite comparação visual entre espectros diferentes selecionados a partir de imagens hiperespectrais de cenas naturais, estimou-se que o número de pares que dicromatas poderiam discriminar era quase 70% dos discriminados por observadores normais, uma fração muito maior do que o antecipado a partir de estimativas do número de cores percebidas em cenas naturais. Portanto, pode ser raro para um dicromat para encontrar dois objetos cujas cores ele confunda. Assim, filtros cromático para deficiências de visão das cores pretendidos para melhorar todas as cores em geral podem constituir baixo valor prático. Neste trabalho é proposto um método para calcular filtros especializados para uma tarefa específica de detecção de cor, tendo em conta o tipo de visão de cor do utilizador, o iluminante local, e os espectros de reflectancia dos objetos pretendidos a serem distinguidos durante essa tarefa. Este método foi aplicado em um caso de um médico com Protanopia para idealizar um filtro para melhorar a detecção de eritema na pele de seus pacientes. O filtro melhorou a diferença média de cor entre o eritema e a pele normal por 44%

Translational Functional Imaging in Surgery Enabled by Deep Learning

Many clinical applications currently rely on several imaging modalities such as Positron Emission Tomography (PET), Magnetic Resonance Imaging (MRI), Computed Tomography (CT), etc. All such modalities provide valuable patient data to the clinical staff to aid clinical decision-making and patient care. Despite the undeniable success of such modalities, most of them are limited to preoperative scans and focus on morphology analysis, e.g. tumor segmentation, radiation treatment planning, anomaly detection, etc. Even though the assessment of different functional properties such as perfusion is crucial in many surgical procedures, it remains highly challenging via simple visual inspection. Functional imaging techniques such as Spectral Imaging (SI) link the unique optical properties of different tissue types with metabolism changes, blood flow, chemical composition, etc. As such, SI is capable of providing much richer information that can improve patient treatment and care. In particular, perfusion assessment with functional imaging has become more relevant due to its involvement in the treatment and development of several diseases such as cardiovascular diseases. Current clinical practice relies on Indocyanine Green (ICG) injection to assess perfusion. Unfortunately, this method can only be used once per surgery and has been shown to trigger deadly complications in some patients (e.g. anaphylactic shock). This thesis addressed common roadblocks in the path to translating optical functional imaging modalities to clinical practice. The main challenges that were tackled are related to a) the slow recording and processing speed that SI devices suffer from, b) the errors introduced in functional parameter estimations under changing illumination conditions, c) the lack of medical data, and d) the high tissue inter-patient heterogeneity that is commonly overlooked. This framework follows a natural path to translation that starts with hardware optimization. To overcome the limitation that the lack of labeled clinical data and current slow SI devices impose, a domain- and task-specific band selection component was introduced. The implementation of such component resulted in a reduction of the amount of data needed to monitor perfusion. Moreover, this method leverages large amounts of synthetic data, which paired with unlabeled in vivo data is capable of generating highly accurate simulations of a wide range of domains. This approach was validated in vivo in a head and neck rat model, and showed higher oxygenation contrast between normal and cancerous tissue, in comparison to a baseline using all available bands. The need for translation to open surgical procedures was met by the implementation of an automatic light source estimation component. This method extracts specular reflections from low exposure spectral images, and processes them to obtain an estimate of the light source spectrum that generated such reflections. The benefits of light source estimation were demonstrated in silico, in ex vivo pig liver, and in vivo human lips, where the oxygenation estimation error was reduced when utilizing the correct light source estimated with this method. These experiments also showed that the performance of the approach proposed in this thesis surpass the performance of other baseline approaches. Video-rate functional property estimation was achieved by two main components: a regression and an Out-of-Distribution (OoD) component. At the core of both components is a compact SI camera that is paired with state-of-the-art deep learning models to achieve real time functional estimations. The first of such components features a deep learning model based on a Convolutional Neural Network (CNN) architecture that was trained on highly accurate physics-based simulations of light-tissue interactions. By doing this, the challenge of lack of in vivo labeled data was overcome. This approach was validated in the task of perfusion monitoring in pig brain and in a clinical study involving human skin. It was shown that this approach is capable of monitoring subtle perfusion changes in human skin in an arm clamping experiment. Even more, this approach was capable of monitoring Spreading Depolarizations (SDs) (deoxygenation waves) in the surface of a pig brain. Even though this method is well suited for perfusion monitoring in domains that are well represented with the physics-based simulations on which it was trained, its performance cannot be guaranteed for outlier domains. To handle outlier domains, the task of ischemia monitoring was rephrased as an OoD detection task. This new functional estimation component comprises an ensemble of Invertible Neural Networks (INNs) that only requires perfused tissue data from individual patients to detect ischemic tissue as outliers. The first ever clinical study involving a video-rate capable SI camera in laparoscopic partial nephrectomy was designed to validate this approach. Such study revealed particularly high inter-patient tissue heterogeneity under the presence of pathologies (cancer). Moreover, it demonstrated that this personalized approach is now capable of monitoring ischemia at video-rate with SI during laparoscopic surgery. In conclusion, this thesis addressed challenges related to slow image recording and processing during surgery. It also proposed a method for light source estimation to facilitate translation to open surgical procedures. Moreover, the methodology proposed in this thesis was validated in a wide range of domains: in silico, rat head and neck, pig liver and brain, and human skin and kidney. In particular, the first clinical trial with spectral imaging in minimally invasive surgery demonstrated that video-rate ischemia monitoring is now possible with deep learning

The effects of scarring on face recognition

The focus of this research is the effects of scarring on face recognition. Face recognition is a common biometric modality implemented for access control operations such as customs and borders. The recent report from the Special Group on Issues Affecting Facial Recognition and Best Practices for their Mitigation highlighted scarring as one of the emerging challenges. The significance of this problem extends to the ISO/IEC and national agencies are researching to enhance their intelligence capabilities. Data was collected on face images with and without scars, using theatrical special effects to simulate scarring on the face and also from subjects that have developed scarring within their lifetime. A total of 60 subjects participated in this data collection, 30 without scarring of any kind and 30 with preexisting scars. Controlled data on scarring is problematic for face recognition research as scarring has various manifestations among individuals, yet is universal in that all individuals will manifest some degree of scarring. Effect analysis was done with controlled scarring to observe the factor alone, and wild scarring that is encountered during operations for realistic contextualization. Two environments were included in this study, a controlled studio that represented an ideal face capture setting and a mock border control booth simulating an operational use case

Photonics simulation and modelling of skin for design of spectrocutometer

fi=vertaisarvioitu|en=peerReviewed

Screening for Neonatal Jaundice by Smartphone Sclera Imaging

Jaundice is observed in over 60% of neonates and must be carefully monitored. Ifsevere cases go unnoticed, death or permanent disability can result. Neonatal jaun-dice causes 100,000 deaths yearly, with low-income countries in Africa and SouthAsia particularly affected. There is an unmet need for an accessible and objectivescreening method. This thesis proposes a smartphone camera-based method forscreening based on quantification of yellow discolouration in the sclera.The primary aim is to develop and test an app to screen for neonatal jaundicethat requires only the smartphone itself. To this end, a novel ambient subtractionmethod is proposed and validated, with less dependence on external hardware orcolour cards than previous app-based methods. Another aim is to investigate thebenefits of screening via the sclera. An existing dataset of newborn sclera images(n=87) is used to show that sclera chromaticity can predict jaundice severity.The neoSCB app is developed to predict total serum bilirubin (TSB) fromambient-subtracted sclera chromaticity via a flash/ no-flash image pair. A studyis conducted in Accra, Ghana to evaluate the app. With 847 capture sessions, thisis the largest study on image-based jaundice detection to date. A model trained onsclera chromaticity is found to be more accurate than one based on skin. The modelis validated on an independent dataset collected at UCLH (n=38).The neoSCB app has a sensitivity of 100% and a specificity of 76% in iden-tifying neonates with TSB≥250μmol/L (n=179). This is equivalent to the TcB(JM-105) data collected concurrently, and as good as the best-performing app in theliterature (BiliCam). Following a one-time calibration, neoSCB works without spe-cialist equipment, which could help widen access to effective jaundice screening

Spectral imaging of human portraits and image quality

This dissertation addresses the problem of capturing spectral images for human portraits and evaluating image quality of spectral images. A new spectral imaging approach is proposed in this dissertation for spectral images of human portraits. Thorough statistical analysis is performed for spectral reflectances from various races and different face parts. A spectral imaging system has been designed and calibrated for human portraits. The calibrated imaging system has the ability to represent not only the facial skin but also the spectra of lips, eyes and hair from various races as well. The generated spectral images can be applied to color-imaging system design and analysis. To evaluate the image quality of spectral imaging systems, a visual psychophysical image quality experiment has been performed in this dissertation. The spectral images were simulated based on real spectral imaging system. Meaningful image quality results have been obtained for spectral images generated from different spectral imaging systems. To bridge the gap between the physical measures and subjective visual perceptions of image quality, four image distortion factors were defined. Image quality metrics were obtained and evaluated based statistical analysis and multiple analysis. The image quality metrics have high correlation with subjective assessment for image quality. The image quality contribution of the distortion factors were evaluated. As an extension of the work other researchers in MCSL have initiated, this dissertation research will, working with other researchers in MCSL, put effort to build a publicly accessible database of spectral images, Lippmann2000

Evaluation and optimal design of spectral sensitivities for digital color imaging

The quality of an image captured by color imaging system primarily depends on three factors: sensor spectral sensitivity, illumination and scene. While illumination is very important to be known, the sensitivity characteristics is critical to the success of imaging applications, and is necessary to be optimally designed under practical constraints. The ultimate image quality is judged subjectively by human visual system. This dissertation addresses the evaluation and optimal design of spectral sensitivity functions for digital color imaging devices. Color imaging fundamentals and device characterization are discussed in the first place. For the evaluation of spectral sensitivity functions, this dissertation concentrates on the consideration of imaging noise characteristics. Both signal-independent and signal-dependent noises form an imaging noise model and noises will be propagated while signal is processed. A new colorimetric quality metric, unified measure of goodness (UMG), which addresses color accuracy and noise performance simultaneously, is introduced and compared with other available quality metrics. Through comparison, UMG is designated as a primary evaluation metric. On the optimal design of spectral sensitivity functions, three generic approaches, optimization through enumeration evaluation, optimization of parameterized functions, and optimization of additional channel, are analyzed in the case of the filter fabrication process is unknown. Otherwise a hierarchical design approach is introduced, which emphasizes the use of the primary metric but the initial optimization results are refined through the application of multiple secondary metrics. Finally the validity of UMG as a primary metric and the hierarchical approach are experimentally tested and verified