Towards Automatic Acne Detection Using a MRF Model with Chromophore Descriptors

International audienceThis paper proposes a new acne detection approach using a Markov random field (MRF) model and chromophore descriptors extracted by bilateral decomposition. Compared to most existing acne segmentation methods, the proposed algorithm enables to cope with large-dynamic-range intensity usually existing in conventional RGB acne images captured under uncontrolled environment. Algorithm performance has been tested on acne images of human face from a free public database. Experimental results show that acne segmentation derived from this new approach highly agrees to human visual inspection. Moreover, inflammatory response and hyperpigmentation scar can be well discriminated. It is expected that a computer-assisted diagnostic system for acne severity evaluation will be constructed as a consequence of the present work

Analysis of segmentation methods for acne vulgaris images. Proposal of a new methodology applied to fluorescence images

[EN] Acne vulgaris is one of the most common human pathologies worldwide. Its prevalence causes a high healthcare expenditure. Acne healthcare costs and effects on individuals' quality of life lead to the need of analysing current acne evaluation, treatment and monitoring methods. One of the most common ones is manual lesion counting by a dermatologist. However, this technique has several limitations, such as time spent. That is the reason why the development of new computer-assisted techniques are needed in order to automatically count the acne lesions. Nonetheless, the first step is automatic acne lesion detection on the skin of patients. The aim of this work is to propose a new methodology to solve the acne images segmentation problem, so that the implementation of a system for automatic counting is possible. The results would be a decrease in both time spent and diagnosis errors. With this objective, after doing a systematic review on the state of the art of acne images segmentation methods, fluorescence images of the face of acne patients are obtained. This image modality enhances visualization of the acne lesions. Finally, using the fluorescence images, a segmentation algorithm is implemented in MATLAB.Moncho Santonja, M.; Sanz Alamán, MB.; Defez Garcia, B.; Lengua Lengua, I.; Peris Fajarnes, G. (2020). Analysis of segmentation methods for acne vulgaris images. Proposal of a new methodology applied to fluorescence images. Editorial Universitat Politècnica de València. 431-440. https://doi.org/10.4995/INN2019.2019.10946OCS43144

Skin image illumination modeling and chromophore identication for melanoma diagnosis

International audienceThe presence of illumination variation in dermatological images has a negative impact on the automatic detection and analysis of cutaneous lesions. This paper proposes a new illumination modeling and chromophore identication method to correct lighting variation in skin lesion images, as well as to extract melanin and hemoglobin concentrations of human skin, based on an adaptive bilateral decomposition and a weighted polynomial curve tting, with the knowledge of a multi-layered skin model. Different from state-of-the-art approaches based on the Lambert law, the proposed method, considering both specular reection and diffuse reection of the skin, enables us to address highlight and strong shading effects usually existing in skin color images captured in an uncontrolled environment. The derived melanin and hemoglobin indices, directly relating to the pathological tissue conditions, tend to be less inuenced by external imaging factors and are more efcient in describing pigmentation distributions. Experiments show that the proposed method gave better visual results and superior lesion segmentation, when compared to two other illumination correction algorithms, both designed specically for dermatological images. For computer-aided diagnosis of melanoma, sensitivity achieves 85.52% when using our chromophore descriptors, which is 8~20% higher than those derived from other color descriptors. This demonstrates the benet of the proposed method for automatic skin disease analysis

Image-based evaluation of treatment responses of facial wrinkles using LDDMM registration and Gabor features

International audienceThis paper presents image-based quantitative evaluation of subtle variations in facial wrinkles for the same subject in response to a dermatological treatment. This is a novel application because the time series images of the same subject over a shorter time period of weeks are analyzed as compared to more prevalent inter-person analysis of facial skin/marks. We propose image features based on Gabor filter bank for an accurate quantitative evaluation of variations in facial wrinkles. Since variations in Gabor features are very small on a time period of weeks, we propose a framework to compare image features in key wrinkle sites only while excluding the noise introduced by non-wrinkle sites. The framework consists of finer registration of images using Large Deformation Diffeo-morphic Metric Mapping (LDDMM) and detection of wrinkle sites using Gabor filter bank and morphological image processing. Preliminary experiments show that the framework is useful in calculating variations in Gabor features at detected sites and indicating trends in the response of facial wrinkles to the dermatological treatment

Aplicación de técnicas de iluminación y procesado de imagen para la detección y medición de lesiones

Tesis por compendio[ES] En el presente trabajo se realiza un análisis completo de las técnicas de iluminación y registro de imagen desarrollados hasta el momento y que permiten emplear la fluorescencia intrínseca de estructuras biológicas para aumentar la capacidad de identificación, detección y análisis de lesiones y anomalías que puedan presentarse. El trabajo se ha enfocado principalmente en a) el análisis, validación y desarrollo de técnicas de detección precoz de lesiones asociadas al Carcinoma Escamoso Epidermoide (oncología otorrinolaringológica), así como posibles lesiones precursoras y b) el análisis y desarrollo de una metodología que permita registrar imágenes de fluorescencia y cuantificar mediante la aplicación de técnicas de procesado de imagen la afección provocada por el Acné Vulgaris (dermatología). Se proponen nuevas formas de adquisición, registro y procesado de imágenes de fluorescencia que mejoran de forma objetiva la capacidad de detección y gestión de las anteriores patologías. El desarrollo de la Tesis ha dado lugar a varios resultados. Parte de los resultados se han estructurado en forma de artículos de investigación y trabajos publicados en revistas JCR. Así, la tesis se va a desarrollar por Compendio de Artículos, incluyéndose: a) Artículo de Investigación 1 publicado en revista JCR. Segmentation methods for acne vulgaris images: Proposal of a new methodology applied to fluorescence images. b) Artículo de Investigación 2 publicado en revista JCR. Hough Transform Sensitivivy Factor Calculation Model Applied to the Analysis of Acné Vulgaris Skin Lesions. c) Artículo de Investigación publicado en Congreso Internacional. Analysis of segmentation methods for acne vulgaris images. Proposal of a new methodology applied to fluorescence images. d) Estudio Observacional (modalidad de ensayo clínico para técnicas no invasivas) con DICTAMEN FAVORABLE para su realización con fecha 29 de Septiembre de 2022. El Estudio Observacional ha sido evaluado por los miembros del Comité Ético de Investigación con medicamentos del Departamento Arnau de Vilanova-Llíria. A causa de la pandemia causada por la COVID-19, la ejecución del trabajo se ha visto pospuesta y se iniciará en el último trimestre de 2022. Título: ANÁLISIS DE IMÁGENES DE AUTOFLUORESCENCIA PARA SU USO POTENCIAL COMO SISTEMA NO INVASIVO EN LA DETECCIÓN DE LESIONES ORALES POTENCIALMENTE MALIGNAS. De forma adicional a los trabajos publicados, se ha redactado en forma de review (susceptible de ser publicado) el estado del arte que ha permitido desarrollar el OBJETIVO ESPECÍFICO 3. Se adjunta como Artículo de Investigación susceptible de publicación en revista JCR. Título: Segmentation of acne vulgaris images algorithms. La ejecución del Estudio Observacional se plantea como la línea de investigación a seguir y que da continuidad a la investigación iniciada en la presente Tesis Doctoral. El documento de Tesis está estructurado en 7 capítulos y 11 Anexos. Para el desarrollo del presente trabajo se han planteado tres objetivos específicos. Cada artículo o trabajo publicado se corresponde con el desarrollo de cada uno de los tres objetivos específicos. Así, cada uno de los capítulos 3, 4 y 5 plantea el escenario, desarrollo y conclusiones obtenidas que han dado como resultado cada uno de los trabajos publicados de forma independiente.[CAT] En el present treball es realitza una anàlisi completa de les tècniques d'il·luminació i registre d'imatge desenvolupats fins al moment i que permeten emprar la fluorescència intrínseca d'estructures biològiques per a augmentar la capacitat d'identificació, detecció i anàlisi de lesions i anomalies que puguen presentar-se. El treball s'ha enfocat principalment en a) l'anàlisi, validació i desenvolupament de tècniques de detecció precoç de lesions associades al Carcinoma Escatós Epidermoide (oncologia otorrinolaringològica), així com possibles lesions precursores i b) l'anàlisi i desenvolupament d'una metodologia que permeta registrar imatges de fluorescència i quantificar mitjançant l'aplicació de tècniques de processament d'imatge l'afecció provocada per l'Acne Vulgaris (dermatologia). Es proposen noves formes d'adquisició, registre i processament d'imatges de fluorescència que milloren de manera objectiva la capacitat de detecció i gestió de les anteriors patologies. El desenvolupament de la Tesi ha donat lloc a diversos resultats. Part dels resultats s'han estructurat en forma d'articles d'investigació i treballs publicats en revistes JCR. Així, la tesi es desenvoluparà per Compendi d'Articles, incloent-se: a) Article d'Investigació 1 publicat en revista JCR. Segmentation methods for acne vulgaris images: Proposal of a new methodology applied to fluorescence images. b) Article d'Investigació 2 publicat en revista JCR. Hough Transform Sensitivivy Factor Calculation Model Applied to the Analysis of Acné Vulgaris Skin Lesions. c) Article d'Investigació publicat en Congrés Internacional. Analysis of segmentation methods for acne vulgaris images. Proposal of a new methodology applied to fluorescence images. d) Estudi Observacional (modalitat d'assaig clínic per a tècniques no invasives) amb DICTAMEN FAVORABLE per a la seua realització amb data 29 de Setembre de 2022. L'Estudi Observacional ha sigut avaluat pels membres del Comité Ètic d'Investigació amb medicaments del Departament Arnau de Vilanova-Llíria. A causa de la pandèmia causada per la COVID-19, l'execució del treball s'ha vist posposada i s'iniciarà en l'últim trimestre de 2022. Títol: ANÁLISIS DE IMÁGENES DE AUTOFLUORESCENCIA PARA SU USO POTENCIAL COMO SISTEMA NO INVASIVO EN LA DETECCIÓN DE LESIONES ORALES POTENCIALMENTE MALIGNAS. De manera addicional als treballs publicats, s'ha redactat en forma de review (susceptible de ser publicat) l'estat de l'art que ha permés desenvolupar l'OBJECTIU ESPECÍFIC 3. S'adjunta com a Article d'Investigació susceptible de publicació en revista JCR. Títol: Segmentation of acne vulgaris images algorithms. L'execució de l'Estudi Observacional es planteja com la línia d'investigació a seguir i que dona continuïtat a la investigació iniciada en la present Tesi Doctoral. El document de Tesi està estructurat en 7 capítols i 11 Annexos. Per al desenvolupament del present treball s'han plantejat tres objectius específics. Cada article o treball publicat es correspon amb el desenvolupament de cadascun dels tres objectius específics. Així, cadascun dels capítols 3, 4 i 5 planteja l'escenari, desenvolupament i conclusions obtingudes que han donat com a resultat cadascun dels treballs publicats de manera independent.[EN] In the present work, a complete analysis is made of the illumination and image recording techniques developed so far that allow the use of intrinsic fluorescence of biological structures to increase the capacity of identification, detection and analysis of lesions and anomalies that may occur. The work has focused mainly on a) the analysis, validation and development of techniques for the early detection of lesions associated with Squamous Epidermoid Carcinoma (otorhinolaryngological oncology), as well as possible precursor lesions, and b) the analysis and development of a methodology for recording fluorescence images and quantifying the condition caused by Acne Vulgaris (dermatology) through the application of image processing techniques. New ways of acquisition, registration and processing of fluorescence images are proposed to objectively improve the capacity of detection and management of the previous pathologies. The development of the Thesis has led to several results. Part of the results have been structured in the form of research articles and papers published in JCR journals. Thus, the thesis is going to be developed by Compendium of Articles, including: a) Research Article 1 published in JCR journal. Segmentation methods for acne vulgaris images: Proposal of a new methodology applied to fluorescence images. b) Research Article 2 published in JCR journal. Hough Transform Sensitivity Factor Calculation Model Applied to the Analysis of Acne Vulgaris Skin Lesions. c) Research Article published in International Congress. Analysis of segmentation methods for acne vulgaris images. Proposal of a new methodology applied to fluorescence images. d) Observational study (clinical trial modality for non-invasive techniques) with FAVORABLE OPINION for its realization on September 29, 2022. The Observational Study has been evaluated by the members of the Ethics Committee for Research with Medicines of the Arnau de Vilanova-Llíria Department. Due to the pandemic caused by COVID-19, the execution of the work has been postponed and will start in the last quarter of 2022. Title: ANALYSIS OF AUTOFLUORESCENCE IMAGES FOR POTENTIAL USE AS A NON-INVASIVE SYSTEM IN THE DETECTION OF POTENTIALLY MALIGNANT ORAL LESIONS. In addition to the published works, the state of the art that has allowed the development of SPECIFIC OBJECTIVE 3 has been written in the form of a review (susceptible of being published). It is attached as a Research Article susceptible of being published in a JCR journal. Title: Segmentation of acne vulgaris images algorithms. The execution of the Observational Study is proposed as the line of research to be followed and which gives continuity to the research initiated in the present Doctoral Thesis. The Thesis document is structured in 7 chapters and 11 Annexes. Three specific objectives have been set for the development of this work. Each article or published work corresponds to the development of each of the three specific objectives. Thus, each of the chapters 3, 4 and 5 presents the scenario, development and conclusions obtained that have resulted in each of the works published independently.Moncho Santonja, M. (2022). Aplicación de técnicas de iluminación y procesado de imagen para la detección y medición de lesiones [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/191027Compendi

Artificial Intelligence for Skin Lesion Analysis based on Computer Vision and Deep Learning

Skin lesions appear in various sizes and forms and can be localised in one place or spread across the whole body due to different conditions. Dermatologists typically undertake physical examinations to diagnose skin lesions. However, this task costs time and requires excessive effort and can be inconsistent. Depending on the type of lesion and whether or not malignancy is present, additional diagnostic testing, such as imaging or biopsy, may be needed. Computer-aided diagnosis (CAD) systems, using clinical and dermoscopic images, could provide a quantitative assessment tool to help clinicians identify skin lesions and evaluate their severity. The recent progress in computer vision and deep learning has encouraged researchers to harness medical imaging data to develop powerful tools which could provide better diagnosis, treatment and prediction of skin conditions. By leveraging artificial intelligence techniques, including computer vision and deep learning, this work introduces intelligent computerised approaches using dermoscopic and clinical images to analyse and identify two types of skin lesions producing enhanced medical information. This thesis designed, realised, and evaluated the benefit of features learned automatically from images through the stacked layers of convolution filters in the convolutional neural network (CNN) models. The final objective of conducting the research in this thesis is to benefit patients with skin lesion condition assessment and skin cancer identification without adding to the already high medical costs. An automated regression-based method has been developed in this thesis for acne counting and severity grading from clinical facial images. In addition to the acne lesions, another type of skin lesion has been considered, represented by melanoma-related lesions. Two pipelines have been presented in this thesis to identify melanoma lesions. The first framework benchmarks and evaluates several CNN models for melanoma and non- melanoma classification from only dermoscopic images. While the second developed model for melanoma detection integrates the seven-point checklist scheme with CNN using both clinical and dermoscopic images. The experimental results of the work presented in this thesis manifest improved/ competitive performance compared to the state-of-the-art skin analysis methods using several evaluation metrics. The findings of the developed approaches demonstrated effective analysis of skin lesions with high accuracy, reducing the risk of misdiagnosis, and providing a more efficient means of detecting melanoma and automated acne lesion severity grading. Additionally, the application of computational intelligence allows for cost savings by reducing the need for manual analysis and enabling the automation of grading support, resulting in a more reliable and consistent process. Overall, the new automated methods based on computational intelligence demonstrate the benefits of developing computer vision and deep learning techniques for skin lesion analysis towards early skin cancer identification and cost-effective and robust grading support