40 research outputs found

    A survey, review, and future trends of skin lesion segmentation and classification

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    The Computer-aided Diagnosis or Detection (CAD) approach for skin lesion analysis is an emerging field of research that has the potential to alleviate the burden and cost of skin cancer screening. Researchers have recently indicated increasing interest in developing such CAD systems, with the intention of providing a user-friendly tool to dermatologists to reduce the challenges encountered or associated with manual inspection. This article aims to provide a comprehensive literature survey and review of a total of 594 publications (356 for skin lesion segmentation and 238 for skin lesion classification) published between 2011 and 2022. These articles are analyzed and summarized in a number of different ways to contribute vital information regarding the methods for the development of CAD systems. These ways include: relevant and essential definitions and theories, input data (dataset utilization, preprocessing, augmentations, and fixing imbalance problems), method configuration (techniques, architectures, module frameworks, and losses), training tactics (hyperparameter settings), and evaluation criteria. We intend to investigate a variety of performance-enhancing approaches, including ensemble and post-processing. We also discuss these dimensions to reveal their current trends based on utilization frequencies. In addition, we highlight the primary difficulties associated with evaluating skin lesion segmentation and classification systems using minimal datasets, as well as the potential solutions to these difficulties. Findings, recommendations, and trends are disclosed to inform future research on developing an automated and robust CAD system for skin lesion analysis

    Using adaptive thresholding and skewness correction to detect gray areas in melanoma \u3ci\u3ein situ\u3c/i\u3e images

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    The incidence of melanoma in situ (MIS) is growing significantly. Detection at the MIS stage provides the highest cure rate for melanoma, but reliable detection of MIS with dermoscopy alone is not yet possible. Adjunct dermoscopic instrumentation using digital image analysis may allow more accurate detection of MIS. Gray areas are a critical component of MIS diagnosis, but automatic detection of these areas remains difficult because similar gray areas are also found in benign lesions. This paper proposes a novel adaptive thresholding technique for automatically detecting gray areas specific to MIS. The proposed model uses only MIS dermoscopic images to precisely determine gray area characteristics specific to MIS. To this aim, statistical histogram analysis is employed in multiple color spaces. It is demonstrated that skew deviation due to an asymmetric histogram distorts the color detection process. We introduce a skew estimation technique that enables histogram asymmetry correction facilitating improved adaptive thresholding results. These histogram statistical methods may be extended to detect any local image area defined by histograms --Abstract, page iv

    Statistical techniques applied to the automatic diagnosis of dermoscopic images

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    An image based system implementing a well‐known diagnostic method is disclosed for the automatic detection of melanomas as support to clinicians. The software procedure is able to recognize automatically the skin lesion within the digital image, measure morphological and chromatic parameters, carry out a suitable classification for the detection of structural dermoscopic criteria provided by the 7‐Point Check. Original contribution is referred to advanced statistical techniques, which are introduced at different stages of the image processing, including the border detection, the extraction of low‐level features and scoring of high order features (namely dermoscopic criteria). The proposed approach is experimentally tested with reference to a large image set of pigmented lesions

    A review of the quantification and classification of pigmented skin lesions: from dedicated to hand-held devices

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    In recent years, the incidence of skin cancer caseshas risen, worldwide, mainly due to the prolonged exposure toharmful ultraviolet radiation. Concurrently, the computerassistedmedical diagnosis of skin cancer has undergone majoradvances, through an improvement in the instrument and detectiontechnology, and the development of algorithms to processthe information. Moreover, because there has been anincreased need to store medical data, for monitoring, comparativeand assisted-learning purposes, algorithms for data processingand storage have also become more efficient in handlingthe increase of data. In addition, the potential use ofcommon mobile devices to register high-resolution imagesof skin lesions has also fueled the need to create real-timeprocessing algorithms that may provide a likelihood for thedevelopment of malignancy. This last possibility allows evennon-specialists to monitor and follow-up suspected skin cancercases. In this review, we present the major steps in the preprocessing,processing and post-processing of skin lesion images,with a particular emphasis on the quantification andclassification of pigmented skin lesions. We further reviewand outline the future challenges for the creation of minimum-feature,automated and real-time algorithms for the detectionof skin cancer from images acquired via common mobiledevices

    Data fusion by using machine learning and computational intelligence techniques for medical image analysis and classification

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    Data fusion is the process of integrating information from multiple sources to produce specific, comprehensive, unified data about an entity. Data fusion is categorized as low level, feature level and decision level. This research is focused on both investigating and developing feature- and decision-level data fusion for automated image analysis and classification. The common procedure for solving these problems can be described as: 1) process image for region of interest\u27 detection, 2) extract features from the region of interest and 3) create learning model based on the feature data. Image processing techniques were performed using edge detection, a histogram threshold and a color drop algorithm to determine the region of interest. The extracted features were low-level features, including textual, color and symmetrical features. For image analysis and classification, feature- and decision-level data fusion techniques are investigated for model learning using and integrating computational intelligence and machine learning techniques. These techniques include artificial neural networks, evolutionary algorithms, particle swarm optimization, decision tree, clustering algorithms, fuzzy logic inference, and voting algorithms. This work presents both the investigation and development of data fusion techniques for the application areas of dermoscopy skin lesion discrimination, content-based image retrieval, and graphic image type classification --Abstract, page v

    Automatic Detection of Critical Dermoscopy Features for Malignant Melanoma Diagnosis

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    Improved methods for computer-aided analysis of identifying features of skin lesions from digital images of the lesions are provided. Improved preprocessing of the image that 1) eliminates artifacts that occlude or distort skin lesion features and 2) identifies groups of pixels within the skin lesion that represent features and/or facilitate the quantification of features are provided including improved digital hair removal algorithms. Improved methods for analyzing lesion features are also provided

    Fuzzy Color Clustering for Melanoma Diagnosis in Dermoscopy Images

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    A fuzzy logic-based color histogram analysis technique is presented for discriminating benign skin lesions from malignant melanomas in dermoscopy images. The approach extends previous research for utilizing a fuzzy set for skin lesion color for a specified class of skin lesions, using alpha-cut and support set cardinality for quantifying a fuzzy ratio skin lesion color feature. Skin lesion discrimination results are reported for the fuzzy clustering ratio over different regions of the lesion over a data set of 517 dermoscopy images consisting of 175 invasive melanomas and 342 benign lesions. Experimental results show that the fuzzy clustering ratio applied over an eight-connected neighborhood on the outer 25% of the skin lesion with an alpha-cut of 0.08 can recognize 92.6% of melanomas with approximately 13.5% false positive lesions. These results show the critical importance of colors in the lesion periphery. Our fuzzy logic-based description of lesion colors offers relevance to clinical descriptions of malignant melanoma

    Development of a new spectral imaging system for the diagnosis of skin cancer

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    The incidence of skin cancer in Europe, US and Australia has been rising rapidly. Skin cancer accounts for one in three cancers worldwide and a person has 1:25 chance to develop a melanoma, the most aggressive form. Visual inspection followed by histological examination is, still today, the gold standard for clinicians, which is carried out through a dermoscope, a handheld device with a magnifying lens and a white and uniform illumination field. The dermoscopic technique requires considerable training in the interpretation of what is seen and is highly dependent on subjective impressions. In consequence, a large number of unnecessary surgical procedures are performed. For this reason, in this thesis a spectral imaging system to improve skin cancer diagnosis has been developed. This work has been carried out in the framework of the European project DIAGNOPTICS "Diagnosis of skin cancer using optics", which aimed to launch a hospital service based on a multiphotonic platform to improve skin cancer with the combination of four non-invasive novel techniques: 3D and multispectral imaging, optical feedback interferometry and confocal microscopy. The handheld system built included a monochromatic CCD camera attached to an objective lens and a light source containing 32 light emitting diodes (LEDs) with 8 spectral bands from 400 nm to 1000 nm. An acquisition software to control all the components of the multispectral system was programmed as well as a simplest version for physicians. The changes over time of the emission of the LEDs was analysed, and also the linear response of the camera at each wavelength, the uniformity of the LED emission and the short and long-term repeatability of the system in acquiring images, to ensure the good performance of the system. In order to proceed with the Ethical Committee approval and to launch the systems in both hospitals, irradiance and radiance measurements were done according to the standard UNE-EN 62471. A Graphical User Interface (GUI) was developed for the spectral images processing and corresponding analysis, allowing spectral and colorimetric features to be computed in terms of reflectance, absorbance and colour parameters. Furthermore, a segmentation algorithm was also implemented to extract the isolated information from the lesion. For all images calculated in terms of any of the parameters, conventional statistical descriptors were obtained. As a first approach to extracting textural information we also used the analysis of the statistical properties of the histogram. An inclusion criteria and a measurement protocol were established. From all lesions analysed, 620 were measured with the multispectral system, 572 of them had a clinical or histopathological diagnosis, and 502 could be properly segmented. Therefore, 429 skin lesions were finally included in the study: 290 nevi, 95 melanomas and 44 basal cell carcinomas. A classification algorithm was developed in order to decide whether the lesions were malignant (melanomas and basal cell carcinomas) or not (nevi), splitting previously the data into training and validations set of the same size. 15 parameters from 1309 were found to be not redundant providing a 91.3% of sensitivity and 54.5% of specificity. Accordingly, the addition of textural information was shown to be useful for the diagnosis of malignant lesions than the sole use of averaged spectral and colour information. The same steps were carried out for the 3D imaging system also included in the multiphotonic platform. In this case, 3 parameters were found to be useful for the classification providing values of 55.6% and 83.7% of sensitivity and specificity, respectively. Finally, the combination of both system was also tested as a first attempt to improve the detection of melanomas, providing 100% and 72.2% of sensitivity and specificity, respectively. However, the conclusions reached in this case should be taken with caution due to the limited number of lesions.La incidència del càncer de pell a Europa, Estats Units i Austràlia ha anat augmentant ràpidament. Representa un de cada tres càncers a tot el món i una persona té 1:25 oportunitats de desenvolupar un melanoma, la forma més agressiva. Actualment, la inspecció visual amb un dermoscopi seguida d'un examen histològic és l'estàndard utilitzat pels metges a l'hora de diagnosticar-lo. La dermoscòpia requereix una formació considerable per interpretar el que es veu i depèn de les impressions subjectives dels clínics. En conseqüència, es realitzen una gran quantitat de procediments quirúrgics innecessaris. Per aquest motiu, en aquesta tesi s'ha desenvolupat una sistema d'imatge espectral per millorar el diagnòstic del càncer de pell. Aquest treball s'ha realitzat dins el marc del projecte Europeu DIAGNOPTICS ¿Diagnosis del càncer de pell utilitzant òptica?, el qual ha posat a punt un servei hospitalari basat en un plataforma multifotònica que combina quatre tècniques òptiques innovadores: sistemes d'imatge multiespectral 3D, interferometria de retroalimentació i microscòpia confocal. El sistema portàtil desenvolupat inclou una càmera monocromàtica CCD, un objectiu i una font de llum formada per 32 díodes emissors de llum (LED) amb 8 bandes espectrals diferents que emeten des de 400 nm fins a 1000 nm. S'ha preparat un programa d'adquisició per controlar tots els components del sistema així com una versió més simple del mateix pels metges. Per tal d'assegurar el bon funcionament del sistema, es van analitzar els canvis temporals en l'emissió dels LEDs així com la seva uniformitat d'emissió, la resposta lineal de la càmera per cada longitud d'ona i la repetibilitat del sistema pel què fa a l'adquisició d'imatges. Per tal d'obtenir l'aprovació del Comitè Ètic i poder realitzar l'estudi clínic en els hospitals, es van dur a terme mesures d'irradiància i radiància d'acord amb la norma UNE-EN 62471. També es va implementar una interfície gràfica d'usuari (GUI) per al processament de les imatges espectrals i la seva corresponent anàlisi. Aquest algorisme permet calcular paràmetres espectrals i colorimètrics de la pell en termes de reflectància, absorbància i d'altres basats en el color. A més, inclús es va desenvolupar un algorisme de segmentació per extreure informació aïllada de cada lesió. Per a totes les imatges calculades en termes de qualsevol paràmetre, es van obtenir descriptors estadístics convencionals i també es van utilitzar propietats estadístiques dels histogrames com una primera aproximació d'extreure informació de textura de la lesió. Finalment, es van establir els criteris d'inclusió i un protocol de mesura. De totes les lesions analitzades, se'n van mesurar 620, de les quals 572 tenien un diagnòstic clínic o histopatològic; 502 es van poder segmentar adequadament. D'aquesta manera es van incloure 429 lesions cutànies a l'estudi: 290 nevus, 95 melanomes i 44 carcinomes de cèl·lules basals. Es va desenvolupar un algorisme de classificació per determinar si les lesions eren malignes (melanomes i carcinomes de cèl·lules basals) o no (nevus), dividint prèviament les dades en un grup d'entrenament i un altre de validació de la mateixa mida. Es va observar que 15 del 1309 paràmetres proporcionaven informació rellevant per a la classificació, obtenint una sensibilitat del 91,3% i una especificitat del 54,5%. Així doncs, es demostra que la incorporació d'informació de textura és molt útil per al diagnòstic del càncer de pell més enllà de la informació espectral i de color. Aquests mateixos passos es van seguir pel sistema 3D també inclòs en la plataforma multifotònica, tot i que en aquest cas el número de lesions de què es disposava era més limitat. En aquest cas, es van seleccionar 3 paràmetres i es va obtenir una sensibilitat del 55,6% i una especificitat del 83,7%. Finalment, amb la combinació d'ambdós sistemes la sensibilitat obtinguda va ser de100% i l'especificitat del 72,2%.Postprint (published version

    Development of a new spectral imaging system for the diagnosis of skin cancer

    Get PDF
    The incidence of skin cancer in Europe, US and Australia has been rising rapidly. Skin cancer accounts for one in three cancers worldwide and a person has 1:25 chance to develop a melanoma, the most aggressive form. Visual inspection followed by histological examination is, still today, the gold standard for clinicians, which is carried out through a dermoscope, a handheld device with a magnifying lens and a white and uniform illumination field. The dermoscopic technique requires considerable training in the interpretation of what is seen and is highly dependent on subjective impressions. In consequence, a large number of unnecessary surgical procedures are performed. For this reason, in this thesis a spectral imaging system to improve skin cancer diagnosis has been developed. This work has been carried out in the framework of the European project DIAGNOPTICS "Diagnosis of skin cancer using optics", which aimed to launch a hospital service based on a multiphotonic platform to improve skin cancer with the combination of four non-invasive novel techniques: 3D and multispectral imaging, optical feedback interferometry and confocal microscopy. The handheld system built included a monochromatic CCD camera attached to an objective lens and a light source containing 32 light emitting diodes (LEDs) with 8 spectral bands from 400 nm to 1000 nm. An acquisition software to control all the components of the multispectral system was programmed as well as a simplest version for physicians. The changes over time of the emission of the LEDs was analysed, and also the linear response of the camera at each wavelength, the uniformity of the LED emission and the short and long-term repeatability of the system in acquiring images, to ensure the good performance of the system. In order to proceed with the Ethical Committee approval and to launch the systems in both hospitals, irradiance and radiance measurements were done according to the standard UNE-EN 62471. A Graphical User Interface (GUI) was developed for the spectral images processing and corresponding analysis, allowing spectral and colorimetric features to be computed in terms of reflectance, absorbance and colour parameters. Furthermore, a segmentation algorithm was also implemented to extract the isolated information from the lesion. For all images calculated in terms of any of the parameters, conventional statistical descriptors were obtained. As a first approach to extracting textural information we also used the analysis of the statistical properties of the histogram. An inclusion criteria and a measurement protocol were established. From all lesions analysed, 620 were measured with the multispectral system, 572 of them had a clinical or histopathological diagnosis, and 502 could be properly segmented. Therefore, 429 skin lesions were finally included in the study: 290 nevi, 95 melanomas and 44 basal cell carcinomas. A classification algorithm was developed in order to decide whether the lesions were malignant (melanomas and basal cell carcinomas) or not (nevi), splitting previously the data into training and validations set of the same size. 15 parameters from 1309 were found to be not redundant providing a 91.3% of sensitivity and 54.5% of specificity. Accordingly, the addition of textural information was shown to be useful for the diagnosis of malignant lesions than the sole use of averaged spectral and colour information. The same steps were carried out for the 3D imaging system also included in the multiphotonic platform. In this case, 3 parameters were found to be useful for the classification providing values of 55.6% and 83.7% of sensitivity and specificity, respectively. Finally, the combination of both system was also tested as a first attempt to improve the detection of melanomas, providing 100% and 72.2% of sensitivity and specificity, respectively. However, the conclusions reached in this case should be taken with caution due to the limited number of lesions.La incidència del càncer de pell a Europa, Estats Units i Austràlia ha anat augmentant ràpidament. Representa un de cada tres càncers a tot el món i una persona té 1:25 oportunitats de desenvolupar un melanoma, la forma més agressiva. Actualment, la inspecció visual amb un dermoscopi seguida d'un examen histològic és l'estàndard utilitzat pels metges a l'hora de diagnosticar-lo. La dermoscòpia requereix una formació considerable per interpretar el que es veu i depèn de les impressions subjectives dels clínics. En conseqüència, es realitzen una gran quantitat de procediments quirúrgics innecessaris. Per aquest motiu, en aquesta tesi s'ha desenvolupat una sistema d'imatge espectral per millorar el diagnòstic del càncer de pell. Aquest treball s'ha realitzat dins el marc del projecte Europeu DIAGNOPTICS ¿Diagnosis del càncer de pell utilitzant òptica?, el qual ha posat a punt un servei hospitalari basat en un plataforma multifotònica que combina quatre tècniques òptiques innovadores: sistemes d'imatge multiespectral 3D, interferometria de retroalimentació i microscòpia confocal. El sistema portàtil desenvolupat inclou una càmera monocromàtica CCD, un objectiu i una font de llum formada per 32 díodes emissors de llum (LED) amb 8 bandes espectrals diferents que emeten des de 400 nm fins a 1000 nm. S'ha preparat un programa d'adquisició per controlar tots els components del sistema així com una versió més simple del mateix pels metges. Per tal d'assegurar el bon funcionament del sistema, es van analitzar els canvis temporals en l'emissió dels LEDs així com la seva uniformitat d'emissió, la resposta lineal de la càmera per cada longitud d'ona i la repetibilitat del sistema pel què fa a l'adquisició d'imatges. Per tal d'obtenir l'aprovació del Comitè Ètic i poder realitzar l'estudi clínic en els hospitals, es van dur a terme mesures d'irradiància i radiància d'acord amb la norma UNE-EN 62471. També es va implementar una interfície gràfica d'usuari (GUI) per al processament de les imatges espectrals i la seva corresponent anàlisi. Aquest algorisme permet calcular paràmetres espectrals i colorimètrics de la pell en termes de reflectància, absorbància i d'altres basats en el color. A més, inclús es va desenvolupar un algorisme de segmentació per extreure informació aïllada de cada lesió. Per a totes les imatges calculades en termes de qualsevol paràmetre, es van obtenir descriptors estadístics convencionals i també es van utilitzar propietats estadístiques dels histogrames com una primera aproximació d'extreure informació de textura de la lesió. Finalment, es van establir els criteris d'inclusió i un protocol de mesura. De totes les lesions analitzades, se'n van mesurar 620, de les quals 572 tenien un diagnòstic clínic o histopatològic; 502 es van poder segmentar adequadament. D'aquesta manera es van incloure 429 lesions cutànies a l'estudi: 290 nevus, 95 melanomes i 44 carcinomes de cèl·lules basals. Es va desenvolupar un algorisme de classificació per determinar si les lesions eren malignes (melanomes i carcinomes de cèl·lules basals) o no (nevus), dividint prèviament les dades en un grup d'entrenament i un altre de validació de la mateixa mida. Es va observar que 15 del 1309 paràmetres proporcionaven informació rellevant per a la classificació, obtenint una sensibilitat del 91,3% i una especificitat del 54,5%. Així doncs, es demostra que la incorporació d'informació de textura és molt útil per al diagnòstic del càncer de pell més enllà de la informació espectral i de color. Aquests mateixos passos es van seguir pel sistema 3D també inclòs en la plataforma multifotònica, tot i que en aquest cas el número de lesions de què es disposava era més limitat. En aquest cas, es van seleccionar 3 paràmetres i es va obtenir una sensibilitat del 55,6% i una especificitat del 83,7%. Finalment, amb la combinació d'ambdós sistemes la sensibilitat obtinguda va ser de100% i l'especificitat del 72,2%
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