Segmentation of Melanoma Skin Lesion Using Perceptual Color Difference Saliency with Morphological Analysis

The prevalence of melanoma skin cancer disease is rapidly increasing as recorded death cases of its patients continue to annually escalate. Reliable segmentation of skin lesion is one essential requirement of an efficient noninvasive computer aided diagnosis tool for accelerating the identification process of melanoma. This paper presents a new algorithm based on perceptual color difference saliency along with binary morphological analysis for segmentation of melanoma skin lesion in dermoscopic images. The new algorithm is compared with existing image segmentation algorithms on benchmark dermoscopic images acquired from public corpora. Results of both qualitative and quantitative evaluations of the new algorithm are encouraging as the algorithm performs excellently in comparison with the existing image segmentation algorithms

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

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

Scaled conjugate gradient based decision support system for automated diagnosis of skin cancer

Melanoma is the most deathful form of skin cancer but early diagnosis can ensure a high rate of survival. Early diagnosis is one of the greatest challenges due to lack of experience of general practitioners (GPs). This paper presents a clinical decision support system designed for the use of general practitioners, aiming to save time and resources in the diagnostic process. Segmentation, pattern recognition, and lesion detection are the important steps in the proposed decision support system. The system analyses the images to extract the affected area using a novel proposed segmentation method. It determinates the underlying features which indicate the difference between melanoma and benign images and makes a decision. Considering the efficiency of neural networks in classification of complex data, scaled conjugate gradient based neural network is used for classification. The presented work also considers analyzed performance of other efficient neural network training algorithms on the specific skin lesion diagnostic problem and discussed the corresponding findings. The best diagnostic rates obtainedthrough the proposed decision support system are around 92%

Color detection in dermoscopic images of pigmented skin lesions through computer vision techniques

This thesis offers an insight into skin cancer detection, focusing on the extraction of distinct features (color, namely) from potential melanoma lesions. The following document provides an outlook of melanoma analysis, as well as experimental results based on Matlab implementations. The relevance of the work carried out throughout this project resides in the specificity of the study: color is a key characteristic in melanoma inspection. It is usually linked to pattern analysis but seldom the sole object of research. Most lines of work in the field of skin cancer diagnosis associate color with other features such as texture, shape, asymmetry or pattern of the lesion. Studies cement this belief regarding the vital significance of color, as the number of colors in a lesion happens to be the most significant biomarker for determining malignancy. Different image processing techniques will be applied to build statistical models that shape the outcome of the prospective diagnosis. The purpose of the project is the development of an assisting tool able to detect the most prevalent colors in skin pigmented lesions, in order to give a probabilistic result. The strength of this idea lies in the resemblance to actual medical procedures; dermatologists examine color to diagnose melanoma. Simulating medical proceedings is a burgeoning trend in CAD systems because it renders the advancements in this field more likely to be accepted by the medical community. An additional motivation comes from real-life statistics: skin cancer is, by far, the most frequent type of cancer. Moreover, although melanoma is the least common form of skin cancer at only around 1% of all cases, the majority of deaths related to skin cancer are due to melanoma. Furthermore, the rate of melanoma occurrence is particularly high in Spain and has significantly increased in the last decade, hence the importance of reliable diagnosis that is not exclusively contingent on the specialist’s subjective judgment.Ingeniería de Sistemas Audiovisuale

Image analysis for diagnostic support in biomedicine: neuromuscular diseases and pigmented lesions

Tesis descargada desde TESEOEsta tesis presenta dos sistemas implementados mediante técnicas de procesamiento de imagen, para ayuda al diagnóstico de enfermedades neuromusculares a partir de imágenes de microscopía de fluorescencia y análisis de lesiones pigmentadas a partir de imágenes dermoscópicas. El diagnóstico de enfermedades neuromusculares se basa en la evaluación visual de las biopsias musculares por parte del patólogo especialista, lo que conlleva una carga subjetiva. El primer sistema propuesto en esta tesis analiza objetivamente las biopsias musculares y las clasifica en distrofias, atrofias neurógenas o control (sin enfermedad) a través de imágenes de microscopía de fluorescencia. Su implementación reúne los elementos propios de un sistema de ayuda al diagnóstico asistido por ordenador: segmentación, extracción de características, selección de características y clasificación. El procedimiento comienza con una segmentación precisa de las fibras musculares usando morfología matemática y una transformada Watershed. A continuación, se lleva a cabo un paso de extracción de características, en el cual reside la principal contribución del sistema, ya que no solo se extraen aquellas que los patólogos tienen en cuenta para diagnosticar sino características que se escapan de la visión humana. Estas nuevas características se extraen suponiendo que la estructura de la biopsia se comporta como un grafo, en el que los nodos se corresponden con las fibras musculares, y dos nodos están conectados si dos fibras son adyacentes. Para estudiar la efectividad que estos dos conjuntos presentan en la categorización de las biopsias, se realiza una selección de características y una clasi- ficación empleando una red neuronal Fuzzy ARTMAP. El procedimiento concluye con una estimación de la severidad de las biopsias con patrón distrófico. Esta caracterización se realiza mediante un análisis de componentes principales. Para la validación del sistema se ha empleado una base de datos compuesta por 91 imágenes de biopsias musculares, de las cuales 71 se consideran imágenes de entrenamiento y 20 imágenes de prueba. Se consigue una elevada tasa de aciertos de clasificacion y se llega a la importante conclusión de que las nuevas características estructurales que no pueden ser detectadas por inspección visual mejoran la identificación de biopsias afectadas por atrofia neurógena. La segunda parte de la tesis presenta un sistema de clasificación de lesiones pigmentadas. Primero se propone un algoritmo de segmentación de imágenes en color para ais lar la lesión de la piel circundante. Su desarrollo se centra en conseguir un algoritmo relacionado con las diferencias color percibidas por el ojo humano. Consiguiendo así, no solo un método de segmentación de lesiones pigmentadas sino un algoritmo de segmentación de propósito general. El método de segmentación propuesto se basa en un gradiente para imágenes en color integrado en una técnica de level set para detección de bordes. La elección del gradiente se derivada a partir de un análisis de tres gradientes de color implementados en el espacio de color uniforme CIE L∗a∗b∗ y basados en las ecuaciones de diferencia de color desarrolladas por la comisión internacional de iluminación (CIELAB, CIE94 y CIEDE2000). El principal objetivo de este análisis es estudiar cómo estas ecuaciones afectan en la estimación de los gradientes en términos de correlación con la percepción visual del color. Una técnica de level-set se aplica sobre estos gradientes consiguiendo así un detector de borde que permite evaluar el rendimiento de dichos gradientes. La validación se lleva a cabo sobre una base de datos compuesta por imágenes sintéticas diseñada para tal fin. Se realizaron tanto medidas cuantitativas como cualitativas. Finalmente, se concluye que el detector de bordes basado en la ecuación de diferencias de color CIE94 presenta la mayor correlación con la percepción visual del color. A partir de entonces, la tesis intenta emular el método de análisis de patrones, la técnica de diagnóstico de lesiones pigmentadas de la piel más empleada por los dermatólogos. Este método trata de identificar patrones específicos, pudiendo ser tanto globales como locales. En esta tesis se presenta una amplia revisión de los métodos algorítmicos, publicados en la literatura, que detectan automáticamente dichos patrones a partir de imágenes dermoscópicas de lesiones pigmentadas. Tras esta revisón se advierte que numerosos trabajos se centran en la detección de patrones locales, pero solo unos pocos abordan la detección de patrones globales. El siguiente paso de esta tesis, por tanto, es la propuesta de diferentes métodos de clasi- ficación de patrones globales. El objetivo es identificar tres patrones: reticular, globular y empedrado (considerado un solo patrón) y homogéneo. Los métodos propuestos se basan en un análisis de textura mediante técnicas de modelado. En primer lugar una imagen demoscópica se modela mediante campos aleatorios de Markov, los parámetros estimados de este modelo se consideran características. A su vez, se supone que la distribución de estas características a lo largo de la lesión sigue diferentes modelos: un modelo gaussiano, un modelo de mezcla de gaussianas o un modelo de bolsa de características. La clasificación se lleva a cabo mediante una recuperación de imágenes basada en diferentes métricas de distancia. Para validar los métodos se emplea un conjunto significativo de imágenes dermatológicas, concluyendo que el modelo basado en mezcla de gaussianas proporciona la mejor tasa de clasificación. Además, se incluye una evaluación adicional en la que se clasifican melanomas con patrón multicomponente obteniendo resultados prometedores. Finalmente, se presenta una discusión sobre los hallazgos y conclusiones más relevantes extraídas de esta tesis, así como las líneas futuras que se derivan de este trabajo.Premio Extraordinario de Doctorado U

Computer aided diagnostic support system for skin cancer: A review of techniques and algorithms

Image-based computer aided diagnosis systems have significant potential for screening and early detection of malignant melanoma. We review the state of the art in these systems and examine current practices, problems, and prospects of image acquisition, pre-processing, segmentation, feature extraction and selection, and classification of dermoscopic images. This paper reports statistics and results from the most important implementations reported to date. We compared the performance of several classifiers specifically developed for skin lesion diagnosis and discussed the corresponding findings. Whenever available, indication of various conditions that affect the technique's performance is reported. We suggest a framework for comparative assessment of skin cancer diagnostic models and review the results based on these models. The deficiencies in some of the existing studies are highlighted and suggestions for future research are provided. © 2013 Ammara Masood and Adel Ali Al-Jumaily

Computer Aided Diagnostic Support System for Skin cancer: Review of techniques and algorithms

Image-based computer aided diagnosis systems have significant potential for screening and early detection of malignant melanoma. We review the state of the art in these systems and examine current practices, problems, and prospects of image acquisition, pre-processing, segmentation, feature extraction and selection, and classification of dermoscopic images. This paper reports statistics and results from the most important implementations reported to date. We compared the performance of several classifiers specifically developed for skin lesion diagnosis and discussed the corresponding findings. Whenever available, indication of various conditions that affect the technique’s performance is reported. We suggest a framework for comparative assessment of skin cancer diagnostic models and review the results based on these models. The deficiencies in some of the existing studies are highlighted and suggestions for future research are provided

Evaluation of different segmentation-based approaches for skin disorders from dermoscopic images

Treballs Finals de Grau d'Enginyeria Biomèdica. Facultat de Medicina i Ciències de la Salut. Universitat de Barcelona. Curs: 2022-2023. Tutor/Director: Sala Llonch, Roser, Mata Miquel, Christian, Munuera, JosepSkin disorders are the most common type of cancer in the world and the incident has been lately increasing over the past decades. Even with the most complex and advanced technologies, current image acquisition systems do not permit a reliable identification of the skin lesion by visual examination due to the challenging structure of the malignancy. This promotes the need for the implementation of automatic skin lesion segmentation methods in order to assist in physicians’ diagnostic when determining the lesion's region and to serve as a preliminary step for the classification of the skin lesion. Accurate and precise segmentation is crucial for a rigorous screening and monitoring of the disease's progression. For the purpose of the commented concern, the present project aims to accomplish a state-of-the-art review about the most predominant conventional segmentation models for skin lesion segmentation, alongside with a market analysis examination. With the rise of automatic segmentation tools, a wide number of algorithms are currently being used, but many are the drawbacks when employing them for dermatological disorders due to the high-level presence of artefacts in the image acquired. In light of the above, three segmentation techniques have been selected for the completion of the work: level set method, an algorithm combining GrabCut and k-means methods and an intensity automatic algorithm developed by Hospital Sant Joan de Déu de Barcelona research group. In addition, a validation of their performance is conducted for a further implementation of them in clinical training. The proposals, together with the got outcomes, have been accomplished by means of a publicly available skin lesion image database