Resolution invariant wavelet features of melanoma studied by SVM classifiers

This article refers to the Computer Aided Diagnosis of the melanoma skin cancer. We derive wavelet-based features of melanoma from the dermoscopic images of pigmental skin lesions and apply binary C-SVM classifiers to discriminate malignant melanoma from dysplastic nevus. The aim of this research is to select the most efficient model of the SVM classifier for various image resolutions and to search for the best resolution-invariant wavelet bases. We show AUC as a function of the wavelet number and SVM kernels optimized by the Bayesian search for two independent data sets. Our results are compatible with the previous experiments to discriminate melanoma in dermoscopy images with ensembling and feed-forward neural networks

Computer-Aided Diagnosis for Melanoma using Ontology and Deep Learning Approaches

The emergence of deep-learning algorithms provides great potential to enhance the prediction performance of computer-aided supporting diagnosis systems. Recent research efforts indicated that well-trained algorithms could achieve the accuracy level of experienced senior clinicians in the Dermatology field. However, the lack of interpretability and transparency hinders the algorithms’ utility in real-life. Physicians and patients require a certain level of interpretability for them to accept and trust the results. Another limitation of AI algorithms is the lack of consideration of other information related to the disease diagnosis, for example some typical dermoscopic features and diagnostic guidelines. Clinical guidelines for skin disease diagnosis are designed based on dermoscopic features. However, a structured and standard representation of the relevant knowledge in the skin disease domain is lacking. To address the above challenges, this dissertation builds an ontology capable of formally representing the knowledge of dermoscopic features and develops an explainable deep learning model able to diagnose skin diseases and dermoscopic features. Additionally, large-scale, unlabeled datasets can learn from the trained model and automate the feature generation process. The computer vision aided feature extraction algorithms are combined with the deep learning model to improve the overall classification accuracy and save manual annotation efforts

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

Computational Methods for Pigmented Skin Lesion Classification in Images: Review and Future Trends

Skin cancer is considered as one of the most common types of cancer in several countries, and its incidence rate has increased in recent years. Melanoma cases have caused an increasing number of deaths worldwide, since this type of skin cancer is the most aggressive compared to other types. Computational methods have been developed to assist dermatologists in early diagnosis of skin cancer. An overview of the main and current computational methods that have been proposed for pattern analysis and pigmented skin lesion classification is addressed in this review. In addition, a discussion about the application of such methods, as well as future trends, is also provided. Several methods for feature extraction from both macroscopic and dermoscopic images and models for feature selection are introduced and discussed. Furthermore, classification algorithms and evaluation procedures are described, and performance results for lesion classification and pattern analysis are given

Dermoscopy as a technique for the early identification of melanoma. Current status and future pespectives

Το μελάνωμα αν και αποτελεί τη σπανιότερη κακοήθεια του δέρματος, ευθύνεται για το 90% των θανάτων εξαιτίας δερματικών καρκίνων. Από την άλλη πλευρά, είναι ένας εξαιρετικής πρόγνωσης όγκος αν ανιχνευθεί στα αρχικά στάδια. Το γεγονός αυτό καθιστά επιτακτική την έγκαιρη διάγνωση του και εδώ καταδεικνύεται ο πολύτιμος ρόλος της δερματοσκόπησης. Ο στόχος της μελέτης αυτής ήταν να ανασκοπηθεί η βιβλιογραφία αναφορικά με τη χρήση του δερματοσκοπίου στην ορθή και έγκαιρη διάγνωση του μελανώματος. Η χρήση του έχει αποδειχτεί ότι αυξάνει τη διαγνωστική ευαισθησία κατά 30% ενώ στα χέρια ενός έμπειρου δερματοσκόπου αγγίζει ποσοστά ευαισθησίας 92% και ειδικότητας 99%. Πολλοί κανόνες έχουν προταθεί για τη δερματοσκοπική ανάλυση μίας μελαγχρωματικής βλάβης, πέραν όλων αυτών όμως η υιοθέτηση βασικών αρχών δερματοσκοπικής εξέτασης είναι εις θέση να εξασφαλίσει την έγκαιρη διάγνωση αλλά και να αποτρέψει τον κίνδυνο μη διάγνωσης ενός μελανώματος.Cutaneous melanoma is the rarest skin tumor but causes the 90% of skin cancer mortality. Its incidence increases steadily during the last decades being placed among the six most common malignancies among men and women. Compared with other forms of cancer CM remains one with relatively high 5 year survival, especially when diagnosed at an early stage. Biopsy remains the gold standard for the diagnosis of cutaneous melanoma provided that the physician will suspect and excise the skin lesion. However, the challenge is to identify as early as possible lesions that have the possibility of being a melanoma. Dermoscope is an easy to use, hand held device, which enables the evaluation of skin lesions by observing lesion’s color, patterns, vessels and structures. Nowadays, its use has gained profit and dermoscope is now considered as the dermatologist’s stethoscope. With a magnification of 10 to 70 fold it represents a valuable tool between histopathology and naked eye examination. Dermoscopy has been shown to increase diagnostic sensitivity for melanoma up to 35% compared to naked eye examination. Many algorithms are described for the evaluation of pigmented lesions and the early identification of melanoma. Beyond them, looking basically all person’s lesions even only for 10 sec, undressing every patient, comparing and monitoring irregular moles, excising every doubtful nodular lesions and combining clinical, dermoscopical and histopathological criteria represents a more general approach in the direction of avoiding to missing melanoma

Enhanced rotational feature points matching using orientation correction

In matching between images, several techniques have been developed particularly for estimating orientation assignment in order to make feature points invariant to rotation. However, imperfect estimation of the orientation assignment may lead to feature mismatching and a low number of correctly matched points. Additionally, several possible candidates with high correlation values for one feature in the reference image may lead to matching confusion. In this paper, we propose a post-processing matching technique that will not only increase the number of correctly matched points but also manage to solve the above mentioned two issues. The key idea is to modify feature orientation based on the relative rotational degree between two images, obtained by taking the difference between the major correctly matched points in the first matching cycle. From the analysis, our proposed method shows that the number of detected points correctly matched with the reference image can be increased by up to 50%. In addition, some mismatched points due to similar correlation values in the first matching round can be corrected. Another advantage of the proposed algorithm it that it can be applied to other state-of-the-art orientation assignment techniques

Enhanced rotational feature points matching using orientation correction

In matching between images, several techniques have been developed particularly for estimating orientation assignment in order to make feature points invariant to rotation. However, imperfect estimation of the orientation assignment may lead to feature mismatching and a low number of correctly matched points. Additionally, several possible candidates with high correlation values for one feature in the reference image may lead to matching confusion. In this paper, we propose a post-processing matching technique that will not only increase the number of correctly matched points but also manage to solve the above mentioned two issues. The key idea is to modify feature orientation based on the relative rotational degree between two images, obtained by taking the difference between the major correctly matched points in the first matching cycle. From the analysis, our proposed method shows that the number of detected points correctly matched with the reference image can be increased by up to 50%. In addition, some mismatched points due to similar correlation values in the first matching round can be corrected. Another advantage of the proposed algorithm it that it can be applied to other state-of-the-art orientation assignment techniques

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