Segmentation of bone structures in 3D CT images based on continuous max- ow optimization

In this paper an algorithm to carry out the automatic segmentation of bone structures in 3D CT images has been implemented. Automatic segmentation of bone structures is of special interest for radiologists and surgeons to analyze bone diseases or to plan some surgical interventions. This task is very complicated as bones usually present intensities overlapping with those of surrounding tissues. This overlapping is mainly due to the composition of bones and to the presence of some diseases such as Osteoarthritis, Osteoporosis, etc. Moreover, segmentation of bone structures is a very time-consuming task due to the 3D essence of the bones. Usually, this segmentation is implemented manually or with algorithms using simple techniques such as thresholding and thus providing bad results. In this paper gray information and 3D statistical information have been combined to be used as input to a continuous max- ow algorithm. Twenty CT images have been tested and di erent coe cients have been computed to assess the performance of our implementation. Dice and Sensitivity values above 0.91 and 0.97 respectively were obtained. A comparison with Level Sets and thresholding techniques has been carried out and our results outperformed them in terms of accuracy.Ministerio de ciencia e innovación TEC2010-21619-C04-02Junta de Andalucía P11-TIC-772

Color-texture image segmentation based on multistep region growing

A new method for color image segmentation is proposed. It is based on a novel region-growing technique with a growth tolerance parameter that changes with step size, which depends on the variance of the actual grown region. Contrast is introduced to determine which value of the tolerance parameter is taken, choosing the one that provides the region with the highest contrast in relation to the background. Color and texture information are extracted from the image by means of a novel idea: the construction of a color distance image and a texture energy image. The color distance image is formed by calculating CIEDE2000 distance in the L*a*b* color space. The texture energy image is extracted from some statistical moments. Then, a novel texture-controlled multistep region-growing process is performed for the segmentation. One advantage of the method is that it is not designed to work with a particular kind of images. This method is tested on 80 natural color images of the Corel photo stock collection with excellent results. Numerical evidence of the quality of these results is provided by comparing them with the manual segmentation of five experts and with another color and texture segmentation algorith

Colorimetric calibration of images captured under unknown illuminants

In this paper the problem of acquiring colorimetrically-calibrated images under multiple uncontrolled illuminants is studied. One of the main applications is diagnosis of different injuries by skin colour analysis, these images would be captured in hospitals where lighting conditions are uncontrolled. To gain some control over illumination, a xenon flash has been used in an attempt to dominate the ambient illumination. A Macbeth ColorChecker DC has been required as a test target to make measurements of observed colour using a digital camera under various illumination conditions. A colorimetric calibration algorithm that allows to convert RGB values under unknown illuminant to RGB values under D50 illuminant is also presented. The use of this algorithm avoids pixel values dependence on lighting conditions

Perceptual color clustering for color image segmentation based on CIEDE2000 color distance

In this paper, a novel technique for color clustering with application to color image segmentation is presented. Clustering is performed by applying the k-means algorithm in the L*a*b* color space. Nevertheless, Euclidean distance is not the metric chosen to measure distances, but CIEDE2000 color difference formula is applied instead. K-means algorithm performs iteratively the two following steps: assigning each pixel to the nearest centroid and updating the centroids so that the empirical quantization error is minimized. In this approach, in the first step, pixels are assigned to the nearest centroid according to the CIEDE2000 color distance. The minimization of the empirical quantization error when using CIEDE2000 involves finding an absolute minimum in a non-linear equation and, therefore, an analytical solution cannot be obtained. As a consequence, a heuristic method to update the centroids is proposed. The proposed algorithm has been compared with the traditional k-means clustering algorithm in the L*a*b* color space with the Euclidean distance. The Borsotti parameter was computed for 28 color images. The new version proposed outperformed the traditional one in all cases

Maximal Contrast Adaptive Region Growing for CT Airway Tree Segmentation

In this paper we propose a fully self-assessed adaptive region growing airway segmentation algorithm. We rely on a standardized and self-assessed region-based approach to deal with varying imaging conditions. Initialization of the algorithm requires prior knowledge of trachea location. This can be provided either by manual seeding or by automatic trachea detection in upper airway tree image slices. The detection of the optimal parameters is managed internally using a measure of the varying contrast of the growing region. Extensive validation is provided for a set of 20 chest CT scans. Our method exhibits very low leakage into the lung parenchyma, so even though the smaller airways are not obtained from the region growing, our fully automatic technique can provide robust and accurate initialization for other method

Segmentation and classification of burn images by color and texture information

In this paper, a burn color image segmentation and classification system is proposed. The aim of the system is to separate burn wounds from healthy skin, and to distinguish among the different types of burns (burn depths). Digital color photographs are used as inputs to the system. The system is based on color and texture information, since these are the characteristics observed by physicians in order to form a diagnosis. A perceptually uniform color space (L *u*v *) was used, since Euclidean distances calculated in this space correspond to perceptual color differences. After the burn is segmented, a set of color and texture features is calculated that serves as the input to a Fuzzy-ARTMAP neural network. The neural network classifies burns into three types of burn depths: superficial dermal, deep dermal, and full thickness. Clinical effectiveness of the method was demonstrated on 62 clinical burn wound images, yielding an average classification success rate of 82

CAD Tool for Burn Diagnosis

In this paper a new system for burn diagnosis is proposed. The aim of the system is to separate burn wounds from healthy skin, and the different types of burns (burn depths) from each other, identifying each one. The system is based on the colour and texture information, as these are the characteristics observed by physicians in order to give a diagnosis. We use a perceptually uniform colour space (L*u*v*), since Euclidean distances calculated in this space correspond to perceptually colour differences. After the burn is segmented, some colour and texture descriptors are calculated and they are the inputs to a Fuzzy-ARTMAP neural network. The neural network classifies them into three types of burns: superficial dermal, deep dermal and full thickness. Clinical effectiveness of the method was demonstrated on 62 clinical burn wound images obtained from digital colour photographs, yielding an average classification success rate of 82 % compared to expert classified images

Automatic Landmarks Detection in Breast Reconstruction Aesthetic Assessment

This paper addresses a fully automatic landmarks detection method for breast reconstruction aesthetic assessment. The set of landmarks detected are the supraesternal notch (SSN), armpits, nipples, and inframammary fold (IMF). These landmarks are commonly used in order to perform anthropometric measurements for aesthetic assessment. The methodological approach is based on both illumination and morphological analysis. The proposed method has been tested with 21 images. A good overall performance is observed, although several improvements must be achieved in order to refine the detection of nipples and SSNs.Junta de Andalucía PI-0223-201

Does a Previous Segmentation Improve the Automatic Detection of Basal Cell Carcinoma Using Deep Neural Networks?

This article belongs to the Special Issue "Image Processing and Analysis for Preclinical and Clinical Applications"Basal Cell Carcinoma (BCC) is the most frequent skin cancer and its increasing incidence is producing a high overload in dermatology services. In this sense, it is convenient to aid physicians in detecting it soon. Thus, in this paper, we propose a tool for the detection of BCC to provide a prioritization in the teledermatology consultation. Firstly, we analyze if a previous segmentation of the lesion improves the ulterior classification of the lesion. Secondly, we analyze three deep neural networks and ensemble architectures to distinguish between BCC and nevus, and BCC and other skin lesions. The best segmentation results are obtained with a SegNet deep neural network. A 98% accuracy for distinguishing BCC from nevus and a 95% accuracy classifying BCC vs. all lesions have been obtained. The proposed algorithm outperforms the winner of the challenge ISIC 2019 in almost all the metrics. Finally, we can conclude that when deep neural networks are used to classify, a previous segmentation of the lesion does not improve the classification results. Likewise, the ensemble of different neural network configurations improves the classification performance compared with individual neural network classifiers. Regarding the segmentation step, supervised deep learning-based methods outperform unsupervised onesMinisterio de Economía y Competitividad DPI2016-81103-RFEDER-US, Junta de Andalucía US-1381640Fondo Social Europeo Iniciativa de Empleo Juvenil EJ3-83-

Clasificación de lesiones de piel basada en filtros de Gabor y color

CONGRESO ANUAL DE LA SOCIEDAD ESPAÑOLA DE INGENIERÍA BIOMÉDICA (CASEIB 2009) (27) (27.2009.CADIZ, ESPAÑA)Cuando se pretende diagnosticar un posible cáncer de piel, los médicos evalúan la lesión siguiendo diferentes reglas. Aunque la más famosa es la regla ABCD (Asimetría, Borde, Color, Diámetro), una técnica muy empleada en Dermatología es clasificar las lesiones siguiendo un análisis de patrones. Este artículo presenta un método novedoso basado en técnicas de filtrado que clasifica imágenes de color correspondientes a diferentes patrones dermatoscópicos. Hemos evaluado nuestro método usando filtros de Gabor y hemos comparado los resultados obtenidos cuando usamos dos espacios diferentes de color (RGB y L*a*b*) y también cuando consideramos o no la información de color. Para implementar esta tarea hemos evaluado la tasa de clasificación usando 8 vectores diferentes de características. Para cada tipo de vector de características hemos usado el 80% de las imágenes de la base de datos para entrenar una red neuronal fuzzy ARTMAP. El restante 20% de las imágenes fue usado para testear la red. La mejor tasa de clasificación es del 90% cuando usamos el espacio de color L*a*b* para la representación de las imágenes.Junta de Andalucía P06-TIC-0141