Segmentación de imágenes basada en color y textura

En esta tesis se presenta un método para la segmentación de imágenes naturales basado en la técnica de crecimiento de regiones, que toma en consideración la información de color y textura adaptándose a la percepción humana. Para ello reinterpreta el tradicional algoritmo de crecimiento de regiones de forma que la condición de pertenencia y de parada estén determinadas por la distancia perceptiva entre colores, siendo ambas adaptativas y automáticamente ajustadas. De ahí surge la idea de crecimiento de regiones multipaso con condición de pertenencia controlada por textura, extendido a K dimensiones, siendo K el número de colores de referencia encontrados en la zona deseada, como se explicará posteriormente a lo largo de la tesis. Las novedades aportadas en el marco de la segmentación de imágenes en color son: Nuevo algoritmo K-means adaptado a la percepción humana. Nuevo algoritmo de segmentación de imágenes en color mediante crecimiento de regiones adaptado a la percepción humana. Inclusión de información de textura en el método de segmentación. Así mismo, el algoritmo ha sido integrado en una interfaz gráfica amigable para facilitar su uso a personas ajenas al mundo del tratamiento de imágenes

Ingeniería de telecomunicación y género. Indicadores en la Universidad de Sevilla

La Ingeniería de Telecomunicación ha sido tradicionalmente una carrera considerada como masculina, ya que el porcentaje de mujeres que iniciaban su andadura en el campo de las telecomunicaciones era mínimo. Con el paso de los años y el aumento general del número de universitarias, esta titulación no debería ser un campo vedado para las mujeres. Por otro lado, gran parte de estas jóvenes ingenieras dedican su vida laboral a la docencia universitaria y consecuentemente a la investigación, contribuyendo a la innovación y posterior transferencia tecnológica. Las autoras, desde su experiencia como docentes e investigadoras en el Departamento de Teoría de la Señal y Comunicaciones de la Universidad de Sevilla, han realizado un estudio estadístico que pretende proporcionar una visión general de la situación de la mujer en este ámbito. Este trabajo también abarca la complejidad de la conciliación de la vida familiar y laboral, en todas las etapas de la carrera docente

Centroid-Based Clustering with ab-Divergences

Centroid-based clustering is a widely used technique within unsupervised learning algorithms in many research fields. The success of any centroid-based clustering relies on the choice of the similarity measure under use. In recent years, most studies focused on including several divergence measures in the traditional hard k-means algorithm. In this article, we consider the problem of centroid-based clustering using the family of ab-divergences, which is governed by two parameters, a and b. We propose a new iterative algorithm, ab-k-means, giving closed-form solutions for the computation of the sided centroids. The algorithm can be fine-tuned by means of this pair of values, yielding a wide range of the most frequently used divergences. Moreover, it is guaranteed to converge to local minima for a wide range of values of the pair (a, b). Our theoretical contribution has been validated by several experiments performed with synthetic and real data and exploring the (a, b) plane. The numerical results obtained confirm the quality of the algorithm and its suitability to be used in several practical applications.MINECO TEC2017-82807-

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

Principales problemas de los profesores principiantes en la enseñanza universitaria

Se presentan y discuten algunas reflexiones sobre los principales problemas que los profesores principiantes encuentran en la enseñanza universitaria. Dichas dificultades se clasifican y analizan en tres ámbitos: el de la enseñanza, el de las relaciones interpersonales y el de la gestión o el contexto institucional. Se resalta la importancia de una adecuada formación pedagógica por parte del docente novel y el papel de la acción tutorial. Se revisa también los retos que suponen para el profesor principiante la actual reforma del modelo universitario español en el marco del Espacio Europeo de Educación Superior y el conflicto investigación-docencia. Esto porque la actividad investigadora no sólo es indispensable para la continua evolución científica del profesor universitario, sino que también depende de ella su continuidad en la carrera docente. Dicha actividad es a menudo difícil de compatibilizar con la puramente docente, especialmente para el docente principiante

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

Software tool for contrast enhancement and segmentation of melanoma images based on human perception

In this paper we present a software tool for melanoma border detection (MBD). It has been designed to be incorporated in any Computer Aided Diagnosis Tool (CAD) for early detection of melanoma in mass screening programs. The tool is completely automatic, posses a user-friendly interface and does not require any specific hardware. The main steps followed by the implemented algorithm are: uneven illumination correction, color contrast improvement and color image segmentation. All of them are performed in the uniform color space CIE L * a * b * in order to achieve a complete adaptation to human color perception. The program is able to provide not only the final obtained segmentation result but also intermediate graphical outcomes, guiding the user in the process of melanoma detection. This simple, friendly but powerful interface can serve as a support for the medical personnel in the melanoma diagnostic process. The MBD software and some samples of the dermoscopy images used can be downloaded at http://cs.ntu. edu.pk/research.php

Fully automatized parallel segmentation of the optic disc in retinal fundus images

This paper presents a fully automatic parallel software for the localization of the optic disc (OD) in retinal fundus color images. A new method has been implemented with the Graphics Processing Units (GPU) technology. Image edges are extracted using a new operator, called AGP-color segmentator. The resulting image is binarized with Hamadani’s technique and, finally, a new algorithm called Hough circle cloud is applied for the detection of the OD. The reliability of the tool has been tested with 129 images from the public databases DRIVE and DIARETDB1 obtaining an average accuracy of 99.6% and a mean consumed time per image of 7.6 and 16.3 s respectively. A comparison with several state-of-the-art algorithms shows that our algorithm represents a significant improvement in terms of accuracy and efficiency.Ministerio de Economía y Competitividad TIN2012-3743

A Method for Unsupervised Semi-Quantification of Inmunohistochemical Staining with Beta Divergences

In many research laboratories, it is essential to determine the relative expression levels of some proteins of interest in tissue samples. The semi-quantitative scoring of a set of images consists of establishing a scale of scores ranging from zero or one to a maximum number set by the researcher and assigning a score to each image that should represent some predefined characteristic of the IHC staining, such as its intensity. However, manual scoring depends on the judgment of an observer and therefore exposes the assessment to a certain level of bias. In this work, we present a fully automatic and unsupervised method for comparative biomarker quantification in histopathological brightfield images. The method relies on a color separation method that discriminates between two chromogens expressed as brown and blue colors robustly, independent of color variation or biomarker expression level. For this purpose, we have adopted a two-stage stain separation approach in the optical density space. First, a preliminary separation is performed using a deconvolution method in which the color vectors of the stains are determined after an eigendecomposition of the data. Then, we adjust the separation using the non-negative matrix factorization method with beta divergences, initializing the algorithm with the matrices resulting from the previous step. After that, a feature vector of each image based on the intensity of the two chromogens is determined. Finally, the images are annotated using a systematically initialized k-means clustering algorithm with beta divergences. The method clearly defines the initial boundaries of the categories, although some flexibility is added. Experiments for the semi-quantitative scoring of images in five categories have been carried out by comparing the results with the scores of four expert researchers yielding accuracies that range between 76.60% and 94.58%. These results show that the proposed automatic scoring system, which is definable and reproducible, produces consistent results.FEDER / Junta de Andalucía-Consejería de Economía y Conocimiento US-1264994Fondo de Desarrollo (FEDER). Unión Europea PGC2018-096244-B-I00, SAF2016-75442-RMinisterio de Economía, Industria y Competitividad (MINECO). España TEC2017- 82807-

Automated detection of microaneurysms by using region growing and fuzzy artmap neural network

Objective: To assess whether the methodological changes of this new algorithm improves the results of a previously presented strategy. Methods: We enhance the image and filter out the green channel of the digital color retinog- raphy. Multitolerance thresholding was applied to obtain candidate points and make a seed growing region by varying intensities. We took 15 characteristics from each region to train a fuzzy Artmap neural network using 42 retinal photographs. This network was then applied in the study of 11 good quality retinal photographs included in the diabetic retinopathy early detection screening program, with initial stages of retinopathy, obtained with the Topcon NW200 non-mydriatic retinal camera. Results: Two experienced ophthalmologists detected 52 microaneurysms in 11 images. The algorithm detected 39 microaneurysms and 3752 more regions, confirming 38 microa- neurysm and 135 false positives. The sensitivity is improved compared to the previous algorithm, from 60.53% to 73.08%. False positives have dropped from 41.8 to 12.27 per image. Conclusions: The new algorithm is better than the previous one, but there is still room for improvement, especially in the initial determination of seed