Irene Fondón

Unsupervised skin lesions border detection via two-dimensional image analysis

The skin cancer was analyzed by dermoscopy helpful for dermatologists. The classification of melanoma and carcinoma such as basal cell, squamous cell, and merkel cell carcinomas tumors can be increased the sensitivity and specificity. The detection of an automated border is an important step for the correctness of subsequent phases in the computerized melanoma recognition systems. The artifacts such as, dermoscopy-gel, specular reflection and outline (skin lines, blood vessels, and hair or ruler markings) were also contained in the dermoscopic images. In this paper, we present an unsupervised approach for multiple lesion segmentation, modification of Region-based Active Contours (RACs) as well as artifact diminution steps. Iterative thresholding is applied to initialize level set automatically; the stability of curves is enforced by maximum smoothing constraints on Courant-Friedreichs-Lewy (CFL) function. The work has been tested on dermoscopic database of 320 images. The border detection error is quantified by five distinct statistical metrics and manually used to determine the borders from a dermatologist as the ground truth. The segmentation results were compared with other state-of-the-art methods along with the evaluation criteria. The unsupervised border detection system increased the true detection rate (TDR) is 4.31% and reduced the false positive rate (FPR) of 5.28%.

Computer-aided pattern classification system for dermoscopy images.

Computer‐aided pattern classification of melanoma and other pigmented skin lesions is one of the most important tasks for clinical diagnosis. To differentiate between benign and malignant lesions, the extraction of color, architectural order, symmetry of pattern and homogeneity (CASH) is a challenging task.

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 algorithm.

Automatic optic cup segmentation algorithm for retinal fundus images based on random forest classifier

Glaucoma is an eye disease that constitutes the second cause of blindness over the world. Although it cannot be cured, its progression may be prevented if it is early detected. Expert ophthalmologists use as a sign of suffering from the disease, the evaluation of the relationship between optic disc and cup areas in retinal fundus images and, therefore, image processing techniques applied to glaucoma has become an emerging research line. This paper presents a novel technique for the detection of the optic cup in retinal fundus images, which may be included in a glaucoma computer aided diagnosis tool. The method, based on a color space related to human perception and adapted to surrounding conditions, JCh from CIECAM 02 (International Commission on Illumination Color Appearance Model), utilizes a random forest classifier to obtain cup edge pixels. As vessels tend to bend in the edge of the cup, the classifier does not consider all the pixels in the image. In fact, only those belonging to vessels and possessing the highest curvature among their neighbors are taken into account. Another prior knowledge used in the proposed method is the fact that cup area usually posses a bright yellow color. Therefore the feature vector serving as an input for the classifier is made with the curvature, the color of the candidate pixel and its location relative to the OD center. Finally, a basic post processing is performed to join the selected pixels with a smooth curve. The method has been tested in a publicly available database, GlaucomaRepo, from where we used 35 images for training and 55 for test. Five numerical parameters were calculated and a comparison against three color spaces was performed. The results obtained indicate the effectiveness of the approach.

Burn Depth Analysis Using Multidimensional Scaling Applied to Psychophysical Experiment Data

In this paper a psychophysical experiment and a multidimensional scaling (MDS) analysis are undergone to determine the physical characteristics that physicians employ to diagnose a burn depth. Subsequently, these characteristics are translated into mathematical features, correlated with these physical characteristics analysis. Finally, a study to verify the ability of these mathematical features to classify burns is performed. In this study, a space with axes correlated with the MDS axes has been developed. 74 images have been represented in this space and a k-nearest neighbor classifier has been used to classify these 74 images. A success rate of 66.2% was obtained when classifying burns into three burn depths and a success rate of 83.8% was obtained when burns were classified as those which needed grafts and those which did not. Additional studies have been performed comparing our system with a principal component analysis and a support vector machine classifier. Results validate the ability of the mathematical features extracted from the psychophysical experiment to classify burns into their depths. In addition, the method has been compared with another state-of-the-art method and the same database.

Automatic cup-to-disc ratio estimation using active contours and color clustering in fundus images for glaucoma diagnosis

In this paper we propose a new automatic technique for the segmentation of the Optic Disc (OD) and optic nerve head (cup) regions in retinographies for glaucoma diagnosis. It provides an estimation of the Cup-to-Disc Ratio, the main clinical indicator of the disease. OD is detected combining intensity-based, multi-tolerance and morphological methods along with the active contour technique. Cup region is obtained with a new human perception adapted version of the well-known K-means algorithm in the uniform CIE L*a*b* color space with CIE94 color difference. For comparisons, the accurate cup border obtained is rounded and soften with two different techniques: ellipse fitting and mathematical morphology along with Gaussian Smoothing. The proposed method with both rounding steps has been tested in a database of 55 images and compared with the ground truth provided by an expert ophthalmologist. Both, OD and cup region, were satisfactory localized, achieving a mean error of 0.14 for ellipse fitting and 0.13 for morphology. The algorithm proposed seems to be a robust and reliable tool worthy to be included in any CAD system for glaucoma screening programs.

Principales Problemas de los Profesores Principiantes en la Enseñanza Universitaria

Some reflections on the main problems that novice university instructors face in higher education are presented and discussed. Such difficulties are classified and analyzed in three aspects: that of teaching, that of interpersonal relationships and that of management or institutional context. The importance of an adequate pedagogical training of the novice teacher and the role of the tutorial action are emphasized. The challenges that the novice instructor must face in the present reform of the Spanish university model according to the European Space for Higher Education and the research-teaching conflict are reviewed. This because research activity is not only indispensable for the constant scientific evolution of the university professor, but it is also an aspect that may guarantee continuity of the professor in the university career. Such activity is usually difficult to combine with the purely educational, especially for novice instructors.

Automatic detection of optic disc from retinal fundus images using dynamic programming

Automatic detection of optic disc (OD) in fundus images is used to determine potential clinical parameters for diagnosis of retinopathic diseases. Due to the presence of vascular-tree blood vessels, the detection of OD area is a complicated task for a computer-aided diagnosis (CAD) system, desirable by ophthalmologists. In this paper, a novel system for the detection of OD area has been developed. It consists of four major steps: preprocessing with a color space transformation and contrast normalization; segmentation of the vascular-tree through radial projection (RP) and weighted-derivative of Gaussian (WDOG) techniques; feature preserving removal of the detected vessels by a fast marching inpainting algorithm, detection of candidate to OD pixels via dynamic programming and OD area location using ellipse fitting methods. The proposed technique has been tested on 129 retinal images from to public and widely used datasets, DRIVE and DIARETB1. Experiments on this dataset indicate that this algorithm is computationally fast and able to achieve 92.5 % of accuracy for OD detection.

Automatic recognition of severity level for diagnosis of diabetic retinopathy using deep visual features

Diabetic retinopathy (DR) is leading cause of blindness among diabetic patients. Recognition of severity level is required by ophthalmologists to early detect and diagnose the DR. However, it is a challenging task for both medical experts and computer-aided diagnosis systems due to requiring extensive domain expert knowledge. In this article, a novel automatic recognition system for the five severity level of diabetic retinopathy (SLDR) is developed without performing any pre- and post-processing steps on retinal fundus images through learning of deep visual features (DVFs). These DVF features are extracted from each image by using color dense in scale-invariant and gradient location-orientation histogram techniques. To learn these DVF features, a semi-supervised multilayer deep-learning algorithm is utilized along with a new compressed layer and fine-tuning steps. This SLDR system was evaluated and compared with state-of-the-art techniques using the measures of sensitivity (SE), specificity (SP) and area under the receiving operating curves (AUC). On 750 fundus images (150 per category), the SE of 92.18%, SP of 94.50% and AUC of 0.924 values were obtained on average. These results demonstrate that the SLDR system is appropriate for early detection of DR and provide an effective treatment for prediction type of diabetes.

Perceptually adapted method for optic disc detection on retinal fundus images

This paper presents a novel technique for the detection of the optic disc (OD) in retinal fundus images. The method exploits the color information of the image with a perception adapted approach. CIE L*a*b* color space along with CIE94 color distance are used to obtain 12 color derivatives for each pixel under study. Based on this information, a classifier assigns a probability value to each pixel in the image, meaning its suitability for being part of the OD border. Looking for the pixels with highest probability values, the method detects the basic points for the OD border that are subsequently connected with the livewire technique. The reliability of the tool has been tested with three different classifiers on 198 images from four public available databases obtaining an average success percentage of 85.48% and a mean distance to the closest point of 2 pixels.