Silvia Alayon

A fuzzy system for helping medical diagnosis of malformations of cortical development

Malformations of the cerebral cortex are recognized as a common cause of developmental delay, neurological deficits, mental retardation and epilepsy. Currently, the diagnosis of cerebral cortical malformations is based on a subjective interpretation of neuroimaging characteristics of the cerebral gray matter and underlying white matter. There is no automated system for aiding the observer in making the diagnosis of a cortical malformation. In this paper a fuzzy rule-based system is proposed as a solution for this problem. The system collects the available expert knowledge about cortical malformations and assists the medical observer in arriving at a correct diagnosis. Moreover, the system allows the study of the influence of the various factors that take part in the decision. The evaluation of the system has been carried out by comparing the automated diagnostic algorithm with known case examples of various malformations due to abnormal cortical organization. An exhaustive evaluation of the system by comparison with published cases and a ROC analysis is presented in the paper.

RIM-ONE: An open retinal image database for optic nerve evaluation

Automated diagnosis of glaucoma disease has been studied for years. A great amount of research work in this field has been focused on the analysis of retinal fundus images to localize, detect and evaluate the optic disc. An open fundus image database with accurate gold standards of the optic nerve head has been implemented. A variability measurement by zones of the optic disc is also proposed. The relevance of this work is to provide accurate ONH segmentations and a segmentation assessment procedure to allow the design of computerized methods for glaucoma detection.

Measuring hemoglobin levels in the optic nerve head: comparisons with other structural and functional parameters of glaucoma.

PURPOSE We evaluated and compared the ability of a new method for measuring hemoglobin (Hb) levels at the optic nerve head (ONH) to that of visual field evaluation, scanning laser ophthalmoscopy (HRT), scanning laser polarimetry (GDx), and optical coherence tomography (OCT) for diagnosing glaucoma. METHODS Healthy eyes (n = 102) and glaucomatous eyes (n = 101) underwent reliable Oculus Spark perimetry, and imaging with the HRT, GDx, and Cirrus OCT. In addition, ONH color images were acquired with a non-mydriatic fundus camera. The Laguna ON(h)E program then was used to calculate the Hb amount in each of 24 sectors of the ONH. Sensitivities at 95% fixed specificity, diagnostic agreement, and linear correlations between parameters with the best diagnostic ability were calculated. RESULTS The glaucoma discriminant function (GDF) of the Laguna program, evaluating Hb in the vertical intermediate sectors and center/periphery Hb amount slope, yielded an 89.1% sensitivity and 95.1% specificity, which was superior or similar to the other tests. The best GDF diagnostic agreement was for the OCT-vertical cup-to-disc (C/D) ratio (kappa = 0.772) and the final phase Spark pattern SD (kappa = 0.672). Hb levels correlated strongly with the Spark mean sensitivity (first phase 0.70, final phase 0.71). Hb also correlated well with the Reinhard OW Burk discriminant function of the HRT (0.56), nerve fiber indicator of GDx (-0.64), and vertical C/D ratio of OCT (0.71). CONCLUSIONS Hb levels evaluated by color analysis of ONH photographs had high reproducibility, a high sensitivity-specificity balance, and moderate to strong agreement with other structural and functional tests.

Cytological image analysis with a genetic fuzzy finite state machine

The objective of this research is to design a pattern recognition system based on a Fuzzy Finite State Machine (FFSM). We try to find an optimal FFSM with Genetic Algorithms (GA). In order to validate this system, the classifier has been applied to a real problem: distinction between normal and abnormal cells in cytological breast fine needle aspirate images and cytological peritoneal fluid images. The characteristic used in the discrimination between normal and abnormal cells is a texture measurement of the chromatin distribution in cellular nuclei. Furthermore, the effectiveness of this method as a pattern classifier is compared with other existing supervised and unsupervised methods and evaluated with Receiver Operating Curves (ROC) methodology.

Evolution in Medical Decision Making

The classical approach to medical decision making can be limited by the underlying theories. The evolutionary computation is a different concept, which can find many different solutions of the problem. In medicine, this is useful because of different expectations the decision system must face. We implemented a tool for genetic induction of vector decision trees, which are a good choice for a medical decision model because of their simplicity and transparency. The vector decision tree gives multiple classifications in one single pass. Evolutionary development of such trees achieved good results when the results were statistically compared to those of other classical methods. For medical interpretation however a cooperation with doctors is needed to verify the model build.

Cytological breast fine needle aspirate images analysis with a genetic fuzzy finite state machine

A system based on a fuzzy finite state machine (FFSM) has been developed for evaluating cytological features derived directly from a digital scan of breast fine needle aspirate (FNA) slides. The system uses computer vision techniques to analyse cell nuclei in order to extract determinate features and to try to find, by means of genetic algorithms (GA), the ideal FFSM that is able to classify them. This application to breast cancer diagnosis uses the characteristics of individual cells to discriminate benign from malignant breast lumps. In our system, we try to find a texture measurement that can be included in the feature set in order to improve the classifier performance: a complexity measurement of the structural pattern is used to discriminate between benign and malign cells. With this measure and the technique described, we have observed that not only is the absolute complexity of the image relevant, but also the way in which the complexity is distributed at different scales.

Estimation of the Relative Amount of Hemoglobin in the Cup and Neuroretinal Rim Using Stereoscopic Color Fundus Images

PURPOSE To calculate the relative amount of hemoglobin (Hb) in sectors of the optic nerve head (ONH) from stereoscopic color fundus images using the Laguna ONhE method and compare the results with the visual field evaluation and optical coherence tomography (OCT). METHODS Healthy eyes (n = 87) and glaucoma eyes (n = 71) underwent reliable Oculus Spark perimetry and Cirrus OCT. Optical nerve head color images were acquired with a nonmydriatic stereoscopic Wx Kowa fundus camera. Laguna ONhE program was applied to these images to calculate the relative Hb amount in the cup and six sectors of the rim. Receiver operating characteristic (ROC) analysis and correlations between parameters were calculated. RESULTS We did not observe any variations in the relative amount of Hb in relation to age in healthy subjects (R(2) = 0.033, P > 0.05). Maximum ROC area confidence intervals were observed for a combination between perimetric indices and the Laguna ONhE Glaucoma discriminant function (0.970-0.899) followed by rim area (0.960-0.883), and mean deviation (MD; 0.944-0.857). In glaucoma cases, relative Hb amount presented significant reduction in all rim sectors, especially 231° to 270° and 81° to 120° (P < 0.001), except in the temporal 311° to 40° (P = 0.11). Perimetry mean sensitivity by sectors was better correlated with respective Hb levels than with rim areas or the corresponding nerve fiber thickness, especially the superior and inferior sectors (P < 0.05). CONCLUSIONS Visual field sensitivity was better correlated with Hb levels than with rim sector areas or the corresponding nerve fiber thickness. In many cases the remaining rim show low perfusion, especially in the superior and inferior sectors.

Symbol-Based Machine Learning Approach for Supervised Segmentation of Follicular Lymphoma Images

Lymphoma is a broad term encompassing a variety of cancers of the lymphatic system. Lymphoma is differentiated by the type of cell that multiplies and how the cancer presents itself. It is very important to get an exact diagnosis regarding lymphoma and to determine the treatments that will be most effective for the patients condition. Our work was focused on the identification of lymphomas by finding follicles in microscopy images provided by the Laboratory of Pathology in the University Hospital of Tenerife, Spain. Roughly we can divide our work in two stages: in the first stage we did image pre-processing and feature extraction, and in the second stage we used different rough set approaches for pixel classification. These results were compared to decision tree results we obtained earlier. Symbolic machine learning approaches are often neglected when looking for image analysis tools. The results we got are very promising and show that symbolic approaches can be successful in image analysis applications.

Using social networks at university: The case of school of computer science

At present, not all of the universities understands the usefulness of social networks for teaching and working insertion. However, both the teachers and the students make use of them. In USA, 100% of the universities make use of social networks in some way. The most widely used social network among American students is Facebook (98%), followed by Twitter (84%) and Linkedin (47%). In this paper, different uses of social networks in education and working insertion at School of Computer Science of University of La Laguna will be described (educational use, work insertion and social media).

Variabilidad entre expertos al delimitar el borde y área papilar

OBJECTIVE Estimation of the error rate in the subjective determination of the optic nerve head edge and area. METHOD 1) 169 images of optic nerve disc were evaluated by five experts for the defining of the edges in 8 positions (every 45°). 2) The estimated areas of 26 cases were compared with the measurements of the Cirrus Optical Coherence Tomography (OCT-Cirrus). RESULTS 1) The mean variation of the estimated radius was ±5.2%, with no significant differences between sectors. Specific differences were found between the 5 experts (P <.001), each one compared with the others. 2) The disc area measured by the OCT-Cirros was 1.78 mm² (SD =0.27). The results corresponding to the experts who detected smaller areas were better correlated to the area detected by the OCT-Cirrus (r=0.77-0.88) than the results corresponding to larger areas (r =0.61-0.69) (P <.05 in extreme cases). CONCLUSIONS There are specific patterns in each expert for defining the disc edges and involve 20% variation in the estimation of the optic nerve area. The experts who detected smaller areas have a higher agreement with the objective method used. A web tool is proposed for self-assessment and training in this task.