Application of a Mamdani-type fuzzy rule-based system to segment periventricular cerebral veins in susceptibility-weighted images

This paper presents an algorithm designed to segment veins in the periventricular region of the brain in susceptibility-weighted magnetic resonance images. The proposed algorithm is based on a Mamdani-type fuzzy rule-based system that enables enhancement of veins within periventricular regions of interest as the first step. Segmentation is achieved after determining the cut-off value providing the best trade-off between sensitivity and specificity to establish the suitability of each pixel to belong to a cerebral vein. Performance of the algorithm in susceptibility-weighted images acquired in healthy volunteers showed very good segmentation, with a small number of false positives. The results were not affected by small changes in the size and location of the regions of interest. The algorithm also enabled detection of differences in the visibility of periventricular veins between healthy subjects and multiple sclerosis patients. © Springer International Publishing Switzerland 2016.Postprint (author's final draft

V-ANFIS for Dealing with Visual Uncertainty for Force Estimation in Robotic Surgery

Accurate and robust estimation of applied forces in Robotic-Assisted Minimally Invasive Surgery is a very challenging task. Many vision-based solutions attempt to estimate the force by measuring the surface deformation after contacting the surgical tool. However, visual uncertainty, due to tool occlusion, is a major concern and can highly affect the results' precision. In this paper, a novel design of an adaptive neuro-fuzzy inference strategy with a voting step (V-ANFIS) is used to accommodate with this loss of information. Experimental results show a significant accuracy improvement from 50% to 77% with respect to other proposals.Peer ReviewedPostprint (published version

A Fuzzy Regional-Based Approach for Detecting Cerebrospinal Fluid Regions in presence of Multiple Sclerosis Lesions

Postprint (published version

Estudi de la transformació de l'espai de color RGB a l'espai de color HSV

S’apliquen les tècniques clàssiques de propagació de l’error a la transformació de l’espai de color RGB en l’espai de color HSV a un conjunt de 1098 imatges test. El conjunt d’imatges test són 183 paletes de color i sis nivells d’il·luminació diferents. Els resultats que es presenten indiquen com varien la mitjana i la variància per la transformació.Preprin

Automated detection of parenchymal changes of ischemic stroke in non-contrast computer tomography: a fuzzy approach

The detection of ischemic changes is a primary task in the interpretation of brain Computer Tomography (CT) of patients suffering from neurological disorders. Although CT can easily show these lesions, their interpretation may be difficult when the lesion is not easily recognizable. The gold standard for the detection of acute stroke is highly variable and depends on the experience of physicians. This research proposes a new method of automatic detection of parenchymal changes of ischemic stroke in Non-Contrast CT. The method identifies non-pathological cases (94 cases, 40 training, 54 test) based on the analysis of cerebral symmetry. Parenchymal changes in cases with abnormalities (20 cases) are detected by means of a contralateral analysis of brain regions. In order to facilitate the evaluation of abnormal regions, non-pathological tissues in Hounsfield Units were characterized using fuzzy logic techniques. Cases of non-pathological and stroke patients were used to discard/confirm abnormality with a sensitivity (TPR) of 91% and specificity (SPC) of 100%. Abnormal regions were evaluated and the presence of parenchymal changes was detected with a TPR of 96% and SPC of 100%. The presence of parenchymal changes of ischemic stroke was detected by the identification of tissues using fuzzy logic techniques. Because of abnormal regions are identified, the expert can prioritize the examination to a previously delimited region, decreasing the diagnostic time. The identification of tissues allows a better visualization of the region to be evaluated, helping to discard or confirm a stroke.Peer ReviewedPostprint (author's final draft

On real time image processing on a network of PCs

Many image processing algorithms have a very high execution time if only a processor is used for processing them. Using a SIMD parallel structure for its execution could reduce this time. This is particularly important in the case of algorithms that must be processed in real time. The use of networks of PC is an appealing solution that besides its low cost, takes advantage from both the high speed of actual interconnection networks, and the high-performance of PC. In this paper we present a model that explicitly considers system parameters, network parameters, and application parameters. So, the speed and communication model of the considered network, the workstations and PC computing power, the per-pixel computational cost of the algorithms (that can be constant or variable), and a variable number of computers have been considered. We do not aim to evaluate the processing of high and medium-level algorithms of a MISD structure, but we present the first results of our evaluations for iterative low-level image processing applications. Specifically, we give a prediction model to distribute the data to each processor of a distributed system, minimizing the processors' idle time.Peer ReviewedPostprint (published version

On real time image processing on a network of PCs

Many image processing algorithms have a very high execution time if only a processor is used for processing them. Using a SIMD parallel structure for its execution could reduce this time. This is particularly important in the case of algorithms that must be processed in real time. The use of networks of PC is an appealing solution that besides its low cost, takes advantage from both the high speed of actual interconnection networks, and the high-performance of PC. In this paper we present a model that explicitly considers system parameters, network parameters, and application parameters. So, the speed and communication model of the considered network, the workstations and PC computing power, the per-pixel computational cost of the algorithms (that can be constant or variable), and a variable number of computers have been considered. We do not aim to evaluate the processing of high and medium-level algorithms of a MISD structure, but we present the first results of our evaluations for iterative low-level image processing applications. Specifically, we give a prediction model to distribute the data to each processor of a distributed system, minimizing the processors' idle time.Peer Reviewe

Fuzzy sets in computer vision: an overview

Every computer vision level crawl with uncertainty, what makes its management a significant problem to be considered and solved when trying for automated systems for scene analysis and interpretation. This is why fuzzy set theory and fuzzy logic is making many inroads into the handling of uncertainty in various aspects of image processing and computer vision. The growth within the use of fuzzy set theory in computer vision is keeping pace with the use of more complex algorithms addressed to solve problems arisen from image vagueness management. Due to the natural linguistic capabilities of high - level computer vision, it is a very appropriate place for applying fuzzy sets. Moreover, scene description, i.e., the language -based representation of regions and their relationships, for either humans or higher automated reasoning provides an excellent opportunity. With this overview we want to address the various aspects of image processing and analysis problems where the theory of fuzzy sets has so far been applied. On the other hand, we will discuss the possibility of making fusion of the merits of fuzzy set theory, neural networks theory and genetic algorithms for improved performance. Finally a list of representative references is also provided

Application of fuzzy techniques to the design of algorithms in computer vision

In this paper a method for the design of algorithms is presented which use fuzzy techniques in order to achieve a better vagueness treatment. A base of rules will be developed in order to design the algorithms. Data fuzzification problem is solved by using probability density functions and probability distribution functions, whereas data analysis is set out associating, to each one of the "analysis rules", a fuzzy set which will be obtained by applying an aggregation function which will be defined by using an OWA operator. The proposed design provides a solution to the data value fuzzification problem, which is a quite well solved problem for applied control algorithms, but, up to now, displayed great difficulties for vision ones. Moreover, the proposed data analysis method provides a solution for non intrinsic problems from vision algorithms