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

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

Sensorless force estimation using a neuro-vision-based approach for robotic-assisted surgery

This paper addresses the issue of lack of force feedback in robotic-assisted minimally invasive surgeries. Force is an important measure for surgeons in order to prevent intra-operative complications and tissue damage. Thus, an innovative neuro-vision based force estimation approach is proposed. Tissue surface displacement is first measured via minimization of an energy functional. A neuro approach is then used to establish a geometric-visual relation and estimate the applied force. The proposed approach eliminates the need of add-on sensors, carrying out biocompatibility studies and is applicable to tissues of any shape. Moreover, we provided an improvement from 15.14% to 56.16% over other approaches which demonstrate the potential of our proposalPeer ReviewedPostprint (author's final draft

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

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

State of the art survey on MRI brain tumor segmentation

Brain tumor segmentation consists of separating the different tumor tissues (solid or active tumor, edema,and necrosis) from normal brain tissues: gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). In brain tumor studies, the existence of abnormal tissues may be easily detectable most of the time. However, accurate and reproducible segmentation and characterization of abnormalities are not straightforward. In the past, many researchers in the field of medical imaging and soft computing have made significant survey in the field of brain tumor segmentation. Both semiautomatic and fully automatic methods have been proposed. Clinical acceptance of segmentation techniques has depended on the simplicity of the segmentation, and the degree of user supervision. Interactive or semiautomatic methods are likely to remain dominant in practice for some time, especially in these applications where erroneous interpretations are unacceptable. This article presents an overview of the most relevant brain tumor segmentation methods, conducted after the acquisition of the image. Given the advantages of magnetic resonance imaging over other diagnostic imaging, this survey is focused on MRI brain tumor segmentation. Semiautomatic and fully automatic techniques are emphasized.Postprint (published version