Juan Ignacio Pastore

Segmentation of brain magnetic resonance images through morphological operators and geodesic distance

When segmenting magnetic resonance (MR) images, a wide range of useless information arises, which has to be discarded as a step prior to classifying the different cerebral cortex areas. To obtain effective results during the classification process, it is necessary to work with images solely containing the brain and eliminate the cranium and surrounding meninges. This work introduces an automatic method for the detection of said structures based on the application of morphology alternating sequential filters by reconstruction with structuring elements of growing size. Apart from enhancing and filtering in this way, this method captures the interior of a closed simple curve employing geodesic distance. Said curve represents the external brain boundary.

ARITHMETIC MEAN BASED COMPENSATORY FUZZY LOGIC

Fuzzy Logic is a multi-valued logic model based on fuzzy set theory, which may be considered as an extension of Boolean Logic. One of the fields of this theory is the Compensatory Fuzzy Logic, based on the removal of some axioms in order to achieve a sensitive and idempotent multi-valued system. This system is based on a quadruple of continuous operators: conjunction, disjunction, order and negation. In this work we present a new model of Compensatory Fuzzy Logic based on a different set of operators, conjunction and disjunction, than the ones used in the original definition, and then prove that this new model satisfies the required axioms. As an example, we present an application to decision-making, comparing the results against the ones based on the original model.

Medical Image Segmentation using the HSI color space and Fuzzy Mathematical Morphology

Diabetic retinopathy is the most common cause of blindness among the active population in developed countries. An early ophthalmologic examination followed by proper treatment can prevent blindness. The purpose of this work is develop an automated method for segmentation the vasculature in retinal images in order to assist the expert in the evolution of a specific treatment or in the diagnosis of a potential pathology. Since the HSI space has the ability to separate the intensity of the intrinsic color information, its use is recommended for the digital processing images when they are affected by lighting changes, characteristic of the images under study. By the application of color filters, is achieved artificially change the tone of blood vessels, to better distinguish them from the bottom. This technique, combined with the application of fuzzy mathematical morphology tools as the Top-Hat transformation, creates images of the retina, where vascular branches are markedly enhanced over the original. These images provide the visualization of blood vessels by the specialist.

Fuzzy mathematical morphology for color images defined by fuzzy preference relations

Nowadays, the representation and the treatment of color images are still open problems. Mathematical morphology is the natural area for a rigorous formulation of many problems in image analysis. Moreover, it comprises powerful non-linear techniques for filtering, texture analysis, shape analysis, edge detection or segmentation. A large number of morphological operators have been widely defined and tested to process binary and gray scale images. However, the extension of mathematical morphology operators to multi-valued functions, and in particular to color images, is neither direct nor general due to the vectorial nature of the data. In this paper, basic morphological operators, erosion and dilation, are extended to color images from a new vector ordering scheme based on a fuzzy order in the RGB color space. Experimental results show that the proposed color operators can be efficiently used for color image processing. HighlightsFuzzy mathematical morphology for color images.Erosion and dilation operators avoid false colors.Proposed order considers all the components with the same weight.

A survey of medical images and signal processing problems solved successfully by the application of Type-2 Fuzzy Logic

Typical problems concerning to Digital Image Processing (DIP) and to Digital Signals Processing require specific models for each particular problem and the characteristics of the data involved. However, usually these data show a high degree of uncertainty due to the acquisition system itself, noise or uncertainties related to the nature of the problem. They often require considering different points of view of experts in a single model to determine a set of rules or predicates that would achieve the desired solution. Type-2 fuzzy sets can adequately model such uncertainties. This paper presents a study on different applications of type-2 fuzzy sets in image and signal processing, analyzing the main advantages of this type of fuzzy sets in modeling uncertainties. We also review the definitions of type-2 fuzzy sets, their main properties and operations between them.

Enhancement of medical images in HSI color space

In this paper we use the HSI color space as an alternative to RGB space. The HSI space considers the image as a combination of the components: hue, saturation and intensity. In this paper we propose to design a chromatic filter in order to obtain improvements in the enhancement of medical images. In image processing systems it is usual specify colors in a form compatible with the hardware used. The RGB color model, where is computationally convenient, is not very useful in the specification and color recognition. The human being does not recognize a color by having an amount of red, green or blue components, but uses attributes perceptual of hue, saturation and intensity. The chromatics models HSI, HLS, HSV, and its variants, encodes the color with the above attributes and are defined as intuitive spaces.

Multiscale Morphological Operators and Geodesic Distance applied to Computed Axial Tomography Segmentation

Lately, several diagnostic and therapeutic procedures for mediastinal tumors and masses are done by videothoraxcoscopic techniques. Consequently, Computed Axial Tomography (C.A.T.) become a fundamental tool in tumors and masses diagnostic, especially to locate and measure them with accuracy and precision. This paper presents a segmentation method in C.A.T. images through techniques based in Mathematical Morphology. A semi-automatic method for structure segmentation based on Multiscale Morphological Operators is introduced. In addition to filtering and enhancing, this method capture the interior of a closed simple curve by the geodesic distance. These curves represents the external boundary of the different mediastinal structures. This method allows to calculate exactly the external boundary of the segmented structure and also the delimitated area.

Type-2 Fuzzy Logic in Decision Support Systems

Decision Support Systems have been widely used in expert knowledge modeling. One of the known implementation approaches is through definition of Fuzzy Sets and Fuzzy Predicates, whose evaluation determines the system’s output. Despite Type-1 Fuzzy Sets have been widely used in this type of implementation, there are uncertainty sources that cannot be adequately modeled when using expert knowledge minimizing their effect on system’s output, especially when it comes from several experts opinions. Type-2 Fuzzy Sets deal with fuzzy membership degrees, which can represent adequately the typical uncertainties of these systems. In this chapter, we generalize the operators of Fuzzy Logic in order to evaluate Fuzzy Predicates with Type-2 Fuzzy Sets and we define measures to assess the degree of truth of these predicates to define the theoretical background of the Decision Support Systems using this methodology. We present an example application of decision-making and a brief discussion of the results.

Fuzzy Mathematical Morphology Toolbox and Graphical Interface

The task of solving problems using Digital Image Processing requires of the choice of the correct methodology for the issues to be tackled, and a sound selection of parameters for each algorithm to be used. Fuzzy Mathematical Morphology is one of the existing techniques for image processing, being already applied with success on several problems of medical images analysis. However, the selection and use of its operators requires of the continual observation of the results, over a varied range of parameters, for the selection of the optimal ones. Toward that goal, in this work we develop a library of Fuzzy Mathematical Morphology operators, plus a flexible graphical interface, which is of great help in the selection of the optimal parameters and the implementation of Fuzzy operators, with the addition of several classical Image Processing operators. We describe the properties of the library and analyze the simplicity of the graphical interface.

Interpretable interval type-2 fuzzy predicates for data clustering: A new automatic generation method based on self-organizing maps

A new clustering based on interval type-2 fuzzy predicates and SOMs is proposed.SOMs are automatically configured and trained.Fuzzy predicates are generated using cluster prototypes extracted from SOMs.Linguistic knowledge is obtained from the predicates automatically generated.The proposed method overcome existing clustering methods based on fuzzy predicates. In previous works, we proposed two methods for data clustering based on automatically discovered fuzzy predicates which were referred to as SOM-based Fuzzy Predicate Clustering (SFPC) [Meschino et al., Neurocomputing, 147, 4759 (2015)] and Type-2 Data-based Fuzzy Predicate Clustering (T2-DFPC) [Comas et al., Expert Syst. Appl., 68, 136150 (2017)]. In such methods, fuzzy predicates allow both data clustering and knowledge discovering about the obtained clusters. This last feature constitutes novelty comparing to other existing approaches and it is a major contribution in the data clustering field. Based on these previous methods, in the present paper a new automatic clustering method based on fuzzy predicates is proposed which uses Self-Organizing Maps (SOMs) and is called Type-2 SOM-based Fuzzy Predicate Clustering (T2-SFPC). The new method does not require any prior knowledge about the clustering addressed. First, a random partition is defined on the dataset to be clustered and SOMs are configured and trained using the resulting data subsets. Second, an automatic clustering approach is applied on the SOM codebooks, discovering representative data of the different clusters, which are called cluster prototypes. Third, interval type-2 membership function formed by Gaussian-shape sub-functions and fuzzy predicates are defined, allowing data clustering and its interpretation. The proposed method preserves all the advantages of the previous methods SFPC and T2-DFPC in relation to the knowledge extraction capabilities and their potential application on distributed clustering and parallel computing, but results obtained on several public datasets tested showed more compactness and separation of the clusters defined by the T2-SFPC, outperforming both the previous methods and the several classical clustering approaches tested, considering internal and external validation indices. Additionally, both clustering interpretation and optimization capabilities are improved by the proposed method when compared to the methods SFPC and T2-DFPC.