Proteomics and the lung: Analysis of bronchoalveolar lavage fluid

Our knowledge of the complex bronchoalveolar lavage fluid (BALF) proteome has increased significantly over the last decade; but still, there remain many aspects of the BALF proteome that need characterization. Current proteomic methodologies resolve proteins within limited dynamic ranges: thereby, being limited in their ability to examine important areas of the BALF proteome, such as low molecular weight, low abundance proteins. To ensure proper coverage of these proteins in the BALF proteome, a refined 2-DE standard operation protocol is presented, highlighting important issues in sample collection, sample preparation, and 2-D DIGE analysis. It is hoped that this will help advance the field of BALF proteomics, BALFomics, which has lagged behind similar biofluids such as plasma and serum.     

Learning fuzzy partitions in FIR methodology

The main goal of this research is the development of a hybrid genetic fuzzy system (GFS), composed by the fuzzy inductive reasoning (FIR) methodology and a genetic algorithm (GA) that is responsible of learning the fuzzy partitions needed in the recode process of FIR. A partition includes the number of fuzzy sets (classes) per variable and the membership function of each class. The resulting GFS is applied to two real problems, i.e. the estimation of the maintenance cost of medium voltage lines in Spanish towns and the prediction of ozone levels in Austria. The results obtained in each application are compared with some of the most popular classical statistical modeling methods, neural networks and other hybrid evolutionary data analysis techniques.     

Compression and decompression of images with discrete fuzzy transforms

In foregoing papers we have used the compression/decompression method of images based on the concept of discrete fuzzy transform (and its inverse) of a function f defined on a real interval with respect to the fuzzy sets A₁,…,A_n forming a fuzzy partition of such interval. Here we make a detailed experimental comparison with the similar method based on the fuzzy transforms F^↑ and F^↓ of f defined via a continuous triangular norm and its corresponding residuum, respectively. We consider some images of sizes 256 × 256 (pixels) extracted from the well-known database Corel Galery (Arizona Directory). By using the same compression rate in both methods, we have that the PSNR (Peak Signal to Noise Ratio) obtained with the discrete fuzzy transform (and its inverse) of f is more higher than the PSNR determined with the operators F^↑ and F^↓ defined via the usual Lukasiewicz, product and minimum triangular norms. Moreover, we compare our results with the classical JPEG method for values of compression rate approximately equal to those used in the previous methods.     

Segmentation of remotely sensed images with fuzzy thresholding,and quantitative evaluation

Effectiveness of various fuzzy thresholding techniques (based on entropy of fuzzy sets, fuzzy geometrical properties, and fuzzy correlation) is demonstrated on remotely sensed (IRS and SPOT) images. A new quantitative index for image segmentation using the concept of homogeneity within regions is defined. Results are compared with those of probabilistic thresholding, and fuzzy c-means and hard c-means clustering algorithms, both in terms of index value (quantitatively) and structural details (qualitatively). Fuzzy set theoretic algorithms are seen to be superior to their respective non-fuzzy counterparts. Among all the techniques, fuzzy correlation, followed by fuzzy entropy, performed better for extracting the structures. Fuzzy geometry based thresholding algorithms produced a single stable threshold for a wide range of membership variation.     

Segmentation of lip region in color images by fuzzy clustering

Extraction of the lip region is essential to lip reading, which is a field of image processing that is used to obtain meaningful information by the analysis of lip movement from human face images. Many conventional methods for extraction of the lip region have been proposed. One is to identify the lip position by using geometric face structure. Another discriminates lip and skin regions by using color information only. The former is more complex than the latter; however, it can analyze black and white images as well as color images. The latter is simpler than the former; however, it is difficult to discriminate lip and skin regions because of the high similarity between these two regions, and it is less accurate than the former. Conventional methods usually analyze color coordinate systems to extract lip regions rather than analyzing the coordinate system itself. In this paper, the best color coordinate system for lip extraction was selected by the analysis of discriminability. Segmentation of the lip region with this coordinate system and a new feature vector are proposed.     

A new methodology for Decisions in Medical Informatics using fuzzy cognitive maps based on fuzzy rule-extraction techniques

In this research work, a novel framework for the construction of augmented Fuzzy Cognitive Maps based on Fuzzy Rule-Extraction methods for decisions in medical informatics is investigated. Specifically, the issue of designing augmented Fuzzy Cognitive Maps combining knowledge from experts and knowledge from data in the form of fuzzy rules generated from rule-based knowledge discovery methods is explored. Fuzzy cognitive maps are knowledge-based techniques which combine elements of fuzzy logic and neural networks and work as artificial cognitive networks. The knowledge extraction methods used in this study extract the available knowledge from data in the form of fuzzy rules and insert them into the FCM, contributing to the development of a dynamic decision support system. The fuzzy rules, which derived by these extraction algorithms (such as fuzzy decision trees, association rule-based methods and neuro-fuzzy methods) are implemented to restructure the FCM model, producing new weights into the FCM model, that initially structured by experts. Concluding, our scope is to present a new methodology through a framework for decision making tasks using the soft computing technique of FCMs based on knowledge extraction methods. A well known medical decision making problem pertaining to the problem of radiotherapy treatment planning selection is presented to illustrate the application of the proposed framework and its functioning.     

Interactive segmentation of the cerebral lobes with fuzzy inference in 3T MR images.

Measurement of volume and surface area of the frontal, parietal, temporal and occipital lobes from magnetic resonance (MR) images shows promise as a method for use in diagnosis of dementia. This article presents a novel computer-aided system for automatically segmenting the cerebral lobes from 3T human brain MR images. Until now, the anatomical definition of cerebral lobes on the cerebral cortex is somewhat vague for use in automatic delineation of boundary lines, and there is no definition of cerebral lobes in the interior of the cerebrum. Therefore, we have developed a new method for defining cerebral lobes on the cerebral cortex and in the interior of the cerebrum. The proposed method determines the boundaries between the lobes by deforming initial surfaces. The initial surfaces are automatically determined based on user-given landmarks. They are smoothed and deformed so that the deforming boundaries run along the hourglass portion of the three-dimensional shape of the cerebrum with fuzzy rule-based active contour and surface models. The cerebrum is divided into the cerebral lobes according to the boundaries determined using this method. The reproducibility of our system with a given subject was assessed by examining the variability of volume and surface area in three healthy subjects, with measurements performed by three beginners and one expert user. The experimental results show that our system segments the cerebral lobes with high reproducibility.     

A nonlinear-programming methodology for multi-attribute decision-making problem with interval-valued intuitionistic fuzzy soft sets information

Interval-valued intuitionistic fuzzy (IVIF) soft set is one of the useful extensions of the fuzzy soft set which efficiently deals with the uncertain data for the decision-making processes. In this paper, an attempt has been made to present a nonlinear-programming (NP) model based on the technique for order preference by similarity to ideal solution (TOPSIS), to solve multi-attribute decision-making problems. In this approach, both ratings of alternatives on attributes and weights of attributes are represented by IVIF sets. Based on the available information, NP models are constructed on the basis of the concepts of the relative-closeness coefficient and the weighted distance. Some NP models are further deduced to calculate relative-closeness of sets of alternatives which can be used to generate the ranking order of the alternatives. A real example is taken to demonstrate the applicability and validity of the proposed methodology.     

Low light enhancement algorithm for color images using intuitionistic fuzzy sets with histogram equalization

Multimedia Tools and Applications - In this work, a new fuzzy logic-based algorithm is proposed for the enhancement of low light color images. A generalization of a fuzzy set known as an...     

Semi-supervised fuzzy neighborhood preserving analysis for feature extraction in hyperspectral remote sensing images

Neural Computing and Applications - Semi-supervised feature extraction methods are an important focus of interest in data mining and machine learning areas. These methods are improved methods based...     

Critical point detection in fluid flow images using dynamical system properties

This paper introduces an algorithm for critical point detection in textured fluid flow images. A new measure is defined, based on dynamical system properties, that identifies candidate critical points in an orientation field. The candidates are verified or rejected based on estimates of the local flow field properties. The algorithm can locate partially occluded and degraded flow structures, and applications of this algorithm to experimental flow imagery are included. The algorithm performance is quantified, and it is compared to other detectors.     

Noise reduction in synthetic aperture radar images using fuzzy logic and genetic algorithm

Synthetic aperture radar (SAR) is a self-illuminating imaging technique; it produces high resolution images in all weather conditions, day and night. SAR images are widely accepted and used by many application scientists. However, the SAR images are corrupted with speckle noise. Speckle noises are caused by random interference of electromagnetic signals scattered by the object surface within one resolution element. The amount of noise and distribution of noise corrupting the image is unpredictable. Conventional noise filters are quantitative in nature; they are not well suited for uncertainty problems. Fuzzy logic is capable of handling uncertainty. In this work, noisy pixels in the images are identified by using fuzzy rules and filtered using fuzzy weighted mean, keeping the healthy pixels unchanged. The optimum value of parameters used in defining fuzzy membership function is determined by using genetic algorithm (GA). Reducing noise and simultaneously preserving image details are the two most desirable characteristics of noise filters. Peak signal-to-noise ratio (PSNR) and edge preserving factor (EPF) are used to evaluate the performance of the proposed fuzzy filter. SAR images affected by varying amounts of speckle noise are used to evaluate the performance. It was observed that the proposed filter suppresses noise and preserves image edges.

     

A new fuzzy c-means method with total variation regularization for segmentation of images with noisy and incomplete data

The objective function of the original (fuzzy) c-mean method is modified by a regularizing functional in the form of total variation (TV) with regard to gradient sparsity, and a regularization parameter is used to balance clustering and smoothing. An alternating direction method of multipliers in conjunction with the fast discrete cosine transform is used to solve the TV-regularized optimization problem. The new algorithm is tested on both synthetic and real data, and is demonstrated to be effective and robust in treating images with noise and missing data (incomplete data).     

Text localization in camera captured images using fuzzy distance transform based adaptive stroke filter

Multimedia Tools and Applications - Localization of text from camera captured images with complex background is now-a-days a growing demand of modern IT enable service. Most of the current text...     

Fuzzy segmentation of endorrhachis in magnetic resonance images and its fuzzy maximum intensity projection

This paper describes a fuzzy segmentation approach and the rendering technique called fuzzy maximum intensity projection (FMIP) for the endorrhachis in magnetic resonance images. First, we propose a fuzzy segmentation procedure, which assigns the high fuzzy degree for the high possibility to the endorrhachis. Second, we describe FMIP, which projects higher fuzzy membership degrees to brighter values in the 2D plane for every voxel in the volume dataset. This enables us to visualize regions of interest with higher accuracy after the fuzzy segmentation is done in the dataset. The applicability of them is tested in the visualization of the endorrhachis in magnetic resonance images. A comparison between FMIP and MIP shows that FMIP visualizes it more effectively.     

Removal of noise patterns in handwritten images using expectation maximization and fuzzy inference systems

The removal of noise patterns in handwritten images requires careful processing. A noise pattern belongs to a class that we have either seen or not seen before. In the former case, the difficulty lies in the fact that some types of noise patterns look similar to certain characters or parts of characters. In the latter case, we do not know the class of noise in advance which excludes the possibility of using parametric learning methods. In order to address these difficulties, we formulate the noise removal and recognition as a single optimization problem, which can be solved by expectation maximization given that we have a recognition engine that is trained for clean images. We show that the processing time for a noisy input is higher than that of a clean input by a factor of two times the number of connected components of the input image in each iteration of the optimization process. Therefore, in order to speed up the convergence, we propose to use fuzzy inference systems in the initialization step of the optimization process. Fuzzy inference systems are based on linguistic rules that facilitate the definition of some common classes of noise patterns in handwritten images such as impulsive noise and background lines. We analyze the performance of our approach both in terms of recognition rate and speed. Our experimental results on a database of real-world handwritten images corroborate the effectiveness and feasibility of our approach in removing noise patterns and thus improving the recognition performance for noisy images.     

Using anatomical knowledge expressed as fuzzy constraints to segment the heart in CT images

Segmenting the heart in medical images is a challenging and important task for many applications. In particular, segmenting the heart in CT images is very useful for cardiology and oncological applications such as radiotherapy. Although the majority of methods in the literature are designed for ventricle segmentation, there is a real interest in segmenting the heart as a whole in this modality. In this paper, we address this problem and propose an automatic and robust method, based on anatomical knowledge about the heart, in particular its position with respect to the lungs. This knowledge is represented in a fuzzy formalism and it is used both to define a region of interest and to drive the evolution of a deformable model in order to segment the heart inside this region. The proposed method has been applied on non-contrast CT images and the obtained results have been compared to manual segmentations of the heart, showing the good accuracy and high robustness of our approach.     

Landcover classification of satellite images based on an adaptive interval fuzzy c-means algorithm coupled with spatial information

ABSTRACT

Landcover classifications have large uncertainty related to the heterogeneity of similar objects and complex spatial correlations in satellite images, making it difficult to obtain ideal classification results using traditional classification methods. Therefore, to address the uncertainty in landcover classifications based on remotely sensed information, we propose a novel fuzzy c-means algorithm, which integrates adaptive interval-valued modelling and spatial information. It dynamically adjusts the interval width according to the fuzzy degree of the target membership without pre-setting any parameters, controls the fuzziness of the target, and mines the inherent distribution of the data. Furthermore, reliability-based spatial correlation modelling is used to describe the spatial relationship of the target and to improve both robustness and accuracy of the algorithm. Experimental data consisting of SPOT5 (10-m spatial resolution) or Thematic Mapper (30-m spatial resolution) satellite data for three case study areas in China are used to test this algorithm. Compared with other state-of-the-art fuzzy classification methods, our algorithm markedly improved the ground-object separability. Moreover, it balanced improvement of pixel separability and suppression of heterogeneity of intra-class objects, producing more compact landcover areas and clearer boundaries between classes.