Fuzzy reasoning approach to similarity evaluation in image analysis

Fuzzy similarity index for discrimination of EEG signals

In this study, a new approach based on the computation of fuzzy similarity index was presented for discrimination of electroencephalogram (EEG) signals. The EEG, a highly complex signal, is one of the most common sources of information used to study brain function and neurological disorders. The analyzed EEG signals were consisted of five sets (set A-healthy volunteer, eyes open; set B-healthy volunteer, eyes closed; set C-seizure-free intervals of five patients from hippocampal formation of opposite hemisphere; set D-seizure-free intervals of five patients from epileptogenic zone; set E-epileptic seizure segments). The EEG signals were considered as chaotic signals and this consideration was tested successfully by the computation of Lyapunov exponents. The computed Lyapunov exponents were used to represent the EEG signals. The aim of the study is discriminating the EEG signals by the combination of Lyapunov exponents and fuzzy similarity index. Toward achieving this aim, fuzzy sets were obtained from the feature sets (Lyapunov exponents) of the signals under study. The results demonstrated that the similarity between the fuzzy sets of the studied signals indicated the variabilities in the EEG signals. Thus, the fuzzy similarity index could discriminate the healthy EEG segments (sets A and B) and the other three types of segments (sets C, D, and E) recorded from epileptic patients.

Cooperation of fuzzy segmentation operators for correction aliasing phenomenon in 3D color Doppler imaging

The study described in this paper concerns natural object modeling in the context of uncertain, imprecise and inconsistent representation. We propose a fuzzy system which offers a global modeling of object properties such as color, shape, velocity, etc. This modeling makes a transition from a low level reasoning (pixel level), which implies a local precise but uncertain representation, to a high level reasoning (region level), inducing a certain assignment. So, we use fuzzy structured partitions characterizing these properties. At this level. each property will have its own global modeling. Then, these different models are merged for decision making. Our approach was tested with several applications. In particular, we show here its performance in the area of blood flow analysis from 3D color Doppler images in order to quantify and study the development of this flow. We present methods that detect and correct aliasing phenomenon, i.e. inconsistent information. At first, the flow space is partitioned into fuzzy sectors where each sector is defined by a center, an angle and a direction. In parallel, the velocity information carried by the pixels is classified into fuzzy classes. Then, by combining these two partitions, we obtain the velocity distribution into sectors. Moreover, for each found path (from the first sector to the last one), we locate and correct inconsistent velocities by applying global rules. After extracting some meaningful sector features, the fuzzy modeling, applied to the aliasing correction, makes it possible to simplify and synthesize the blood flow direction.

Extensions to the fuzzy pointed set with applications to image processing

In all man-machine systems with image processing functions, an important unsolved problem arises in the treatment of uncertain and incomplete image information. Several frameworks have been suggested for handling uncertain image information including; expert systems, fuzzification, likelihood estimation, and neural networks. In this paper we review those methods. We also present a new method for handling uncertainties by unifying the representations of gray-values and uncertainty into one framework in a way that parallels fuzzy logic. This new framework is based on the application of the extended fuzzy pointed set and an associated algebra to handle uncertain information. We further show how this framework can be used in image processing and artificial intelligence.