A fuzzy operator for the enhancement of blurred and noisy images

An extensible six-step methodology to automatically generate fuzzy DSSs for diagnostic applications

Background

The diagnosis of many diseases can be often formulated as a decision problem; uncertainty affects these problems so that many computerized Diagnostic Decision Support Systems (in the following, DDSSs) have been developed to aid the physician in interpreting clinical data and thus to improve the quality of the whole process. Fuzzy logic, a well established attempt at the formalization and mechanization of human capabilities in reasoning and deciding with noisy information, can be profitably used. Recently, we informally proposed a general methodology to automatically build DDSSs on the top of fuzzy knowledge extracted from data.

Methods

We carefully refine and formalize our methodology that includes six stages, where the first three stages work with crisp rules, whereas the last three ones are employed on fuzzy models. Its strength relies on its generality and modularity since it supports the integration of alternative techniques in each of its stages.

Results

The methodology is designed and implemented in the form of a modular and portable software architecture according to a component-based approach. The architecture is deeply described and a summary inspection of the main components in terms of UML diagrams is outlined as well. A first implementation of the architecture has been then realized in Java following the object-oriented paradigm and used to instantiate a DDSS example aimed at accurately diagnosing breast masses as a proof of concept.

Conclusions

The results prove the feasibility of the whole methodology implemented in terms of the architecture proposed.

An optimal fuzzy system for color image enhancement

A Gaussian membership function is proposed to fuzzify the image information in spatial domain. We introduce a global contrast intensification operator (GINT), which contains three parameters, viz., intensification parameter t, fuzzifier fh, and the crossover point mu(c), for enhancement of color images. We define fuzzy contrast-based quality factor Qf and entropy-based quality factor Qe and the corresponding visual factors for the desired appearance of images. By minimizing the fuzzy entropy of the image information with respect to these quality factors, the parameters t, fh, and mu(c) are calculated globally. By using the proposed technique, a visible improvement in the image quality is observed for under exposed images, as the entropy of the output image is decreased. The terminating criterion is decided by both the visual and quality factors. For over exposed and under plus over exposed images, the proposed fuzzification function needs to be modified by taking maximum intensity as the fourth parameter. The type of the images is indicated by the visual factor which is less than 1 for under exposed images and more than 1 for over exposed images.

Enhancement of fluoroscopic images with varying contrast

A heuristic algorithm for enhancement of fluoroscopic images of varying contrast is proposed. The new technique aims at identifying a suitable type of enhancement for different locations in an image. The estimation relies on simple preliminary classification of image parts into one of the following types: uniform, sharp (with sufficient contrast), detail-containing (structure present) and unknown (for the cases where it is difficult to make a decision). Different smoothing techniques are applied locally in the different types of image parts. For those parts that are classified as detail-containing, probable object boundaries are identified and local sharpening is carried out to increase the contrast at these places. The adopted approach attempts to improve the quality of an image by reducing available noise and simultaneously increasing the contrast at probable object boundaries without increasing the overall dynamic range. In addition, it allows noise to be cleaned, that at some locations is stronger than the fine structure at other locations, whilst preserving the details.

Realizations of fast 2-D/3-D image filtering and enhancement

This paper proposes a novel algorithm for multidimensional image enhancement based on a fuzzy domain enhancement method, and an implementation of a recursive and separable low-pass filter. Considering a smoothed image as a fuzzy data set, each pixel in an image is processed independently, using fuzzy domain transformation and enhancement of both the dynamic range and the local gray level variations. The algorithm has the advantages of being fast and adaptive, so it can be used in real-time image processing applications and for multidimensional data with low computational cost. It also has the ability to reduce noise and unwanted background that may affect the visualization quality of two-dimensional (2-D)/three-dimensional (3-D) data. Examples for the applications of the algorithm are given for mammograms, ultrasound 3-D images, and photographic images.