An optimal fuzzy system for color image enhancement

Contrast Enhancement of Scintigraphic Image Using Fuzzy Intensification

Introduction

The objective of this study was to see the effect of fuzzy intensification (INT) operator on enhancement of scintigraphic image.

Materials and Methods

Nuclear medicine physician (NMP) provided 25 scintigraphic images that required enhancement. The image pixels value was converted into fuzzy plane and was subjected to contrast INT operator with parameters of INT operator i.e., cross-over = 0.5 and number of iterations = 1 and 2. The enhanced image was again brought back into spatial domain (de-fuzzification) whose intensity value was in the range 0-255. NMP compared the enhanced image with its input image and labeled it as acceptable or unacceptable. The quality of enhanced image was also accessed objectively using four different image metrics namely: Entropy, edge content, absolute mean brightness error and saturation metrics.

Results

Most of the enhanced images (18 out of 25 images) obtained at cross-over = 0.5 and number of iterations = 1 are acceptable and found to have overall better contrast compared to the corresponding input image. Four images (two brain positron emission tomography scan and two I-131 scan) obtained at cross-over = 0.5 and with iteration = 2 are acceptable. Three input images (one dimercaptosuccinic acid (DMSA), one I-131 and one I-131- metaiodo-benzyl-guanidine (MIBG) scan) were better than their enhanced images.

Conclusions

The enhancement produced by fuzzy INT operator was encouraging. Majority of enhanced images were acceptable at cross-over = 0.5 and number iterations = 1.

Formulation of probability-based pervasive information set features and Hanman transform classifier for the categorization of mammograms

With aim of detecting breast cancer at the early stages using mammograms, this study presents the formulation of five feature types by extending the information set to encompass the concept of an intuitionist fuzzy set. The resulting pervasive information set gives not only the certainty of the pixel intensities of mammograms to a class but also the deficiency in the fuzzy modeling referred to as the hesitancy. The generalized adaptive Hanman Anirban fuzzy entropy function is shown to be equivalent to the hesitancy entropy function. The probability-based fuzzy Hanman transform and the pervasive Information with probability taking the role of hesitancy degree help derive the above five feature types termed as probability-based pervasive Information set features. The effectiveness of each feature type is demonstrated on the mini-MIAS and DDSM databases for the multi-class categorization of mammograms using the Hanman transform classifier. The statistical analysis by ANOVA test proves that the features are statistically significant and the experimental results are shown to be clinically relevant by the expert radiologists. The performance of the five feature types is either superior to or equal to that of some deep learning architectures on comparison but they outperform the state-of-the-art literature methods in the classification of breast cancer using mammograms.

Fuzzy-Contextual Contrast Enhancement

This paper presents contrast enhancement algorithms based on fuzzy contextual information of the images. We introduce fuzzy similarity index and fuzzy contrast factor to capture the neighborhood characteristics of a pixel. A new histogram, using fuzzy contrast factor of each pixel is developed, and termed as the fuzzy dissimilarity histogram (FDH). A cumulative distribution function (CDF) is formed with normalized values of FDH and used as a transfer function to obtain the contrast enhanced image. The algorithm gives good contrast enhancement and preserves the natural characteristic of the image. In order to develop a contextual intensity transfer function, we introduce a fuzzy membership function based on fuzzy similarity index and coefficient of variation of the image. The contextual intensity transfer function is designed using the fuzzy membership function to achieve final contrast enhanced image. The overall algorithm is referred as the fuzzy contextual contrast-enhancement (FCCE) algorithm. The proposed algorithms are compared with conventional and state-of-art contrast enhancement algorithms. The quantitative and visual assessment of the results is performed. The results of quantitative measures are statistically analyzed using t-test. The exhaustive experimentation and analysis show the proposed algorithm efficiently enhances contrast and yields in natural visual quality images.

Efficient contrast enhancement using adaptive gamma correction with weighting distribution

This paper proposes an efficient method to modify histograms and enhance contrast in digital images. Enhancement plays a significant role in digital image processing, computer vision, and pattern recognition. We present an automatic transformation technique that improves the brightness of dimmed images via the gamma correction and probability distribution of luminance pixels. To enhance video, the proposed image-enhancement method uses temporal information regarding the differences between each frame to reduce computational complexity. Experimental results demonstrate that the proposed method produces enhanced images of comparable or higher quality than those produced using previous state-of-the-art methods.