Advanced fuzzy set: an application to flat electroencephalography image

Epileptic seizures refer to temporary disturbance in the electrical activity of the brain. The real time electrical activities of the cortical and subcortical neuronal activity are recorded by using Electroencephalogram (EEG) whereby few specific electrodes are placed on the scalp. EEG measures the differential voltage fluctuations resulting from ionic current flows within the neurons of the brain and can detect the changes over milliseconds. In this study, the image form of the EEG signals known as Flat EEG image is carried out. The advanced fuzzy techniques namely intuitionistic fuzzy set (IFS) and type-2 fuzzy set are explored to enhance the image of Flat EEG. The parameter in intuitionistic fuzzy image is optimized using intuitionistic fuzzy entropy. Whereas Hamacher t-conorm is applied for type-2 fuzzy enhancement. Experimental results on Flat EEG input images at two different time show that type-2 produced better output images compared to intuitionistic fuzzy methods

An Adaptive Fuzzy Contrast Enhancement Algorithm with Details Preserving

This paper modifies the Adaptive Contrast Enhancement Algorithm with Details Preserving (ACEDP) technique by integrating a fuzzy element in the image type selection. The proposed technique, named the Adaptive Fuzzy Contrast Enhancement with Details Preserving (AFCEDP) technique, first computes the degree of membership of the input image to three categories, i.e. low-, middle- or high-level images. The AFCEDP technique then clips the histogram at different plateau limits that are computed from both the degree of membership and the clipping functions. The classification of an image in the ACEDP technique is done based solely on the intensity range of the maximum number of pixels, which may be inaccurate. In the proposed AFCEDP technique, the image type classification is handled in a better way with the integration of a fuzzy element. The performance of the proposed AFCEDP technique was compared with the conventional ACEDP technique and several state-of-art techniques described in the literature. The simulation results revealed that the AFCEDP technique demonstrates good capability in contrast enhancement and detail preservation. In addition, the experiments using cervical cell images and HEp-2 cell images showed great potential of the AFCEDP technique as a technique for enhancing medical microscopic images

An Adaptive Fuzzy Contrast Enhancement Algorithm with Details Preserving

This paper modifies the Adaptive Contrast Enhancement Algorithmwith Details Preserving (ACEDP) technique by integrating a fuzzy element inthe image type selection. The proposed technique, named the Adaptive FuzzyContrast Enhancement with Details Preserving (AFCEDP) technique, firstcomputes the degree of membership of the input image to three categories, i.e.low-, middle- or high-level images. The AFCEDP technique then clips thehistogram at different plateau limits that are computed from both the degree ofmembership and the clipping functions. The classification of an image in theACEDP technique is done based solely on the intensity range of the maximumnumber of pixels, which may be inaccurate. In the proposed AFCEDP technique,the image type classification is handled in a better way with the integration of afuzzy element. The performance of the proposed AFCEDP technique wascompared with the conventional ACEDP technique and several state-of-arttechniques described in the literature. The simulation results revealed that theAFCEDP technique demonstrates good capability in contrast enhancement anddetail preservation. In addition, the experiments using cervical cell images andHEp-2 cell images showed great potential of the AFCEDP technique as atechnique for enhancing medical microscopic images

Enhancement of dronogram aid to visual interpretation of target objects via intuitionistic fuzzy hesitant sets

In this paper, we address the hesitant information in enhancement task often caused by differences in image contrast. Enhancement approaches generally use certain filters which generate artifacts or are unable to recover all the objects details in images. Typically, the contrast of an image quantifies a unique ratio between the amounts of black and white through a single pixel. However, contrast is better represented by a group of pix- els. We have proposed a novel image enhancement scheme based on intuitionistic hesi- tant fuzzy sets (IHFSs) for drone images (dronogram) to facilitate better interpretations of target objects. First, a given dronogram is divided into foreground and background areas based on an estimated threshold from which the proposed model measures the amount of black/white intensity levels. Next, we fuzzify both of them and determine the hesitant score indicated by the distance between the two areas for each point in the fuzzy plane. Finally, a hyperbolic operator is adopted for each membership grade to improve the pho- tographic quality leading to enhanced results via defuzzification. The proposed method is tested on a large drone image database. Results demonstrate better contrast enhancement, improved visual quality, and better recognition compared to the state-of-the-art methods.Web of Science500866

Application of a Mamdani-type fuzzy rule-based system to segment periventricular cerebral veins in susceptibility-weighted images

This paper presents an algorithm designed to segment veins in the periventricular region of the brain in susceptibility-weighted magnetic resonance images. The proposed algorithm is based on a Mamdani-type fuzzy rule-based system that enables enhancement of veins within periventricular regions of interest as the first step. Segmentation is achieved after determining the cut-off value providing the best trade-off between sensitivity and specificity to establish the suitability of each pixel to belong to a cerebral vein. Performance of the algorithm in susceptibility-weighted images acquired in healthy volunteers showed very good segmentation, with a small number of false positives. The results were not affected by small changes in the size and location of the regions of interest. The algorithm also enabled detection of differences in the visibility of periventricular veins between healthy subjects and multiple sclerosis patients. © Springer International Publishing Switzerland 2016.Postprint (author's final draft

A Heuristic Neural Network Structure Relying on Fuzzy Logic for Images Scoring

Traditional deep learning methods are sub-optimal in classifying ambiguity features, which often arise in noisy and hard to predict categories, especially, to distinguish semantic scoring. Semantic scoring, depending on semantic logic to implement evaluation, inevitably contains fuzzy description and misses some concepts, for example, the ambiguous relationship between normal and probably normal always presents unclear boundaries (normal − more likely normal - probably normal). Thus, human error is common when annotating images. Differing from existing methods that focus on modifying kernel structure of neural networks, this study proposes a dominant fuzzy fully connected layer (FFCL) for Breast Imaging Reporting and Data System (BI-RADS) scoring and validates the universality of this proposed structure. This proposed model aims to develop complementary properties of scoring for semantic paradigms, while constructing fuzzy rules based on analyzing human thought patterns, and to particularly reduce the influence of semantic conglutination. Specifically, this semantic-sensitive defuzzier layer projects features occupied by relative categories into semantic space, and a fuzzy decoder modifies probabilities of the last output layer referring to the global trend. Moreover, the ambiguous semantic space between two relative categories shrinks during the learning phases, as the positive and negative growth trends of one category appearing among its relatives were considered. We first used the Euclidean Distance (ED) to zoom in the distance between the real scores and the predicted scores, and then employed two sample t test method to evidence the advantage of the FFCL architecture. Extensive experimental results performed on the CBIS-DDSM dataset show that our FFCL structure can achieve superior performances for both triple and multiclass classification in BI-RADS scoring, outperforming the state-of-the-art methods