Fuzzy Edge Detection Research Articles

Morphological analysis of 3D SPECT images via nilpotent t-norms in diagnosis of Alzheimer's disease

We propose a fuzzy image processing approach serving as a potential diagnostic tool for Alzheimer's disease. We have set up a sequence of several image-processing methods based on morphological fuzzy edge detection followed by a region-growing segmentation. We applied these operations to two sets of three-dimensional SPECT images of human brains: one set of images was composed of patients with Alzheimer's disease and the second was represented by control brains from non-demented patients. Then we undertook an analysis of nilpotent t-norms forming the edge detectors and the parameter of Gaussian filter, and in the end we carried out an evaluation of segments that was computed after the final watershed segmentation. This article provides a detailed description of these methods as well as the analysis of evaluated data using Student's two-sample t-test. Traditional gradient magnitude edge detectors are included and compared with fuzzy detectors. Our goal was to demonstrate the usefulness of the Łukasiewicz BL-algebra in feature extraction of 3D biomedical images by using enhanced methods of image morphology.

A FUZZY DIFFERENCE BASED EDGE DETECTOR

In this paper, a new algorithm for edge detection based on fuzzy concept is suggested. The proposed approach defines dynamic membership functions for different groups of pixels in a 3 by 3 neighborhood of the central pixel. Then, fuzzy distance and �-cut theory are applied to detect the edge map by following a simple heuristic thresholding rule to produce a thin edge image. A large number of experiments are employed to confirm the robustness of the proposed algorithm. In the experiments different cases such as normal images, images corrupted by Gaussian noise, and uneven lightening images are involved. The results obtained are compared with some famous algorithms such as Canny and Sobel operators, a competitive fuzzy edge detector, and a statistical based edge detector. The visual and quantitative comparisons show the effectiveness of the proposed algorithm even for those images that were corrupted by strong noise.

AN FUZZY NEURAL APPROACH FOR MEDICAL IMAGE RETRIEVAL

Image retrieval based on a query image is necessary for effective and efficient use the information that is stored in medical image databases. Medical Image Retrieval is difficult as not only the localization and directionality of human visual system is to be considered but also the pathological condition. Image identification and segmentation for feature extraction pose a challenge to image retrieval process. Challenges posed include large number of images to be processed for the image retrieval and identifying the region of interest automatically to optimize the search. In this study, we propose a novel image segmentation algorithm Fuzzy Edge Detection and Segmentation (FEDS). The proposed FEDS algorithm is tested on medical images and for classification of images, a bell fuzzy multilayer perceptron is proposed. The proposed neural network Bell Fuzzy Multi Layer Perceptron (BF-MLP) Neural network is constructed by introducing a fuzzy logic in hidden layer with the sugeno model and bell function. The proposed neural network consists of two layers with the first layer being a tanh activation function and the second layer containing the bell fuzzy activation function. The proposed FEDS method was implemented using Matlab and Modelsim. A total of 44 images were considered with three class labels. The edge obtained for which segmentation is done using the proposed segmentation algorithm. The proposed BF-MLP neural network algorithm was implemented using Visual Studio and the classification accuracy compared with MLP Neural Network with sigmoid activation function. In this study, a fuzzy segmentation algorithm and a fuzzy classification algorithm is proposed to improve the medical image retrieval accuracy. The proposed segmentation algorithm, Fuzzy Edge Detection and Segmentation (FEDS), was implemented using Matlab and features were extracted using Fast Hartley Transform (FHT). The extracted features were used to train the proposed neural network, Bell Fuzzy Multi Layer Perceptron Neural Network (BF-MLP). 44 images with 3 class labels were used to test the algorithm and classification accuracy of 93.2% was obtained.

Edge Detection Combined Local Gray Level Difference and Competitive Fuzzy Logic

In this paper, we present a new approach by local gray level difference based competitive fuzzy edge detection. In the light of human visual perception, a preprocessing step is proposed to simplify original images and further enhance the performance of edge extraction. Then we define the feature vector of each pixel in four directions and six edge prototype. Finally, BP neural network is used to classify the type of edge, and the competitive rule is adopted to thin the thick edge image. From the experimental result, it can be seen that the edge detection method proposed in this paper is superior to Canny method and Log method under the noisy condition.

Fuzzy automated visual broken edge detection

This paper describes an automated machine vision-based inspection method for fast and accurate detection of broken edges on the machined surface of water pumps which are formed during casting. The paper proposes a collection of steps to inspect images taken in a noisy environment to identify broken edges. By developing three broken edge verification features and using fuzzy C-means clustering, we provide a fuzzy broken edge inspection model that classifies water pumps into three classes: pumps with significant (major) broken edges, pumps with insignificant (minor) broken edges, and pumps with no broken edges. The devised machine vision-based inspection method is efficient in terms of processing time and accuracy to recognize the parts with broken edges and to make decisions consistent with human knowledge. The fuzzy broken edge detection was carried out on a database of 150 gray-level images. The developed method properly identifies about 95 % of the broken edges in the entire database.

Different Edge Detection Algorithms Comparison and Analysis on Handwritten Chinese Character Recognition

The acquired image will has different levels of noise pollution and image distortion in Handwritten Chinese character recognition document image processing. For these situations the accurate and fast edge detection method is an important prerequisite for the recognition results. The widely used edge detection algorithms such as: first derivative-based edge detection method, second derivative edge detection method, canny operator, mathematical morphology edge detection and fuzzy edge detection method. In this paper according to our database of handwritten Chinese characters a lot of experiments to compare the excellent of various algorithms in the field of Chinese character handwriting recognition application. These algorithms are sensitive to image noise and we nowadays discuss new edge detection for the theory of wavelet edge detection method and genetic algorithms. According to our experimental results we explore the new edge detection method to improve the edge detection, image segmentation accuracy and quickness.

Memristive fuzzy edge detector

Fuzzy inference systems always suffer from the lack of efficient structures or platforms for their hardware implementation. In this paper, we tried to overcome this difficulty by proposing a new method for the implementation of the fuzzy rule-based inference systems. To achieve this goal, we have designed a multi-layer neuro-fuzzy computing system based on the memristor crossbar structure by introducing a new concept called the fuzzy minterm. Although many applications can be realized through the use of our proposed system, in this study we only show how the fuzzy XOR function can be constructed and how it can be used to extract edges from grayscale images. One main advantage of our memristive fuzzy edge detector (implemented in analog form) compared to other commonly used edge detectors is it can be implemented in parallel form, which makes it a powerful device for real-time applications.

Image segmentation with complicated background by using seeded region growing

This study proposes a novel seeded region growing based image segmentation method for complicated background in both color and gray level images. The proposed fuzzy edge detection method, that only detects the connected edge, is used with fuzzy image pixel similarity to automatically select the initial seeds not in the detail and complicated background. The fuzzy distance is used to determine the difference between the pixel and region in the consequent region growing and the difference between two regions in the region merging. The conventional region growing is modified in this study to ensure that the pixel on the edge is processed later than other pixels. Finally, the simulations in study prove that the proposed method is better than other existing segmentation methods.

Exudates segmentation using inverse surface adaptive thresholding

This paper presents a new approach to detect exudates and optic disc from color fundus images based on inverse surface thresholding. The strategy involves the applications of fuzzy c-means clustering, edge detection, otsu thresholding and inverse surface thresholding. The main advantage of the proposed approach is that it does not depend on manually selected parameters that are normally chosen to suit the tested databases. When applied to two sets of databases the proposed method outperforms methods based on watershed segmentation and morphological reconstruction. The proposed method obtained 98.2 and 90.4 in terms of sensitivity for Standard Diabetic Retinopathy Database – Calibration Level 1 (DIARETDB1) and a local dataset provided by National University Hospital of Malaysia (NUHM), respectively.

The Study of an Improved Fuzzy Edge Detection Algorithm in the Radial Tire Quality Detection

The internal radial tire defects will affect the performance and the service life of the automobile. But the traditional detection method is to observe and detect in eyes using X-ray map which result that a lot of quality problems can not properly been judged out. This paper describes the X-ray image recognition using a fuzzy algorithm. The improved algorithm simplifies the traditional fuzzy algorithm complexity without the transform and inverse transform. The experimental results show that the improved algorithm makes the computational efficiency and extract more refined and clear edge for the radial tire which can correctly identify the failure and provide the necessary data processing.

Image Edge Detection Based on Direction Fuzzy Entropy

To improve the ability of the fuzzy edge detection and anti-noise performance, the paper proposes a new weighted direction fuzzy entropy image edge detection method. The proposed method converts the feature space of image gray to the fuzzy feature space, and then extracts the weighted information measure of the direction structural in the fuzzy entropy feature space. Finally, the proposed method determines the edge pixel by an adaptive threshold after non-maxima suppression. The experiment demonstrates that the proposed method can extract the image edges effectively by means of the fuzzy edge detection.

Fuzzy Edge Detection Based on Maximum Entropy Thresholding

This paper presents a fuzzy edge detection technique based on the maximum fuzzy entropy principle. The concept of fuzzy probability and fuzzy partition is introduced first. Then, based on the conditional probabilities and fuzzy partition, a two-level optimal thresholding is searched adaptively through the maximum fuzzy entropy principle of the gradient image and then the edge images are obtained.

Automated Identification of Exudates and Optic Disc Based on Inverse Surface Thresholding

This paper presents a new approach to detect exudates and optic disc from color fundus images based on inverse surface thresholding. The strategy involves the applications of fuzzy c-means clustering, edge detection, otsu thresholding and inverse surface thresholding. The main advantage of the proposed approach is that it does not depend on manually selected parameters that are normally chosen to suit the tested databases. When applied to two sets of databases the proposed method outperforms a method based on watershed segmentation.

MEDICAL IMAGE COMPRESSION USING HYBRID CODER WITH FUZZY EDGE DETECTION

Medical imaging techniques produce prohibitive amounts of digitized clinical data. Compression of medical images is a must due to large memory space required for transmission and storage. This paper presents an effective algorithm to compress and to reconstruct medical images. The proposed algorithm first extracts edge information of medical images by using fuzzy edge detector. The images are decomposed using Cohen-Daubechies-Feauveau (CDF) wavelet. The hybrid technique utilizes the efficient wavelet based compression algorithms such as JPEG2000 and Set Partitioning In Hierarchical Trees (SPIHT). The wavelet coefficients in the approximation sub band are encoded using tier 1 part of JPEG2000. The wavelet coefficients in the detailed sub bands are encoded using SPIHT. Consistent quality images are produced by this method at a lower bit rate compared to other standard compression algorithms. Two main approaches to assess image quality are objective testing and subjective testing. The image quality is evaluated by objective quality measures. Objective measures correlate well with the perceived image quality for the proposed compression algorithm.

An improvement in fuzzy entropy edge detection for X-ray imaging

The following paper discusses the topic of edge detection in X-ray hand images. It criticises the existing solution by highlighting a design fault, which is a carelessly chosen function and then proposes a way to eliminate the fault by replacing it with a better suited function. The search for this function and its results are also discussed in this paper. It also presents the aspect of pre- and postprocessing through filtering as another improvement in edge detection.

Use of fuzzy edge single-photon emission computed tomography analysis in definite Alzheimer's disease - a retrospective study

BackgroundDefinite Alzheimer's disease (AD) requires neuropathological confirmation. Single-photon emission computed tomography (SPECT) may enhance diagnostic accuracy, but due to restricted sensitivity and specificity, the role of SPECT is largely limited with regard to this purpose.MethodsWe propose a new method of SPECT data analysis. The method is based on a combination of parietal lobe selection (as regions-of-interest (ROI)), 3D fuzzy edge detection, and 3D watershed transformation. We applied the algorithm to three-dimensional SPECT images of human brains and compared the number of watershed regions inside the ROI between AD patients and controls. The Student's two-sample t-test was used for testing domain number equity in both groups.ResultsAD patients had a significantly reduced number of watershed regions compared to controls (p < 0.01). A sensitivity of 94.1% and specificity of 80% was obtained with a threshold value of 57.11 for the watershed domain number. The narrowing of the SPECT analysis to parietal regions leads to a substantial increase in both sensitivity and specificity.ConclusionsOur non-invasive, relatively low-cost, and easy method can contribute to a more precise diagnosis of AD.

Method for Crater Detection From Martian Digital Topography Data Using Gradient Value/Orientation, Morphometry, Vote Analysis, Slip Tuning, and Calibration

Recently, all the craters from the major currently available manually assembled catalogs have been merged into the catalog with 57 633 known Martian impact craters. This paper presents a new crater detection algorithm (CDA) for the search of still uncataloged impact craters. The CDA is based on fuzzy edge detectors and Radon/Hough transform and utilizes digital topography data instead of image data. The critical parts of the method providing increased accuracy are as follows: (1) gradient-value/orientation-based techniques; (2) automated morphometry measurements of depth/diameter ratio, circularity, topographic cross-profile, rim, central peak, and radial range where the crater is preserved; (3) circularity analysis of votes in parameter space; (4) slip tuning of detected craters' parameters; and (5) calibration which partially compensates differences in morphology between small and large craters. Using the framework for the evaluation of CDAs, in comparison with prior work, the proposed detector shows the following: (1) significantly larger area under the free-response receiver operating characteristics (AUROC) and (2) significantly larger number of correct detections. Using the Mars Orbiter Laser Altimeter data as input, the CDA proposed numerous candidates for GT-57633 catalog extension. After the manual survey of all proposed craters and rejection of false detections, 57 592 impact craters were confirmed as correct detections. The accompanying result to the CDA is a new GT-115225 catalog.

A novel fuzzy edge detection of seismic images based on bi-level maximum entropy thresholding

The study of edge detection techniques in earthquake engineering is extremely important for recognition of seismic faults in the viewpoint of disaster prevention. Further, Seismic datasets are huge (several terabytes), redundant and complex. Hence, cost increases to large extent for storage and transmission against the limited memory and bandwidth. This paper presents a novel fuzzy edge detection technique of seismic images based on bi-level maximum entropy thresholding principle. Seismic edge images are obtained based on the concept of fuzzy conditional probabilities, fuzzy partition and adaptively searching the two-level optimal threshold through maximum fuzzy entropy of seismic gradient images.

Hybrid wet paper coding mechanism for steganography employing n-indicator and fuzzy edge detector

Data hiding technique can facilitate security and the safe transmission of important information in the digital domain, which generally requires a high embedding payload and good stego image quality. Recently, a steganographic framework known as wet paper coding has been utilized as an effective strategy in image hiding to achieve the requirements of high embedding payload, good quality and robust security. In this paper, besides employing this mechanism as a fundamental stage, we take advantage of two novel techniques, namely, an efficient n -indicator and a fuzzy edge detector. The first is to increase the robustness of the proposed system to guard against being detected or traced by the statistics methods while allowing the receiver without knowledge of secret data positions to retrieve the embedded information. The second is to improve the payload and enhance the quality of stego image. The experimental results show that our proposed scheme outperforms its ability to reduce the conflict among three steganography requirements.

Efficient Edge Detection Using Fuzzy Heuristic Particle Swarm Optimization

This paper presents a hybridization of Particle Swarm Optimization (PSO) and Fuzzy edge detector. The edge detector is used as the initial population and as the objective function. The purpose of hybridizing the algorithm is to create an optimized edge detector. Classical Fuzzy Heuristics (CFH) detects thick edges. These thick edges need to be optimized to obtain a thin line. In this research the PSO is used to optimize the edge detection detected by the CFH algorithm and it is referred to as FHPSO. The test images are radiographs images of the metacarpal. These images have been used, because there is a need to detect strong and thin edges. Radiograph images are noisy in nature, which makes it difficult to measure the cortical thickness, the cortical outline of the inner cortical and outer cortical of the long tubular bone. The outer cortical edges are considered to be the strong edges due to high discontinuity values and the inner cortical edges are considered weak edges due to low their discontinuity values. The performance of FHPSO in detecting edges has been shown to be quite efficient.