Fuzzy Image Processing Research Articles

Automatic screening and classification of diabetic retinopathy and maculopathy using fuzzy image processing.

Digital retinal imaging is a challenging screening method for which effective, robust and cost-effective approaches are still to be developed. Regular screening for diabetic retinopathy and diabetic maculopathy diseases is necessary in order to identify the group at risk of visual impairment. This paper presents a novel automatic detection of diabetic retinopathy and maculopathy in eye fundus images by employing fuzzy image processing techniques. The paper first introduces the existing systems for diabetic retinopathy screening, with an emphasis on the maculopathy detection methods. The proposed medical decision support system consists of four parts, namely: image acquisition, image preprocessing including four retinal structures localisation, feature extraction and the classification of diabetic retinopathy and maculopathy. A combination of fuzzy image processing techniques, the Circular Hough Transform and several feature extraction methods are implemented in the proposed system. The paper also presents a novel technique for the macula region localisation in order to detect the maculopathy. In addition to the proposed detection system, the paper highlights a novel online dataset and it presents the dataset collection, the expert diagnosis process and the advantages of our online database compared to other public eye fundus image databases for diabetic retinopathy purposes.

Towards a Heterogeneous Navigation Team of Aerial-Ground Robots Based on Fuzzy Image Processing

In this paper, aerial-ground robots system has been presented that composed by ground and blimp robots. The blimp robot equipped with single camera in order to scan the environment to detect the ground robot and any obstacles in the environment. The detection algorithm has been designed based on fuzzy edge detection and shape- color features techniques. The blimp will share the environment map to the ground control station. Then, the global optimal path with avoiding obstacles is generated by enhanced genetic algorithm by modified the search A*. Several experiments were carried out in a simulation as well as indoor environments to verify the system applicability. The proposed robotic system shows good results in simultaneous navigation and mapping applications. 

Developing an Intelligent Automatic Appendix Extraction Method from Ultrasonography Based on Fuzzy ART and Image Processing.

Ultrasound examination (US) does a key role in the diagnosis and management of the patients with clinically suspected appendicitis which is the most common abdominal surgical emergency. Among the various sonographic findings of appendicitis, outer diameter of the appendix is most important. Therefore, clear delineation of the appendix on US images is essential. In this paper, we propose a new intelligent method to extract appendix automatically from abdominal sonographic images as a basic building block of developing such an intelligent tool for medical practitioners. Knowing that the appendix is located at the lower organ area below the bottom fascia line, we conduct a series of image processing techniques to find the fascia line correctly. And then we apply fuzzy ART learning algorithm to the organ area in order to extract appendix accurately. The experiment verifies that the proposed method is highly accurate (successful in 38 out of 40 cases) in extracting appendix.

Research of semantic understanding on target region of interest for fuzzy image

Image understanding can provide an accurate and rapid target identification method for some military affairs, public security, finance and other departments. How to carry out the semantic understanding of scene of fuzzy image is a problem to be solved urgently in many departments. Moreover, research on this problem is still in an initial stage at home and abroad. For fuzziness, incompleteness and scene semantic understanding on fuzzy image processing, this paper studies a variety of fuzzy signals, analyzes the uncertainties classification and their influence, constructs a three-tier processing mode framework that can eliminate fuzziness processing, repair processing, dynamic combination processing, and presents some methods and algorithms for fuzzy signal processing. Through defining the support, this paper selects target region of interest (ROI), extracts some multidimensional and effective characteristics of targets on ROI, and builds a fuzzy recognition algorithm for targets. By defining the correlative importance between targets, as well as expert knowledge or associated experimental data, this paper carries out a semantic scene understanding on ROI, and presents a semantic understanding method for fuzzy image understanding. Using the combination method of simulation and instance experiments, this paper systematically analyzes the validity of the model and algorithms. The research developed in this paper can pave a new way to improve the real-time, speed and accuracy of information processing, have an important theoretical reference and practical significance, further form the theoretic base for uncertain information processing, and also provide a new idea and way on target recognition for a variety of scenarios.

Automatic parking of vehicles: A review of literatures

Collision accidents often occur during parking or reversing cars. In allusion to this point, this paper conducts a review of literatures on automatic parking. To begin with, a brief introduction of automatic parking including its background and significance is given. Then its commercial application, research status and latest progress are summarized which include visual perception, ultrasonic sensors and radar technology, path planning, control algorithms based on fuzzy theory, neural network, image processing and recognition technology, and digital signal processing technology, etc. On further analysis, some reasonable conclusions are drawn and the future work is suggested.

Intelligent Model for Manual Sorting of Plastic Wastes

The need for an automated sorting system cannot be overemphasized due to the growing needs for high throughput and accuracy. Manual sorting method may achieve good accuracy, but at the expense of throughput. This study formulates an automated plastic wastes identification model to overcome the limitation of the manual sorting method. The proposed identification model employed singleton Sugeno fuzzy model, image processing and template matching techniques as its classifier using physical properties of plastics such as power spectrum of sound, plastic average area and plastic recycling code as feature set. The model was developed and simulated in the MATLAB R2012a environment. The performance evaluation of the model was carried out using three performance metrics, namely, accuracy, precision and recall. The model obtained average accuracy of 0.88, average precision value of 0.77 and average recall value of 0.25, respectively for the plastic types (PET, HDPE, LDPE, and PP) used in this study.

An Advanced Fuzzy Logic Based Traffic Controller

Traffic light plays an important role in the urban traffic management. Therefore, it is necessary to improve the traffic controller for effective traffic management and better traffic flow leading to greener environment. In this paper, an advanced and intelligent traffic light controller is proposed, utilising the fuzzy logic technology and image processing technique. A fuzzy logic control has been implemented to provide the attribute of intelligence to the system. For real-time image acquisition, the process is further linked to the fuzzy logic controller which generates a unique output for each input pattern. Here image processing and fuzzy logic tool boxes of MATLAB are used where the final output is sent to Peripheral Interface Controller (PIC) microcontroller to drive the traffic signals in the desired manner. The results obtained show an improvement of 44% in the overall outcome of traffic management as compared to the conventional traffic controller, marking great feasibility and practicality of the current model.

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.

Efficient Algorithm to Enhance Lung Lobe Images using Fuzzy Filtering

ABSTRACT Preprocessing is done on the CT image of lung for removal of noise. A fuzzy filter is presented for the noise reduction of medical images corrupted with additive noise. The filter operation involves two stages. The first stage computes a fuzzy derivative for eight different directions. The second stage uses these fuzzy derivatives to perform smoothing with the fuzzification and De fuzzification operations along by weighting the contributions of neighboring pixel values. Both stages are based on fuzzy rules. The filter can be applied iteratively to effectively reduce heavy noise. In particular, the shape of the membership functions is adapted according to the remaining noise level after each iteration, making use of the distribution of the homogeneity in the image. 1. INTRODUCTION Medical imaging is the technique that is used to create images of the human body (or parts and function thereof) for clinical purposes (medical procedures seeking to reveal, diagnose or examine disease) or medical science (including the study of normal anatomy and physiology)[3]. The CT images offer detailed information of lung cavities, which could be used for better surgical planning of treating Lung Cancer. Preprocessing is done to remove the noise from the isotropic CT image. The structure of the human lungs is shown here. Fig 1: Structure of human lungs Many existing methods are there for preprocessing. Filtering is the most fundamental operation in image processing and computer vision. The filtered image at a given location is a function of values of the input image in a small neighborhood of the same location. Assuming that images vary slowly over space, near pixels is likely to have similar values. But this assumption fails at regions that contain edges and image details (e.g. corners, lines, end of lines etc.) Most of the classical linear filters like the averaging low pass filters tend to blur and destroy the lines, edges and other fine image details Wiener filter [2] is an optimization filter aimed to minimize the mean square error between the original image and the filtered counterpart. Since noise in CT images follows the Poisson distribution, which can be approximated using a Gaussian distribution for large number of occurrences. Fuzzy image processing [4] is the collection of all approaches that understand, represent and process the images, their segments and feature as fuzzy sets. The representation and processing depend on the selected fuzzy technique and on the problem to be solved. Fuzzy image processing involves three main stages. Image fuzzification, Membership modification, Image defuzzification. Fuzzy image processing using fuzzy techniques plays a very important role in image processing. Fuzzy techniques are important and powerful tools for knowledge representation and processing, and also managing the subjectivity and uncertainty very efficiently. The three important areas that are not perfect are Greyness ambiguity, Geometrical fuzziness, vague knowledge. Fuzzy Geometry, measures of Fuzziness and image information, fuzzy inference systems, fuzzy clustering, Fuzzy mathematical morphology, fuzzy measure theory, Fuzzy Grammars, neural fuzzy are some of important theoretical components of fuzzy image processing scheme. Median filtering is similar to using an averaging filter, in that each output pixel is set to an average of the pixel values in the neighborhood of the corresponding input pixel. However, with median filtering, the value of an output pixel is determined by the median of the neighborhood pixels, rather than the mean. Median filtering is a specific case of order-statistic filtering

Interval-Valued and Intuitionistic Fuzzy Mathematical Morphologies as Special Cases of $\mathbb{L}$ -Fuzzy Mathematical Morphology

Mathematical morphology (MM) offers a wide range of tools for image processing and computer vision. MM was originally conceived for the processing of binary images and later extended to gray-scale morphology. Extensions of classical binary morphology to gray-scale morphology include approaches based on fuzzy set theory that give rise to fuzzy mathematical morphology (FMM). From a mathematical point of view, FMM relies on the fact that the class of all fuzzy sets over a certain universe forms a complete lattice. Recall that complete lattices provide for the most general framework in which MM can be conducted.The concept of \(\mathbb{L}\)-fuzzy set generalizes not only the concept of fuzzy set but also the concepts of interval-valued fuzzy set and Atanassov’s intuitionistic fuzzy set. In addition, the class of \(\mathbb{L}\)-fuzzy sets forms a complete lattice whenever the underlying set \(\mathbb{L}\) constitutes a complete lattice. Based on these observations, we develop a general approach towards \(\mathbb{L}\)-fuzzy mathematical morphology in this paper. Our focus is in particular on the construction of connectives for interval-valued and intuitionistic fuzzy mathematical morphologies that arise as special, isomorphic cases of \(\mathbb{L}\)-fuzzy MM. As an application of these ideas, we generate a combination of some well-known medical image reconstruction techniques in terms of interval-valued fuzzy image processing.

Fuzzy logic and image processing techniques for the interpretation of seismic data

Since interpretation of seismic data is usually a tedious and repetitive task, the ability to do so automatically or semi-automatically has become an important objective of recent research. We believe that the vagueness and uncertainty in the interpretation process makes fuzzy logic an appropriate tool to deal with seismic data. In this work we developed a semi-automated fuzzy inference system to detect the internal architecture of a mass transport complex (MTC) in seismic images. We propose that the observed characteristics of a MTC can be expressed as fuzzy if-then rules consisting of linguistic values associated with fuzzy membership functions. The constructions of the fuzzy inference system and various image processing techniques are presented. We conclude that this is a well-suited problem for fuzzy logic since the application of the proposed methodology yields a semi-automatically interpreted MTC which closely resembles the MTC from expert manual interpretation.

A Novel Method for Fatigue Detection Based on Fuzzy Logic

The problem of fatigue is becoming one of the factors impacting on the working and studying people. Therefore, how to detect whether the people are tired simply and effectively is paid attention to by more and more researchers. A novel method of fatigue testing based on fuzzy logic and image processing is proposed in this paper. Firstly, positive map and side elevation are extracted through camera device. Then motion detection determined to locate human faces and the frequency of eyes opening and closing is derived by using image processing techniques. Finally the angle between the head and the shoulder is calculated combined with a representation of time function and a fatigue threshold is set up. All of these are determined to estimate the degree of fatigue. Experimental results show that, the method can quickly detect the degree of fatigue simply.

An identification method of malignant and benign liver tumors from ultrasonography based on GLCM texture features and fuzzy SVM

In this study, we present an application of fuzzy support vector machine (FSVM) and image processing techniques for identifying liver tumor, including malignant and benign tumors. The gray level co-occurrence matrix (GLCM) matrices were utilized to evaluate the texture features of the regions of interest (ROI) of sonography in our experiment. Five textural features: energy, contrast, correlation, entropy, and homogeneity were extracted from the liver segmented images and analyzed using the texture average of four directions (0°, 45°, 90°, 135°) and distance, δ = 6. The proposed system adopts the FSVM to distinguish between malignant and benign tumor cases more efficiently than support vector machine (SVM). The Gaussian RBF kernel has been used be more suitable for the application of identifying liver tumor from B-Mode ultrasound images than polynomial learning machine kernel and linear network kernel. The values of the parameters gamma ( g) and regularization parameter ( C) have been selected as 0.29 and 4.31 × 10 3, respectively. Via testing over 200 test cases by using RBF kernel, an overall accuracy of 97.0% has been received by the proposed FSVM algorithm. FSVM ( A Z = 0.984 ± 0.014) obtain a better result than SVM ( A Z = 0.963 ± 0.017) in recognition. It is demonstrated that the proposed FSVM algorithm and GLCM texture features technique are feasible and excellent in ultrasonography classification of liver tumor.

Systematic image processing for diagnosing brain tumors: A Type-II fuzzy expert system approach

This paper presents a systematic Type-II fuzzy expert system for diagnosing the human brain tumors (Astrocytoma tumors) using T 1-weighted Magnetic Resonance Images with contrast. The proposed Type-II fuzzy image processing method has four distinct modules: Pre-processing, Segmentation, Feature Extraction, and Approximate Reasoning. We develop a fuzzy rule base by aggregating the existing filtering methods for Pre-processing step. For Segmentation step, we extend the Possibilistic C-Mean (PCM) method by using the Type-II fuzzy concepts, Mahalanobis distance, and Kwon validity index. Feature Extraction is done by Thresholding method. Finally, we develop a Type-II Approximate Reasoning method to recognize the tumor grade in brain MRI. The proposed Type-II expert system has been tested and validated to show its accuracy in the real world. The results show that the proposed system is superior in recognizing the brain tumor and its grade than Type-I fuzzy expert systems.

Counting eosinophils in bronchoalveolar lavage fluid images with fuzzy methodology

This paper proposes a new fuzzy approach to count eosinophils, as a measure of inflammation, in bronchoalveolar lavage fluid images, provided by digital camera through microscope. We use fuzzy cluster analysis and fuzzy classification algorithm to determine the number of objects in an image. For this purpose, a fuzzy image processing procedure consisting of five main stages is presented. The first stage is pre-highlighting the objects in the images by using an image pre-processing method for enhancement, which is sharpening the image with the Laplaian high pass filter in order to have acceptable contrast in the image. The second stage is segmentation by clustering with fuzzy c-mean algorithm for portioning. In this stage the clustered data are the rough symbols of objects in the image containing noise. In the third step, first, a Gaussian low pass filter is used for noise reduction. Then, a contrast adoption in the image is done by modifying the membership functions in the image [H.R. Tizhoosh, G. Krell, B. Michaelis, Knowledge-based enhancement of megavoltage images in radiation therapy using a hybrid neuro-fuzzy system, Image and Vision Computing 19(July) (2000) 217–233]. Object recognition, the fourth stage, will be done by using fuzzy labeling for the objects in the image, using a fuzzy classification method. The number of labeled images shows the number of eosinophils in an image which is an index for diagnosing inflammation. The last stage is tuning parameters and verification of the system performance by using a feed forward Neural Network.

Configurable ultrasonic flaw classification of oil pipelines

These two papers present an innovative method of configurable flaw classification and volume estimation in oil pipelines. In part I, the ultrasonic image acquisition system is introduced and surface and volume of the flaw are estimated with fuzzy image processing. A number of real figures illustrate the system performance. The flops calculation reveals that this fuzzy estimator could be integrated in a real time flaw detection system. In part II, at first, the dynamic detection of interesting points, i.e. as feature points at different levels of images, is proposed using wavelet transform. Furthermore, a guided searching strategy is used for the best matching from the coarse level to a fine level. Then, an error reduction neural network classifies the type of flaw using the wavelet transform as well as area and volume which were produced in Part I. Finally, a statistical error analyser verifies the system outputs by distinct inputs. The numerical results indicate that significant advantages are achieved over the other neural network based flaw characterisation schemes, whereas the accuracy of the predicted flaw profile can be controlled by the resolution of the network.

An Approach to the Detection of Lesions in Mammograms Using Fuzzy Image Processing

An algorithm was developed in this study, using rule-based fuzzy logic, to enable masses that are hard to recognize or detect in mammograms to become more readily perceptible. Small lesions, such as microcalcifications and other masses that are hard to recognize, especially on film scan mammograms, were processed through segmentation. A total of 40 mammograms were used and they were classified by radiologists into three groups: those with microcalcifications (n=15), those with tumours (n=15), and those with no lesions (n=10). Five mammograms were taken as training data sets from each of the groups with microcalcifications and tumours. The algorithm was then applied to data not taken for training. The algorithm achieved a mean accuracy of 99% compared with the findings of the radiologists.

Rough neural intelligent approach for image classification: A case of patients with suspected breast cancer

The objective of this paper is to introduce a rough neural intelligent approach for rule generation and image classification. Hybridization of intelligent computing techniques has been applied to see their ability and accuracy to classify breast cancer images into two outcomes: malignant cancer or benign cancer. Algorithms based on fuzzy image processing are first applied to enhance the contrast of the whole original image; to extract the region of interest and to enhance the edges surrounding that region. Then, we extract features characterizing the underlying texture of the regions of interest by using the gray-level co-occurrence matrix. Then, the rough set approach to attribute reduction and rule generation is presented. Finally, rough neural network is designed for discrimination of different regions of interest to test whether they represent malignant cancer or benign cancer. Rough neural network is built from rough neurons, each of which can be viewed as a pair of sub-neurons, corresponding to the lower and upper bounds. To evaluate performance of the presented rough neural approach, we run tests over different mammogram images. The experimental results show that the overall classification accuracy offered by rough neural approach is high compared with other intelligent techniques.

Fuzzy rough sets hybrid scheme for breast cancer detection

This paper introduces a hybrid scheme that combines the advantages of fuzzy sets and rough sets in conjunction with statistical feature extraction techniques. An application of breast cancer imaging has been chosen and hybridization scheme have been applied to see their ability and accuracy to classify the breast cancer images into two outcomes: cancer or non-cancer. The introduced scheme starts with fuzzy image processing as pre-processing techniques to enhance the contrast of the whole image; to extracts the region of interest and then to enhance the edges surrounding the region of interest. A subsequently extract features from the segmented regions of the interested regions using the gray-level co-occurrence matrix is presented. Rough sets approach for generation of all reducts that contains minimal number of attributes and rules is introduced. Finally, these rules can then be passed to a classifier for discrimination for different regions of interest to test whether they are cancer or non-cancer. To measure the similarity, a new rough set distance function is presented. The experimental results show that the hybrid scheme applied in this study perform well reaching over 98% in overall accuracy with minimal number of generated rules. (This paper was not presented at any IFAC meeting).

Characterization of trabecular bone structure from high-resolution magnetic resonance images using fuzzy logic

The purpose of this work was to apply fuzzy logic image processing techniques to characterize the trabecular bone structure with high-resolution magnetic resonance images. Fifteen ex vivo high-resolution magnetic resonance images of specimens of human radii at 1.5 T and 12 in vivo high-resolution magnetic resonance images of the calcanei of peri- and postmenopausal women at 3 T were obtained. Soft segmentation using fuzzy clustering was applied to MR data to obtain fuzzy bone volume fraction maps, which were then analyzed with three-dimensional (3D) fuzzy geometrical parameters and measures of fuzziness. Geometrical parameters included fuzzy perimeter and fuzzy compactness, while measures of fuzziness included linear index of fuzziness, quadratic index of fuzziness, logarithmic fuzzy entropy, and exponential fuzzy entropy. Fuzzy parameters were validated at 1.5 T with 3D structural parameters computed from microcomputed tomography images, which allow the observation of true trabecular bone structure and with apparent MR structural indexes at 1.5 T and 3 T. The validation was statistically performed with the Pearson correlation coefficient as well as with the Bland-Altman method. Bone volume fraction correlation values (r) were up to .99 (P<.001) with good agreements based on Bland-Altman analysis showing that fuzzy clustering is a valid technique to quantify this parameter. Measures of fuzziness also showed consistent correlations to trabecular number parameters (r>.85; P<.001) and good agreements based on Bland-Altman analysis, suggesting that the level of fuzziness in high-resolution magnetic resonance images could be related to the trabecular bone structure.