Retinal blood vessel localization to expedite PDR diagnosis

Ophthalmologist relies on the retinal fundus image segmentation for accurate diagnosis of Diabetic Retinopathy caused due to prolonged deterioration in retinal blood vessels. Blood vessel and optical disc localization determines the vascular alterations helpful in identifying retinal diseases with accurate identification of pathologies like microaneurysms and exudates. This work comprises evaluation of proposed Optical Disc Segmentation and blood vessel localization techniques followed by a statistical analysis using three fractal dimensions; box count, information and correlation. Fractal dimensions explored are beneficial for Proliferative Diabetic Retinopathy (PDR) diagnosis as its value for vascular structures increases with increasing level of PDR. Two benchmark fundus image databases, DRIVE and STARE were evaluated by utilizing shape and fractal features for performance validation and average accuracies of 96.79% and 95.68% were achieved for extracted blood vessels using proposed approach

Automatic Blood Vessel Extraction of Fundus Images Employing Fuzzy Approach

Diabetic Retinopathy is a retinal vascular disease that is characterized by progressive deterioration of blood vessels in the retina and is distinguished by the appearance of different types of clinical lesions like microaneurysms, hemorrhages, exudates etc. Automated detection of the lesions plays significant role for early diagnosis by enabling medication for the treatment of severe eye diseases preventing visual loss. Extraction of blood vessels can facilitate ophthalmic services by automating computer aided screening of fundus images. This paper presents blood vessel extraction algorithms with ensemble of pre-processing and post-processing steps which enhance the image quality for better analysis of retinal images for automated detection. Extensive performance based evaluation of the proposed approaches is done over four databases on the basis of statistical parameters. Comparison of both blood vessel extraction techniques on different databases reveals that fuzzy based approach gives better results as compared to Kirsch’s based algorithm. The results obtained from this study reveal that 89% average accuracy is offered by the proposed MBVEKA and 98% for proposed BVEFA

Edge Detection in Remote Sensing Images Based on Fuzzy Image Representation

Edge detection is an important task in image processing. There are a lot of approaches in this area: Sobel, Canny operators and others. One of the perspective techniques in image processing is the use of fuzzy logic and fuzzy sets theory. They allow us to increase processing quality by representing information in its fuzzy form. Most of the existing fuzzy image processing methods switch to fuzzy sets on very late stages, so this leads to some useful information loss. In this paper a novel method of edge detection based on fuzzy image representation and fuzzy pixels is proposed. With this approach we convert the image to fuzzy form on the first step. Different approaches to this conversion are described. Several membership functions for fuzzy pixel description and requirements for their form and view are given. A novel approach to edge detection based on Sobel operator and fuzzy image representation is proposed. Experimental testing of developed method was performed on remote sensing images. Comparison of result with Sobel, Prewitt, Roberts and Canny operators is presented. Developed method selected more details (edges) rather then Sobel, Prewitt and Roberts operators, but less than Canny operator. This is because the selected convolution kernel (Sobel) has size 3x3. There are also used only simple functions of estimating the real intensities of pixels. Later, to increase quality it is necessary to use more complex masks of size 5x5 and 7x7 or median filters. Developed approach showed its workability in solving imagep rocessing problems. The proposed fuzzy model in the future can be extended to use higher level fuzzy sets (Type-2FS and others)

A Framework for Optimum Contour Detection

The importance of contour detection have been acknowledged by researchers worldwide, and indeed dozens of methods have been introduced. However there is no single method suit with various conditions of digital images. Most of the time, a tedious work to select best method from dozens is required only to derive the most appropriate objects contour from a digital image. Once an object contour is recognized, further image analysis process can be computed efficiently. This condition is in contrast with human visual perception which employs contour detection as a preliminary process with minimal energy consumption before conducting exhaustive visual analysis. Therefore this research aims to develop a framework to automatically detecting optimum object contour by selecting the best method for each condition of input image. Efficient energy consumption will be achieved by applying mechanism based on multi criteria decision making

Fuzzy Logic Based Hardware Accelerator with Partially Reconfigurable Defuzzification Stage for Image Edge Detection

In this paper, the design and the implementation of a pipelined hardware accelerator based on a fuzzy logic approach for an edge detection system are presented. The fuzzy system comprises a preprocessing stage, a fuzzifier with four fuzzy inputs, an inference system with seven rules, and a defuzzification stage delivering a single crisp output, which represents the intensity value of a pixel in the output image. The hardware accelerator consists of seven stages with one clock cycle latency per stage. The defuzzification stage was implemented using three different defuzzification methods. These methods are the mean of maxima, the smallest of maxima, and the largest of maxima. The defuzzification modules are interchangeable while the system runs using partial reconfiguration design methodology. System development was carried out using Vivado High-Level Synthesis, Vivado Design Suite, Vivado Simulator, and a set of Xilinx 7000 FPGA devices. Depending upon the speed grade of the device that is employed, the system can operate at a frequency range from 83 MHz to 125 MHz. Its peak performance is up to 58 high definition frames per second. A comparison of this system’s performance and its software counterpart shows a significant speedup in the magnitude of hundred thousand times

Objective measurement for edge and line oriented contour detection

The importance of contour detection has been acknowledged by researchers worldwide, and indeed dozens of methods have been introduced. However there is no single method suit with various conditions of digital images. Most of the time, a tedious work to select best method from dozens is required only to derive the most appropriate objects contour from a digital image. Once an object contour is recognized, further image analysis process can be computed efficiently. This condition is in contrast with human visual perception which employs contour detection as a preliminary process with minimal energy consumption before conducting exhaustive visual analysis. Therefore this research aims to develop a framework to automatically detecting optimum object contour by selecting the best method for each condition of input image. Efficient energy consumption will be achieved by applying mechanism based on multi criteria decision making. Experimental result achieves 76.47% accuracy for detecting object composing a set of digital images

Study and Development of Some Novel Image Segmentation Techniques

Some fuzzy technique based segmentation methods are studied and implemented and some fuzzy c means clustering based segmentation algorithms are developed in this thesis to suppress high and low uniform random noise. The reason for not developing fuzzy rule based segmentation method is that they are application dependent In many occasions, the images in real life are affected with noise. Fuzzy c means clustering based segmentation does not give good segmentation result under such condition. Various extension of the FCM method for segmentation are present in the literature. But most of them modify the objective function hence changing the basic FCM algorithm present in MATLAB toolboxes. Hence efforts have been made to develop FCM algorithm without modifying their objective function for better segmentation . The fuzzy technique based segmentation methods that are studied and developed are summarized here. (A) Fuzzy edge detection based segmentation: Two fuzzy edge detection methods are studied and implemented for segmentation: (i) FIS based edge detection and (ii) Fast multilevel fuzzy edge detector (FMFED). (i): The Fuzzy Inference system (FIS) based edge detector consists of some fuzzy inference rules which are defined in such a way that the FIS system output (“edges”) is high only for those pixels belonging to edges in the input image. A robustness to contrast and lightining variations were also taken into consideration while developing these rules.The output of the FIS based edge detector is then compared with the existing Sobel, LoG and Canny edge detector results. The algorithm is seen to be application dependent and time consuming. (ii) Fast Multilevel Fuzzy Edge Detector: To realise the fast and accurate detection of edges, the FMFED algorithm is proposed. It first enhances the image contrast by means of a fast multilevel fuzzy enhancement algorithm using simple transformation function based on two image thresholds. Second, the edges are extracted from the enhanced image by using a two stage edge detector operator that identifies the edge candidates based on local characteristics of the image and then determines the true edge pixels using edge detector operator based on extremum of the gradient values. Finally the segmentation of the edge image is done by morphological operator by edge linking. (B) FCM based segmentation: Two fuzzy clustering based segmentation methods are developed: (i) Modified Spatial Fuzzy c-Means (MSFCM) (ii) Neighbourhood Attraction Fuzzy c-Means (NAFCM). . (i) Contrast-Limited Adaptive Histogram Equalization Fuzzy c-Means (CLAHEFCM): This proposed algorithm presents a color segmentation process for low contrast images or unevenly illuminated images. The algorithm presented in this paper first enhances the contrast of the image by using contrast limited adaptive histogram equalization. After the enhancement of the image this method divides the color space into a given number of clusters, the number of cluster are fixed initially. The image is converted from RGB color space to LAB color space before the clustering process. Clustering is done here by using Fuzzy c means algorithm. The image is segmented based on color of a region, that is, areas having same color are grouped together. The image segmentation is done by taking into consideration, to which cluster a given pixel belongs the most. The method has been applied on a number of color test images and it is observed to give good segmentation results (ii) Modified Spatial Fuzzy c-means (MSFCM): The proposed algorithm divides the color space into a given number of clusters, the number of cluster are fixed initially. The image is converted from RGB color space to LAB color space before the clustering process. A robust segmentation technique based on extension to the traditional fuzzy c-means (FCM) clustering algorithm is proposed. The spatial information of each pixel in an image has been taken into consideration to get a noise free segmentation result. The image is segmented based on color of a region, that is, areas having same color are grouped together. The image segmentation is done by taking into consideration, to which cluster a given pixel belongs the most. The method has been applied to some color test images and its performance has been compared to FCM and FCM based methods to show its superiority over them. The proposed technique is observed to be an efficient and easy method for segmentation of noisy images. (iv) Neighbourhood Attraction Fuzzy c Means Algorithm: A new algorithm based on the IFCM neighbourhood attraction is used without changing the distance function of the FCM and hence avoiding an extra neural network optimization step for the adjusting parameters of the distance function, it is called Neighborhood Atrraction FCM (NAFCM). During clustering, each pixel attempts to attract its neighbouring pixels towards its own cluster. This neighbourhood attraction depends on two factors: the pixel intensities or feature attraction, and the spatial position of the neighbours or distance attraction, which also depends on neighbourhood structure. The NAFCM algorithm is tested on a synthetic image (chapter 6, figure 6.3-6.6) and a number of skin tumor images. It is observed to produce excellent clustering result under high noise condition when compared with the other FCM based clustering methods