Fuzzy C-Means Clustering Based on Improved Marked Watershed Transformation

Currently, the fuzzy c-means algorithm plays a certain role in remote sensing image classification. However, it is easy to fall into local optimal solution, which leads to poor classification. In order to improve the accuracy of classification, this paper, based on the improved marked watershed segmentation, puts forward a fuzzy c-means clustering optimization algorithm. Because the watershed segmentation and fuzzy c-means clustering are sensitive to the noise of the image, this paper uses the adaptive median filtering algorithm to eliminate the noise information. During this process, the classification numbers and initial cluster centers of fuzzy c-means are determined by the result of the fuzzy similar relation clustering. Through a series of comparative simulation experiments, the results show that the method proposed in this paper is more accurate than the ISODATA method, and it is a feasible training method

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

Analisis Komputasi pada Segmentasi Citra Medis Adaptif Berbasis Logika Fuzzy Teroptimasi

Abstract The objective of this research is to analyze the computation of medical image adaptive segmentation based on optimized fuzzy logic. The success of the image analysis system depends on the quality of the segmentation. The image segmentation is separating the image into regions that are meaningful for a given purpose. In this research, the Fuzzy C-Means (FCM) algorithm with spatial information is presented to segment Magnetic Resonance Imaging (MRI) medical images. The FCM clustering utilizes the distance between pixels and cluster centers in the spectral domain to compute the membership function. The pixels of an object in image are highly correlated, and this spatial information is an important characteristic that can be used to aid their labeling. This scheme greatly reduces the effect of noise. The FCM method successfully classifies the brain MRI images into five clusters. This technique is therefore a powerful method in computation for noisy image segmentation. Keywords: computation analysis, MRI Medical image, adaptive image segmentation, fuzzy c-mean

Analisis Komputasi pada Segmentasi Citra Medis Adaptif Berbasis Logika Fuzzy Teroptimasi

The objective of this research is to analyze the computation of medical image adaptive segmentation based on optimized fuzzy logic. The success of the image analysis system depends on the quality of the segmentation. The image segmentation is separating the image into regions that are meaningful for a given purpose. In this research, the Fuzzy C-Means (FCM) algorithm with spatial information is presented to segment Magnetic Resonance Imaging (MRI) medical images. The FCM clustering utilizes the distance between pixels and cluster centers in the spectral domain to compute the membership function. The pixels of an object in image are highly correlated, and this spatial information is an important characteristic that can be used to aid their labeling. This scheme greatly reduces the effect of noise. The FCM method successfully classifies the brain MRI images into five clusters. This technique is therefore a powerful method in computationfor noisy image segmentation. Keywords: computation analysis, MRI Medical image, adaptive image segmentation, fuzzy cmean

Detection of brain tumour in 2d MRI: implementation and critical review of clustering-based image segmentation methods

Image segmentation can be defined as segregation or partitioning of images into multiple regions with the same predefined homogeneity criterion. Image segmentation is a crucial process in medical image analysis. This paper explores and investigates several unsupervised image segmentation approaches and their viability and performances in delineating tumour region in contrast enhanced T1-weighted brain MRI (Magnetic Resonance Imaging) scans. First and foremost, raw CE T1-weighted brain MR images are downloaded from a free online database. The images are then pre-processed and undergo an important process called skull stripping. Then, image segmentation techniques such as k-means clustering, Gaussian mixture model segmentation and fuzzy c-means are applied to the pre-processed MR images. The image segmentation results are evaluated using several performance measures, such as precision, recall, Tanimoto coefficient and Dice similarity index in reference to ground truth images. The highest average Dice coefficient is achieved by k-means (0.189) before post-processing and GMM (0.208) after post-processing. Unsupervised clustering-based brain tumour segmentation based on just image pixel intensity in single-spectral brain MRI without adaptive post-processing algorithm cannot achieve efficient and robust segmentation results

Colour map image segmentation based on supervised and unsupervised learning techniques

Image segmentation is a very important stage in any image analysis or computer vision system. Map images are considered to be among the most complex of images. The segmentation of colour map images is a difficult problem. In this thesis, four segmentation techniques are presented to extract characters and lines from colour geographic map images. There are: conventional adaptive thresholding, the supervised-learning neural network, the unsupervised fuzzy c—means clustering and nearest-prototype rule, and the combined supervised and unsupervised techniques. In the conventional adaptive thresholding technique, images are divided into subimages. For each bimodal histogram subimage, a threshold is located at the valley of the histogram using an automated histogram analysis technique. A threshold value is obtained for each pixel of the image by interpolation of the thresholds. The image is then segmented by the different thresholds at each pixel. In the supervised-learning neural network based technique, a neural network is first trained with feature values using known character and line pixels and background pixels, and is then used for classification. The image segmentation problem is treated as a pattern classification process and the neural network classifier is used to generate non—linear decision regions to separate the foreground and background of an image that containing a number of nonuniform regions with different colours. In the unsupervised fuzzy clustering and nearest-prototype rule based technique, segmentation is also considered as a process of pixel classification. A set of prototypes is generated using the fuzzy c—means clustering algorithm on the training areas selected from different colour map images, and each pixel of the image is classified into character and line class or background class according to the nearest—prototype rule. In the combined supervised and unsupervised technique, training samples are generated by the unsupervised fuzzy clustering technique applied to subimages and by randomly choosing pixels in the low contrast areas. A supervised learning based multi-layer neural network is trained for classifying character and line pixels and background pixels. These four techniques are applied to many colour geographic map images containing English, Japanese and Chinese characters with different printing styles. The conventional adaptive threshold technique does not work well. The proposed supervised and unsupervised techniques have achieved satisfactory segmentation results although some very low contrast areas require improvement in the unsupervised technique. The combined technique is a way of enchancing the performance of the supervised technique, and it has yielded good segmentation results

Performance characterization of clustering algorithms for colour image segmentation

This paper details the implementation of three traditional clustering techniques (K-Means clustering, Fuzzy C-Means clustering and Adaptive K-Means clustering) that are applied to extract the colour information that is used in the image segmentation process. The aim of this paper is to evaluate the performance of the analysed colour clustering techniques for the extraction of optimal features from colour spaces and investigate which method returns the most consistent results when applied on a large suite of mosaic images

Semiautomatic epicardial fat segmentation based on fuzzy c-means clustering and geometric ellipse fitting

Automatic segmentation of particular heart parts plays an important role in recognition tasks, which is utilized for diagnosis and treatment. One particularly important application is segmentation of epicardial fat (surrounds the heart), which is shown by various studies to indicate risk level for developing various cardiovascular diseases as well as to predict progression of certain diseases. Quantification of epicardial fat from CT images requires advance image segmentation methods. The problem of the state-of-the-art methods for epicardial fat segmentation is their high dependency on user interaction, resulting in low reproducibility of studies and time-consuming analysis. We propose in this paper a novel semiautomatic approach for segmentation and quantification of epicardial fat from 3D CT images. Our method is a semisupervised slice-by-slice segmentation approach based on local adaptive morphology and fuzzy c-means clustering. Additionally, we use a geometric ellipse prior to filter out undesired parts of the target cluster. The validation of the proposed methodology shows good correspondence between the segmentation results and the manual segmentation performed by physicians