A Comparison of Fuzzy Clustering Algorithms Applied to Feature Extraction on Vineyard

Image segmentation is a process by which an image is partitioned into regions with similar features. Many approaches have been proposed for color image segmentation, but Fuzzy C-Means has been widely used, because it has a good performance in a large class of images. However, it is not adequate for noisy images and it also takes more time for execution as compared to other method as K-means. For this reason, several methods have been proposed to improve these weaknesses. Method like Possibilistic C-Means, Fuzzy Possibilistic C-Means, Robust Fuzzy Possibilistic C-Means and Fuzzy C-Means with Gustafson-Kessel algorithm. In this paper we perform a comparison of these clustering algorithms applied to feature extraction on vineyard images. Segmented images are evaluated using several quality parameters such as the rate of correctly classied area and runtim

Noise-robust method for image segmentation

Segmentation of noisy images is one of the most challenging problems in image analysis and any improvement of segmentation methods can highly influence the performance of many image processing applications. In automated image segmentation, the fuzzy c-means (FCM) clustering has been widely used because of its ability to model uncertainty within the data, applicability to multi-modal data and fairly robust behaviour. However, the standard FCM algorithm does not consider any information about the spatial linage context and is highly sensitive to noise and other imaging artefacts. Considering above mentioned problems, we developed a new FCM-based approach for the noise-robust fuzzy clustering and we present it in this paper. In this new iterative algorithm we incorporated both spatial and feature space information into the similarity measure and the membership function. We considered that spatial information depends on the relative location and features of the neighbouring pixels. The performance of the proposed algorithm is tested on synthetic image with different noise levels and real images. Experimental quantitative and qualitative segmentation results show that our method efficiently preserves the homogeneity of the regions and is more robust to noise than other FCM-based methods

Intercomparison of medical image segmentation algorithms

Magnetic Resonance Imaging (MRI) is one of the most widely-used high quality imaging techniques, especially for brain imaging, compared to other techniques such as computed tomography and x-rays, mainly because it possesses better soft tissue contrast resolution. There are several stages involved in analyzing an MRI image, segmentation being one of the most important. Image segmentation is essentially the process of identifying and classifying the constituent parts of an image, and is usually very complex. Unfortunately, it suffers from artefacts including noise, partial volume effects and intensity inhomogeneities. Brain, being a very complicated structure, its precise segmentation is particularly necessary to delineate the borders of anatomically distinct regions and possible tumors. Many algorithms have been proposed for image segmentation, the most important being thresholding, region growing, and clustering methods such as k-means and fuzzy c-means algorithms. The main objective of this project was to investigate a representative number of different algorithms and compare their performance. Image segmentation algorithms, including thresholding, region growing, morphological operations and fuzzy c-means were applied to a selection of simulated and real brain MRI images, and the results compared. The project was realized by developing algorithms using the popular Matlab® software package. Qualitative comparisons were performed on real and simulated brain images, while quantitative comparisons were performed on simulated brain images, using a variety of different parameters, and results tabulated. It was found that the fuzzy c-means algorithm performed better than all the other algorithms, both qualitatively and quantitatively. After comparing the performance of all algorithms, it was concluded that, by combining one or two basic algorithms, a more effective algorithm could be developed for image segmentation that is more robust to noise, considers both intensity and spatial characteristics of an image, and which is computationally efficient.Magnetic Resonance Imaging (MRI) is one of the most widely-used high quality imaging techniques, especially for brain imaging, compared to other techniques such as computed tomography and x-rays, mainly because it possesses better soft tissue contrast resolution. There are several stages involved in analyzing an MRI image, segmentation being one of the most important. Image segmentation is essentially the process of identifying and classifying the constituent parts of an image, and is usually very complex. Unfortunately, it suffers from artefacts including noise, partial volume effects and intensity inhomogeneities. Brain, being a very complicated structure, its precise segmentation is particularly necessary to delineate the borders of anatomically distinct regions and possible tumors. Many algorithms have been proposed for image segmentation, the most important being thresholding, region growing, and clustering methods such as k-means and fuzzy c-means algorithms. The main objective of this project was to investigate a representative number of different algorithms and compare their performance. Image segmentation algorithms, including thresholding, region growing, morphological operations and fuzzy c-means were applied to a selection of simulated and real brain MRI images, and the results compared. The project was realized by developing algorithms using the popular Matlab® software package. Qualitative comparisons were performed on real and simulated brain images, while quantitative comparisons were performed on simulated brain images, using a variety of different parameters, and results tabulated. It was found that the fuzzy c-means algorithm performed better than all the other algorithms, both qualitatively and quantitatively. After comparing the performance of all algorithms, it was concluded that, by combining one or two basic algorithms, a more effective algorithm could be developed for image segmentation that is more robust to noise, considers both intensity and spatial characteristics of an image, and which is computationally efficient

Row Crop's Identication Through Hough Transform using Images Segmented by Robust Fuzzy Possibilistic C-Means

The Hough transform (HT) is a widely used method for line detection and recognition, due to its robustness. But its performance is strongly dependent on the applied segmentation technique. On the other hand, Fuzzy C-Means (FCM) has been widely used in image segmentation because it has a good performance in a large class of images. However, it is not good for noisy images, so that to overcome this weakness several modications to FCM have been proposed, like Robust Fuzzy Possibilistic C-Means (RFPCM). In this paper, we propose to use the RFPCM algorithm for the segmentation of crops images in order to apply the HT to detect lines in row crops for navigation purposes. The proposed method gives better results compared with techniques based on visible spectral-index or Specic threshold-based approaches

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

Brain Tumor Segmentation on MRI Brain Images with Fuzzy Clustering and GVF Snake Model

Deformable or snake models are extensively used for medical image segmentation, particularly to locate tumor boundaries in brain tumor MRI images. Problems associated with initialization and poor convergence to boundary concavities, however, has limited their usefulness. As result of that they tend to be attracted towards wrong image features. In this paper, we propose a method that combine region based fuzzy clustering called Enhanced Possibilistic Fuzzy C-Means (EPFCM) and Gradient vector flow (GVF) snake model for segmenting tumor region on MRI images. Region based fuzzy clustering is used for initial segmentation of tumor then result of this is used to provide initial contour for GVF snake model, which then determines the final contour for exact tumor boundary for final segmentation. The evaluation result with tumor MRI images shows that our method is more accurate and robust for brain tumor segmentation

An Automatic Statistical Segmentation Algorithm for Extraction of Fire and Smoke Regions

Estimation of the extent and spread of wildland fires is an important application of high spatial resolution multispectral images. This work addresses a fuzzy segmentation algorithm to map fire extent, active fire front, hot burn scar, and smoke regions based on a statistical model. The fuzzy results are useful data sources for integrated fire behavior and propagation models built using Dynamic Data Driven Applications Systems (DDDAS) concepts that use data assimilation techniques which require error estimates or probabilities for the data parameters. The Hidden Markov Random Field (HMRF) model has been used widely in image segmentation, but it is assumed that each pixel has a particular class label belonging to a prescribed finite set. The mixed pixel problem can be addressed by modeling the fuzzy membership process as a continuous Multivariate Gaussian Markov Random Field. Techniques for estimating the class membership and model parameters are discussed. Experimental results obtained by applying this technique to two Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) images show that the proposed methodology is robust with regard to noise and variation in fire characteristics as well as background. The segmentation results of our algorithm are compared with the results of a K-means algorithm, an Expectation Maximization (EM) algorithm (which is very similar to the Fuzzy C-Means Clustering algorithm with entropy regularization), and an MRF-MAP algorithm. Our fuzzy algorithm achieves more consistent segmentation results than the comparison algorithms for these test images with the added advantage of simultaneously providing a proportion or error map needed for the data assimilation problem

Image Segmentation by Fuzzy C-Means Clustering Algorithm with a Novel Penalty Term

To overcome the noise sensitiveness of conventional fuzzy c-means (FCM) clustering algorithm, a novel extended FCM algorithm for image segmentation is presented in this paper. The algorithm is developed by modifying the objective function of the standard FCM algorithm with a penalty term that takes into account the influence of the neighboring pixels on the centre pixels. The penalty term acts as a regularizer in this algorithm, which is inspired from the neighborhood expectation maximization algorithm and is modified in order to satisfy the criterion of the FCM algorithm. The performance of our algorithm is discussed and compared to those of many derivatives of FCM algorithm. Experimental results on segmentation of synthetic and real images demonstrate that the proposed algorithm is effective and robust