Yogita K. Dubey

Segmentation of brain MR images using intuitionistic fuzzy clustering algorithm

A new algorithm for the segmentation of brain MR images, using intuitionistic fuzzy clustering (IFCM), is proposed in this paper. The algorithm uses intuitionistic fuzzy representation of image to deal with variations in pixel intensities of brain MR images. The proposed intuitionistic fuzzy clustering algorithms segments brain MR image into three regions, gray matter (GM), white Matter (WM) cerebrospinal fluid (CSF). To evaluate the performance of the proposed method, segmentations results are compared on the basis of segmentation accuracy and computational time with bias corrected fuzzy clustering method (BCFCM). The quantitative evaluation demonstrates the superiority of the proposed algorithm.

Left Ventricular Segmentation of 2-D Echocardiographic Image: A Survey

Cardiovascular diseases are a major health concern worldwide. The left ventricle and in particular the endocardium is a structure of particular interest since it performs the task of pumping oxygenated blood to the entire body. Therefore, segmentation of the left ventricle in echocardiography images is a task with important diagnostic power. More concretely, contour extraction is an important criterion for subjective evaluation of the cardiac function and has become an area of focus. Cardiac function is evaluated quantitatively using echocardiography via the analysis of shape attributes, such as the heart wall thickness or the shape change of the heart wall boundaries. This requires that the complete boundaries of the heart wall be detected from a sequence of two-dimensional ultrasonic images of the heart. The image segmentation process is made difficult since these images are plagued by poor intensity contrast and dropouts caused by the intrinsic limitations of the image formation process. Current studies often require the tedious and time-consuming practice of having trained operators manually trace the heart walls. This paper reviews Left ventricular segmentation methods of 2-D echocardiographic image, in a broad sense.

FCM Clustering Algorithms for Segmentation of Brain MR Images

The study of brain disorders requires accurate tissue segmentation of magnetic resonance MR brain images which is very important for detecting tumors, edema, and necrotic tissues. Segmentation of brain images, especially into three main tissue types: Cerebrospinal Fluid CSF, Gray Matter GM, and White Matter WM, has important role in computer aided neurosurgery and diagnosis. Brain images mostly contain noise, intensity inhomogeneity, and weak boundaries. Therefore, accurate segmentation of brain images is still a challenging area of research. This paper presents a review of fuzzy c-means FCM clustering algorithms for the segmentation of brain MR images. The review covers the detailed analysis of FCM based algorithms with intensity inhomogeneity correction and noise robustness. Different methods for the modification of standard fuzzy objective function with updating of membership and cluster centroid are also discussed.

Texture Classification Using Cosine-modulated Wavelets

This paper proposes a technique for image texture classification based on cosine-modulated wavelet transform. Better discriminability and low implementation cost of the cosine-modulated wavelets has been effectively utilized to yield better features and more accurate classification results. Experimental results demonstrate the effectiveness of this approach on different datasets in three experiments. The proposed approach improves classification rates compared to the traditional Gabor wavelet based approach, rotated wavelet filters based approach, DT-CWT approach and the DLBP approach. The computational cost of the proposed method is less as compared to the other two methods.

Color Image Segmentation Using Kernalized Fuzzy C-means Clustering

The fuzzy c-means (FCM) algorithm is a very popular algorithm in the field of image segmentation because of its simplicity and less sensitivity to noise and it is widely used in the field of engineering disciplines. The FCM membership function can handle the overlapped clusters efficiently with predefined number of clusters, but this algorithm are unable to cluster non-linearly separable data as well as choosing of initial cluster centre is difficult task which results in poor image segmentation. To overcome this drawback, we proposed Kernalized Fuzzy C-means (KFCM) clustering. In that kernel space clustering is used for clustering of nonlinear image, which have kernel functions which transform data in image plane into higher dimension feature space and these kernel functions are used to find non-Euclidean distance between feature point without defining transfer function, and then perform FCM in feature space. Here we use two different kernel functions for image segmentation and compare their outputs.

Intuitionistic fuzzy roughness measure for segmentation of brain MR images

A multilevel thresholding method for the segmentation of Magnetic Resonance (MR) brain images using the concept of intuitionistic fuzzy and rough set is presented here. Intuitionistic fuzzy roughness measure, calculated by considering histogram as lower approximation of rough set and intuitionistic fuzzy histon as upper approximation of rough set, is used to find optimum valley points for segmentation of brain MR images. A new fuzzy complement function is proposed for intuitionistic fuzzy image representation to take into account intensity inhomogeneity and noise in brain MR images. The proposed algorithm segments brain MR image into three regions, gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF). The quantitative evaluation demonstrate the superiority of the proposed algorithm.

Classification of MRI Brain Images Using Cosine-Modulated Wavelets

This paper presents technique for the classification of the MRI images of human brain using cosine modulated wavelet transform. Better discrimination and low design implementation complexity of the cosine-modulated wavelets has been effectively utilized to give better features and more accurate classification results. The proposed technique consists of two stages, namely, feature extraction, and classification. In the first stage, the energy features from MRI images are obtained from sub-band images obtained after decomposition using cosine modulated wavelet transform. In the classification stage, Bays classifier is used to classify the image as normal or abnormal. Average Classification accuracy with a success rate of 100% has been obtained.

Segmentation of Left Ventricle of 2D Echocardiographic Image Using Active Contouring

Cardiovascular diseases are a major health concern worldwide. The left ventricle and in particular the endocardium is a structure of particular interest since it performs the task of pumping oxygenated blood to the entire body. Therefore, segmentation of the left ventricle in echocardiographic images is a task with important diagnostic power. Cardiac function is evaluated quantitatively using echocardiography via the analysis of shape attributes, such as the heart wall thickness or the shape change of the heart wall boundaries. This requires that the complete boundaries of the heart wall be detected from a sequence of two-dimensional ultrasonic images of the heart. The image segmentation process is made difficult since these images are plagued by poor intensity contrast and dropouts caused by the intrinsic limitations of the image formation process. This paper describes a method for obtaining the border of the hearts left ventricle which is useful in the detection of certain heart parameters. The limitation of speckle noise is overcome by smoothening the image using Gaussian filtering.

Multiscale intuitionistic fuzzy roughness measure for image segmentation

In this paper, a method for image segmentation using multiscale intuitionistic fuzzy roughness measure is proposed. The traditional roughness measure tends to over-focus on the little important homogeneous regions but is not accurate enough to measure the homogeneity in an image. By applying the theories of scale-space and using intuitionistic fuzzy representation for images, roughness is measured under multiple scales. Multiscale representation can tolerate the disturbance of trivial regions, and intuitionistic fuzzy representation deals with hesitancy in image boundary, therefore produces precise segmentation results.

Multiscale Intuitionistic Fuzzy Roughness Measure for Color Image Segmentation

In this paper, a method for color image segmentation using multiscale intuitionistic fuzzy roughness measure is proposed. The traditional roughness measure tends to over focus on the little important homogeneous regions but is not accurate enough to measure the homogeneity in an image. By applying the theories of scale space and using intuitionistic fuzzy representation for images, roughness is measured under multiple scales. Multiscale representation can tolerate the disturbance of trivial regions, and intuitionistic fuzzy representation deals with hesitancy in image boundary, therefore produces precise segmentation results.