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The standard fuzzy C-means (FCM) algorithm does not fully utilize the spatial information for image segmentation and is sensitive to noise especially in the presence of intensity inhomogeneity in magnetic resonance imaging (MRI) images. The underlying reason is that a single fuzzy membership function in FCM algorithm cannot properly represent pattern associations to all clusters. In this paper, we...
Documents can be a valuable source of information but often they suffer degradation problems, especially in the case of historical documents, such as strains, background of big variations and uneven illumination, ink seepage, etc. Binarization techniques should be applied to remove the noise and improve the quality of the documents. Collections of historical and old document images care commonly provided...
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...
The brain Magnetic Resonance (MR) image has an embedded bias field. This field need to be corrected to obtain the actual MR image for classification. In this paper, we have proposed three new schemes to simultaneously estimate the bias field and obtain segmentation. These algorithms are modification of Ahmed et al.'s [4] Bias Corrected FCM (BCFCM) algorithm. The first proposed scheme considers the...
This paper addresses the issue of magnetic resonance (MR) Image reconstruction at compressive sampling (or compressed sensing) paradigm followed by its segmentation. To improve image reconstruction problem at low measurement space, weighted linear prediction and random noise injection at unobserved space are done first, followed by spatial domain de-noising through adaptive recursive filtering. Reconstructed...
Image edge detection is sensitive to noise which is contained by natural images so that it affects the quality of the image segmentation. In order to remove noise and improve edge detection accuracy, then improving the quality of image segmentation, a novel image segmentation algorithm via neighborhood the principal component analysis and Laplace operator is proposed. The feature vectors of each pixel...
Moving objects often contain almost important information for surveillance videos, traffic monitoring, human motion capture etc. Background subtraction methods are widely exploited for moving object detection in videos in many applications. Moving object segmentation is the application in video processing. Segmentation helps in detecting various features of moving objects for further video/image processing...
Optical Coherence Tomography (OCT) is a noninvasive technique and depth-resolved imaging modality which is a prominent ophthalmic diagnostic tool. In this paper, an automated segmentation algorithm to detect few intra-retinal layers which are important for Edema detection present in Spectral Domain Optical Coherence Tomography (SDOCT) images is presented. An algorithm for accurate segmentation of...
In this paper, we propose the directional local mean difference level set method (DLMD-LS). Our work is focused on the segmentation for a urinary bladder lumen in a T2 weighted image taken during brachytherapy. The boundary is detected as the region where the intensity means of the areas inside and outside the zero-level contour (edge) are high. The contour is controlled such that it stops evolving...
The traditional artificial cigarette review methods are difficult to match the high-speed automatic cigarette sorting system. In this paper, a new cigarette recognition method using the uniqueness of cigarette bar codes was proposed. Firstly, a binary image filtering algorithm based on square adaptive structure element of mathematical morphology (SASEMM) was used to get several potential sub-regions,...
Echocardiography provides information about size, shape, and function of heart to create the image. Apical four-chamber echocardiography can be obtained by placing the ultrasound probe at the apex of the left ventricle. Such view enables to analyze heart abnormalities. In this regard, chamber quantification is recommended to evaluate the heart volume by defining the endocardial border of the chambers...
Computerized Tomography and Positron Emission Tomography (CT/PET) is an effective and indispensable imaging tool for the application of medical image reconstruction. The noise contained in the data measured by imaging instruments is primarily Poisson type and decreasing the noise has the potential to optimize the quality of CT/PET images. But the traditional iterative reconstruction algorithms of...
A fuzzy algorithm is presented for image segmentation of 2D gray scale images whose quality have been degraded by various kinds of noise. Traditional Fuzzy C Means (FCM) algorithm is very sensitive to noise and does not give good results. To overcome this problem, a new fuzzy c means algorithm was introduced [1] that incorporated spatial information. The spatial function is the sum of all the membership...
In iris recognition systems, iris localization is a critical step which affects the further results definitively. Most of the traditional localization methods were time consuming and sensitive to noises. To solve the problems, we propose an algorithm which adopts a momentum based level set method to locate the pupil boundary. This method hasan advantage of decreasing the effect of local optima solutions...
This paper presents left ventricle (LV) endocardial segmentation from contrast 3D echocardiography (C3DE) images. The C3DE image segmentation is a very challenging problem. Though the image quality is perceived to be improved for visual analysis, the image quality actually deteriorates for the purpose of automatic/semi-automatic analysis due to high speckle noise. To overcome the speckle noise and...
Plane fitting plays an important role in image processing and computer vision. It is challenging because of the outliers that do not follow the plane pattern. In this work, we address the problem of support-plane fitting for room floor detection from point clouds that are generated from depth image. Based on the geometric layout of data, an optimization problem is derived to estimate the support-plane...
This study proposes an approach to segment human object from a depth image based on histogram of depth values. The region of interest is first extracted based on a predefined threshold for histogram regions. A region growing process is then employed to separate multiple human bodies with the same depth interval. Our contribution is the identification of an adaptive growth threshold based on the detected...
Poly Cystic Ovary Syndrome (PCOS) is an endocrine disorder affecting many women in their reproductive age groups related with these problems of infertility, diabetes mellitus and cardiovascular disease. Diagnosis of the condition is mostly done by imaging parameters. Ultrasound imaging has become a very important technology in diagnosis of PCOS. Due to overlapping of the follicles, inherent noise...
Image mosaicking technology, as an image processing technology that can aggregate the information from a sequence of images, has been used to process large size images. In this paper, we are trying to apply the mosaicking technology to nonideal iris recognition study. The proposed algorithm composes the information from a collection of iris images, and generates a “composite” image. The experiment...
Diabetic retinopathy (DR) disease occurs due to leakage of blood and protein from small diseased vessels into retina which serves as main cause of blindness among diabetic patients. The early detection of diabetes in the retinal vessels is useful for the prevention of disease. Therefore, the accurate extraction of blood vessels from retinal images helps in diagnosis of such eye diseases. In this paper...
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