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In this paper, a new medical image segmentation algorithm based on Skew Gaussian distribution is proposed. In brain images, it is necessary to classify the brain voxels into one of the 3 main tissues mainly Gray matter (GM), White matter (WM) and Cerebro Spiral fluid (CSF). Quantization of Gray & White matter is a topic of concern in neuro-degenerative disorders. Viz., Alzheimer disease and Parkinson's...
In this study, a fuzzy clustering method has been proposed in order to segment brain tissues affected by the multiple sclerosis (MS). In traditional fuzzy clustering, the neighboring relations between pixels have not been taken account of. Additionally, the performance of the clustering reduces drastically because of the pixels having close gray levels due to noise. Therefore, in this study, a novel...
In order to quickly and accurately detect microaneurysms of diabetic retinopathy images, a specific method is proposed in this paper, which eliminates useless information based on overall threshold, computes the set of overall connection domain, and precisely orientates microaneurysms by local threshold segmentation and local connection domain identification. Experimental results testify the good...
For lung tissue adhesion of the situation in the lung CT images, based on the classical watershed algorithm, this introduce line-encoded ideology, use contour tracing method to determine adhesion region segmentation points, according to segment code to get the split line, thus separating the adhesion region. Experimental results show that this method will be effective in the lung adhesion region segmentation,...
We present a new frame of lung parenchyma segmentation. Optimal threshold value method and the boundary tracking method are used to get rid of the background interference and segment the lung region. Then new algorithm is used for lung region boundary repairing based on the mathematical morphology method. The experimental results show the new algorithm can segment lung regions from the chest CT images...
In this paper, a new algorithm is proposed to segment medical images to extract the contour of the objects. First, do edge segmentation to the image region with Prewitt operator. And then use Hough transformation to detect the incontinuous points in curve and link the edges. An accurate edge of the organ or tissue of human can be extracted through this method. Based on this, more information can be...
Image segmentation is an important branch of computer vision. Its aim is to extract meaningful lying in objects images, either by dividing images into contiguous semantic regions, or by extracting one or several objects more specific in images, such as medical structures. In general, image segmentation task is very difficult to achieve it since natural images are diverse, complex and the way we perceive...
Glaucoma is one of the main causes of blindness worldwide. Segmentation of vascular system and optic disc is an important step in the development of an automatic retinal screening system. In this paper we present an unsupervised method for the optic disc segmentation. The main obstruction in the optic disc segmentation process is the presence of blood vessels breaking the continuity of the object...
This paper present the results of applying dark stretching technique to enhance and segment the Plasmodium Falciparum based on thick blood smear images. Image enhancement is the process to improve the quality (clarity) of images for human viewing. Removing blurring and noise, increasing contrast, and revealing details are examples of enhancement operations. Reducing the noise and blurring and increasing...
Ultrasound imaging is a widely used and safe medical diagnostic technique, due to its noninvasive nature, low cost, real time imaging. However the usefulness of ultrasound imaging is degraded by the presence of signal depended noise known as speckle.. The speckle pattern depends on the structure of the image tissue and various imaging parameters. There are two main purposes for speckle reduction in...
A reliable and accurate method to measure the width of retinal blood vessel in fundus photography is proposed in this paper. Our approach is based on a graph-theoretic algorithm. The two boundaries of the same blood vessel are segmented simultaneously by converting the two-boundary segmentation problem into a two-slice, three-dimension surface segmentation problem, which is further converted into...
A novel interactive segmentation method based on distance metric learning is proposed for segmentation of tumors in CT and MRI images. Firstly, the moments of the gray-level histogram are extracted as the image features for segmentation. Then, Neighborhood Components Analysis is employed to learn a task-specific distance metric in the feature space using the interactive inputs. The probability of...
Microcalcifications are present in a great number of malignant lesions, being considered as a significant sign of malignancy. However, they are detected in less than 50% of mammograms with carcinomas. Given this scenario, systems to highlight infra-clinic lesions, aiding the specialists to make diagnostics, have been studied. These Computer-aided diagnosis systems have been developed based on parameters...
This paper presents an algorithm to classify pixels in uterine cervix images into two classes, namely normal and abnormal tissues, and simultaneously select relevant features, using group sparsity. Because of the large variations in image appearance due to changes of illumination, specular reflections and other visual noise, the two classes have a strong overlap in feature space, whether features...
This paper presents ongoing work towards creating a framework for the active segmentation and classification of cell assay images. In this paper we focus on the learning of a probabilistic boundary model followed by an extended segmentation method. The abilities are demonstrated on a variety of cell images. We conclude by outlining approaches for the active segmentation of cell images.
Retinal image analysis is currently a very vivid field in biomedical image analysis. One of the most challenging tasks is the reliable automatic detection of microaneurysms (MAs). Computer systems that aid the automatic detection of diabetic retinopathy (DR) greatly rely on MA detection. In this paper, we present a method to construct an MA score map, from which the final MAs can be extracted by simple...
Filamentary structures extraction in medical and biological images is a challenging problem. Muscular/Neural fibers, neurites and blood arteries are some examples. Their delineation is particularly problematic due to the lack of solid visual support that is also compromised by the presence of clutter and low signal to noise ratios. In this article, we propose a modular approach to curvilinear structures...
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