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In image processing to achieve de-noising is an important task. The main thing in image de-noising is to attain edges of image. Many algorithms and methods have been proposed recently but they have not yet achieved the desired level. Among these block matching 3D (BM3D) is the method which is considered best to remove noise as compared to previous techniques. Before BM3D technique, Non Local Means...
The Cholangiocarcinoma (CCA) is the most common in northeast Thailand that is second most common worldwide. The Periductal fibrosis (PDF) ultrasound images can be applied for the CCA surveillance system which increased patient in endemic area of Opisthorchis viverrini (OV). However, the ultrasound images are lack of contrast and a lot of speckle noise. This paper presented the enhanced algorithm of...
Iris recognition becomes an important technology in our society. Visual patterns of human iris provide rich texture information for personal identification. However, it is greatly challenging to match intra-class iris images with large variations in unconstrained environments because of noises, illumination variation, heterogeneity and so on. To track current state-of-the-art algorithms in iris recognition,...
In this paper, we propose a novel algorithm for colour iris segmentation. The algorithm may be divided into the following components: coarse iris localization, limbic boundary segmentation, pupillary boundary segmentation, eyelids fitting, reflection and shadow removal. The key contribution of the proposed algorithm is that we demonstrate the power of sparsity induced by ℓ1-norm in overcoming the...
A video surveillance system is primarily designed to track key objects, or people exhibiting suspicious behavior, as they move from one position to another and record it for possible future use. The critical parts of an object tracking algorithm are object segmentation, image clusters detection, and identification and tracking of these image clusters. The major roadblocks of the tracking algorithm...
Many word spotting strategies for the modern documents are not directly applicable to historical handwritten documents due to writing styles variety and intense degradation. In this paper, a new method that permits effective word spotting in handwritten documents is presented that relies upon document-specific local features which take into account texture information around representative key points...
This paper presents the results of the HDSRC 2014 competition on handwritten digit string recognition in challenging datasets organized in conjunction with ICFHR 2014. The general objective of this competition is to identify, evaluate and compare recent developments in Western Arabic digit string recognition with varying length. In addition, this competition introduces two new challenging datasets...
Fuzzy clustering has been extensively used in brain magnetic resonance (MR) image segmentation. However, due to the existence of noise and intensity inhomogeneity, many segmentation algorithms suffer from limited accuracy. In this paper, we propose a fuzzy clustering algorithm via enhanced spatially constraint for brain MR image segmentation. A novel spatial factor is proposed by incorporating the...
In this paper, we present a non-invasive method of counting fish in their natural habitat using automated analysis of video data. Our approach uses three modular components to preprocess, detect, and track the fish. The preprocessing reduces noise present in the image while enhancing the fish using several different techniques. The fish detection is based on two background subtraction algorithms which...
Traditionally, fish stock assessment is a time-consuming, expensive, and invasive task, since fish are caught and counted using research vessels. Therefore, in a joint project a non-invasive, acoustic-optical Underwater Fish Observatory (UFO) is proposed and developed. The UFO counts and classifies fish utilizing a stereo camera system and a sonar system in order to observe the available biomass....
We design a new fast algorithm to automatically complete closed contours in a finite point cloud on the plane. The only input can be a scanned map with almost closed curves, a hand-drawn artistic sketch or any sparse dotted image in 2D without any extra parameters. The output is a hierarchy of closed contours that have a long enough life span (persistence) in a sequence of nested neighborhoods of...
Multi-modal image registration has been a challenging task in medical images because of the complex intensity relationship between images to be aligned. Registration methods often rely on the statistical intensity relationship between the images which suffers from problems such as statistical insufficiency. The proposed registration method works based on extracting structural features by utilizing...
In recent years many automatic methods have been developed to help physicians diagnose brain disorders, but the problem remains complex. In this paper we propose a method to segment brain structures on two 3D multi-modal MR images taken at different times (longitudinal acquisition). A bias field correction is performed with an adaptation of the Hidden Markov Chain (HMC) allowing us to take into account...
The segmentation of brain magnetic resonance (MR) images into gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) has been an intensive studied area in the medical image analysis community. The Gaussian mixture model (GMM) is one of the most commonly used model to represent the intensity of different tissue types. However, as a histogram-based model, the spatial relationship between...
The growth of kidney disease has gradually increased over the coming years. Ultrasound imaging is an incontestable vital tool for diagnosis, it provides the internal structure of the body to detect eventually diseases or abnormal tissues non-invasively. This paper presents analysis of Ultrasonography (USG) images for detection of Chronic Kidney Disease(CKD) stages. The methodology of image processing...
As one of the most important image segmentation models, the Mumford-Shah functional was developed to pursue a piecewise smooth approximation of a given image based on the regularization on the total length of curves. In this paper, we modify the Mumford-Shah model using Euler's elastic a as the regularization. A two-stage segmentation method is applied the Euler's elastic a regularized Mumford-Shah...
Nonlocal regularization has been verified as an effective way to estimate optical flow. Most work in this line constructs the regularizer by only considering the structure of regular grid-like nonlocal neighborhood, but not explicitly takes advantage of the global structure. In this paper, we propose to construct a super pixel based region tree to explicitly incorporate the global structure information...
This paper proposes a novel local contrast pattern (LCP) to drive the histogram-based Chan-Vese (CV) model for texture image segmentation. The local contrast pattern has two maps, differential contrast map and orientation map, which are well suited to describe texture structure, especially the texture orientation information. In order to enable the extraction of accurate local texture features, a...
Patients waiting for heart transplantation due to a failing heart can get a left ventricular assist device (LVAD) implanted through open chest surgery. The device consists of a pump that pumps blood from the left ventricle into the aorta. To get the correct rotation speed of the pump, the physicians consider a number of measurements as well as a sequence of echocardiographic images. The important...
Active contour is a popular technique for vascular segmentation. However, existing active contour segmentation methods require users to set values for various parameters, which requires insights to the method's mathematical formulation. Manual tuning of these parameters to optimize segmentation results is laborious for clinicians who often lack in-depth knowledge of the segmentation algorithms. Moreover,...
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