The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Multispectral iris recognition uses information from multiple bands of the electromagnetic spectrum to better represent certain physiological characteristics of the iris texture and enhance obtained recognition accuracy. This paper addresses the questions of single versus cross-spectral performance and compares score-level fusion accuracy for different feature types, combining different wavelengths...
This paper describes a new segmentation-based classification technique for fully polarimetric synthetic aperture radar (PolSAR) images. Based on the framework which conjunctively uses statistical region merging (SRM) for segmentation and support vector machine (SVM) for classification, we improve the method by jointly introducing texture features and color features. For the segmentation step, to guarantee...
Character segmentation plays an important role in Optical Character Recognition (OCR) for Indian Script. The image to be segmented consists of various characters and is broken down into different segmented images of individual characters. These segmented characters are then fed to the Recognition phase of OCR. This paper covers only the segmentation of Devnagari script, as it consists of vowels, consonants...
For the patients with neurological disturbance and head injury, brain hemorrhage may be considered as one of the primary causes. It is the leading cause of human deaths in the world following heart disease and cancer. The rupturing of blood vessels present inside the brain results in brain hemorrhage. It consists of bleeding outside or inside of the brain substance. Computed Tomography (CT) is one...
Mars rover is a robot which explores the Mars surface, is equipped to front-line Panoramic Camera (Pancam). Automatic processing and segmentation of images taken by Pancam is one of the most important and most significant tasks of Mars rover since the transformation cost of images from Mars to earth is extremely high. In this paper, a new feature vector for image pixels will be proposed as well as...
In building an automated glaucoma detection system, optic disc segmentation is the first step that needs to be implemented follows by optic cup segmentation in order to quatify the severity level of glaucoma. Glaucoma is an ocular eye disease that can lead to gradual vision loss and permanent blindness if it is not treated in the early stage. Many glaucoma patients are unaware of their disease since...
In any public gathering, to ensure the safety of the people during or after any kind of natural or man-made calamities, counting the number of people in a crowd is of paramount importance. In cases of unpredictable environments where occlusion, shadows, varied illumination and multiple objects in the background persists, many algorithms fail to give out accurate results. In this paper, to overcome...
The abundant spectral and spatial information in the hyperspectral images (HSI) are largely used in the field of remote sensing. Though there are highly sophisticated sensors to capture the hyperspectral imagery, they suffer from issues like hyperspectral noise and spectral mixing. The major challenges encountered in this field, demands the use of preprocessing techniques prior to hyperspectral image...
Segmentation and classification of cells in biological data are important problems in bio-medical image analysis. This paper outlines a novel probabilistic approach to simultaneously classify and segment multiple cells of different classes in a multi-variate setting. Superpixels are extracted from the input vector-valued image, and a 2D hidden Markov model (HMM) is set up on the superpixel graph....
Quantitative analysis of cardiac Magnetic Resonance (CMR) images requires accurate segmentation of myocardium. Although recent multi-atlas segmentation approaches have done a good job improving segmentation accuracy, they also increase the computational burden, which degrades their clinical utility. In this paper, we proposed a novel multi-atlas segmentation framework using an augmented atlas technique...
We use a Field of Experts (FoE) model to segment abdominal regions from MRI affected with Crohns Disease (CD). FoE learns a prior model of diseased and normal bowel, and background non-bowel tissues from manually annotated training images. Unlike current approaches, FoE does not rely on hand designed features but learns the most discriminative features (in the form of filters) for different classes...
Multi-atlas segmentation techniques typically comprise generation of multiple candidate labels that are then combined at a final label fusion stage. Label fusion strategies usually leverage information contained in these training labels but ignore local neuroanatomical information. Here, we address this limitation by explicitly incorporating local information at the label fusion stage. The proposed...
This paper presents new class of time-frequency (T-F) features for automatic detection and classification of epileptic seizure activities in EEG signals. Most previous methods were based only on signal features derived from the instantaneous frequency and energies of EEG signals in different spectral sub-bands. The proposed features based on image descriptors are extracted from the T-F representation...
Automated recognition of brain tumors in magnetic resonance images (MRI) is a difficult procedure due to the variability and complexity of the location, size, shape, and texture of these lesions. Due to intensity similarities between brain lesions and normal tissues, most approaches make use of multi-spectral MRI images. However, the time, cost, and data process restrictions for collecting multi-spectral...
Glioblastoma (GBM) is a markedly heterogeneous brain tumor and is composed of three main volumetric phenotypes, namely, necrosis, active tumor and edema, identifiable on magnetic resonance imaging (MRI). This paper assesses the usefulness of the GBM features detection by using semi-automatic segmentation and texture feature extracted from gray level co-occurrence matrix (GLCM). Feature vectors are...
In this paper a reliable and robust method is presented for the quantification of Focal Arteriolar Narrowing (FAN), a precursor for hypertension, stroke and other cardiovascular diseases. Our contribution in this paper is that we have proposed a novel edge based retinal blood vessel segmentation technique which is very effective in low contrast retinal images. In addition to that we developed a robust...
Robust and accurate segmentation of blood vessels is important for treatment and diagnosis of cardiovascular diseases. Here, we introduce a new approach for 3D segmentation of vessels which is formulated as a convex parameter estimation problem and combined with an incremental tracking approach. Parameter values are determined as global optimum of a semidefinite program and admissible shape variations...
Detection of brain tumors may help in diagnosis, therapy planning and treatment planning. Brain tumor detection in scout scan may also help in identify the surrounding tissue of the pathology and thus help in imaging parameter optimization. We propose an automatic method to segment brain tumor on one single T2W image. The proposed method for the brain tumor segmentation consists of three steps. In...
Image-derived features (“radiomics”) are increasingly being considered for patient management in (neuro)oncology and radiotherapy. In Glioblastoma multiforme (GBM), simple features are often used by clinicians in clinical practice, such as the size of the tumor or the relative sizes of the necrosis and active tumor. First order statistics provide a limited characterization power because they do not...
Conventional multi-atlas-based segmentation demands pairwise full-fledged registration between each atlas image and the target image, which leads to high computational cost and poses great challenge in the new era of big data. On the other hand, only the most relevant atlases should contribute to final label fusion. In this work, we introduce a two-stage fusion set selection method by first trimming...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.