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.
A high recurrence rate, and progression to higher stages are observed for patients diagnosed with urothelial car-cinoma (previously known as transitional cell carcinoma). Low prognostic value of the current grading systems result in extensive follow-up of patients for multiple years after first diagnosis. Although, the aid of computer systems for prognosis prediction of superficial urothelial carcinomas...
This paper describes how to construct a probability map using sparse representation and dictionary learning to indicate the probability of each optic disk pixel of belonging to the optic cup. This probability map will be used in the future as input to a method for automatically detecting glaucoma from color fundus images. The probability map was obtained constructing a model (using the Bayes classifier)...
Dictionary learning and Sparse representation of signals and images has been a hot topic for the past decade and aims to help find the sparsest representation for the signal(s) at hand. Typically, the Dictionary learning process involves finding a large number of free variables. Also, the resulting dictionary in general does not have a specific structure. In this paper we use the ideas from Image...
A technique for designing frames to use with vector selection algorithms, for example Matching Pursuits (MP), is presented along with a novel compression scheme using these optimized frames. The frame design algorithm is iterative and requires a training set. We apply the frame design algorithm and the complete multi-frame compression scheme to electrocardiogram (ECG) signals. Complete coding experiments...
Implantable cardioverter-defibrillator (ICD) prevents sudden cardiac death in patients with healed myocardial infarction (MI) at high risk of serious arrhythmias. This study was designed to identify if texture analysis of cardiac magnetic rensonance (CMR) images can be used to identify high-risk patients likely to benefit from ICD implantation. Two groups of patients with MI were compared: 24 patients...
The late gadolinium enhancement in Cardiac Magnetic Resonance (CMR) imaging is used to increase the intensity of scar area in myocardium for thorough examination. The results in our previous work [1] arises the hypothesis that there are textural differences between the non-scarred myocardium and the scarred areas. This paper presents our work of testing the hypothesis further by applying dictionary...
The Late Gadolinium (LG) enhancement in Cardiac Magnetic Resonance (CMR) imaging is used to increase the intensity of scarred area in myocardium for thorough examination. Automatic segmentation of scar is important because scar size is largely responsible in changing the size, shape and functioning of left ventricle and it is a preliminary step required in exploring the information present in scar...
The recently presented recursive least squares dictionary learning algorithm (RLS-DLA) is tested in a general image compression application. Dictionaries are learned in the pixel domain and in the 9/7 wavelet domain, and then tested in a straightforward compression scheme. Results are compared with state-of-the-art compression methods. The proposed compression scheme using RLS-DLA learned dictionaries...
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.