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.
Lung cancer is a conceivably deadly disease brought on predominantly by ecological factors that transform genes that encodes basic cell regularities proteins. This paper investigates the early detection of lung cancer using computer aided diagnosis system which helps to improve the life term of the patient. The Existing multimodal sparse representation based classification of lung cancer related abnormalities...
Radiographic-imaging modalities like computerized tomography, positron emission tomography, and magnetic resonance imaging are playing a major role in the diagnosis and prognosis of cancer. Gene and protein expression patterns, from the tumor genome, are seen to facilitate individualized selection of therapies. Along with breakthroughs in biotechnology, applicable within cancer radiation biology,...
Accurate prostate localization is the key to the success of radiotherapy. It remains a difficult problem for CT images due to the low image contrast, the prostate motion, and the uncertain presence of rectum gas. In this paper, a learning based framework is proposed to improve the accuracy of prostate detection in CT. It adaptively determines distinctive feature types at distinctive image regions,...
This study presents a computer-aided detection (CADe) system of hepatocellular carcinoma (HCC) using sequential forward floating selection (SFFS) method with linear discriminant analysis (LDA). We extracted morphologic and texture features from the segmented HCC candidate regions from the arterial phase (AP) images of the contrast-enhanced hepatic CT images. To select the most discriminatory features...
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.