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.
The paper introduces a proposal for an automated magnetic resonance (MR) image segmentation called Case-Based Genetic Algorithm Location-Dependent Image Classification (CBGA-LDIC) and presents its evaluation results. This method finds an appropriate cell set towards efficient image segmentation. It uses location-dependent image classification (LDIC), which is integrated by genetic algorithm (GA) combined...
There are 700,000 Rheumatoid Arthritis (RA) patients in Japan, and the number of patients is increased by 30,000 annually. The early detection and appropriate treatment according to the progression of RA are effective to improve the patient's prognosis. The modified Total Sharp (mTS) score is widely used for the progression evaluation of Rheumatoid Arthritis. The mTS score assessments on hand or foot...
In this paper, age estimation models introduced with automatic preprocessing of the T-1 weighted images, dimension reduction via principal component analysis, training of a multiple regression model, and then estimating the age of the subjects from the test samples. The regression model is automatically trained from a diverse set of 80 adult subjects (age 60–92 years) exhibiting significant variation...
Aiming at application to automated recognition of knee bone magnetic resonance (MR) images, an evolutional classification method called CBGA-LDIC is proposed. CBGA-LDIC finds an appropriate cell set towards efficient image segmentation. This method uses location-dependent image classification (LDIC), which is integrated by genetic algorithm (GA) combined with case based reasoning (CB). LDIC introduces...
Cerebral parenchyma segmentation in newborn magnetic resonance (MR) images is crucial for developing computer-aided diagnosis systems in newborn cerebral diseases. However, there is limited number of studies on newborn brain MR image analysis. This study presents a novel method for fully automatically segmenting the cerebral parenchyma region using scale-based fuzzy connected image segmentation and...
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.