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.
Melasma is a widely spread skin pigmentation disease and accurate assessments of the disease severity is crucial during its treatment. Recently, several computerized methods have been developed to overcome the shortcomings of the conventional clinical assessment method. As a key step in algorithm, image segmentation has extensive impacts on the accuracy of the assessment. Currently, the optimal hybrid...
Document image binarization is a crucial step towards optical character recognition and analysis. One common way to achieve image binarization is thresholding. Thresholding methods can be divided into global and local ones in terms of the regional information used in obtaining the threshold values. Both methods have their respective drawbacks. Global methods can not adapt to background variations...
This paper proposes a new method for melasma pigmentary area segmentation utilizing re action-diffusion based level set model (RDLSM) together with local entropy thresholding. In the adopted level set model, a diffusion term is used to regularize the level set function while a reaction term with anticipated sign property is used to force the zero level set towards desired locations. Then local entropy...
Accurate rodent brain extraction is the basic step for many translational studies using MR imaging. This paper presents a template based approach with multi-expert refinement to automatic rodent brain extraction. We first build the brain appearance model based on the learning exemplars. Together with the template matching, we encode the rodent brain position into the search space to reliably locate...
Melasma image segmentation plays a fundamental role for computerized melasma severity assessment. A method of hybrid threshold optimization between a given image and its local regions is proposed and used for melasma image segmentation. An analytic optimal hybrid threshold solution is obtained by minimizing the deviation between the given image and its segmented outcome. This optimal hybrid threshold...
In this paper, we propose a new framework, namely hybrid classifiers ensemble with random undersampling for liver tumor segmentation. Essentially, the proposed framework is working on computed tomography images in which each pixel is represented by a rich feature vector. To handle the class imbalance problem, those pixels which correspond to non-tumor region are randomly subsampled. Outcomes of three...
This paper presents a new approach to detect and segment liver tumors. The detection and segmentation of liver tumors can be formulized as novelty detection or two-class classification problem. Each voxel is characterized by a rich feature vector, and a classifier using random feature subspace ensemble is trained to classify the voxels. Since Extreme Learning Machine (ELM) has advantages of very fast...
Information about abdominal wall can be used for many applications from organ segmentation, registration, and surgical simulation. The challenges exist in abdominal wall extraction due to its varieties in shapes, connection to the internal organs and anterior layer edge formed between the muscle and fascia/fatty layer, which may distract the shape model. In this paper we present an approach to the...
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.