Salwa Elshazly

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This paper examines the effectiveness of geometric feature descriptors, common in computer vision, for false positive reduction and for classification of lung nodules in low dose CT (LDCT) scans. A data-driven lung nodule modeling approach creates templates for common nodule types, using active appearance models (AAM); which are then used to detect(More)
Our long term research goal is to develop an automatic approach for early detection of lung nodules that may lead to lung cancer. This paper focuses on the monitoring of the progress (growth or shrinking) of lung nodules in successive chest low dose CT (LDCT) scans of a person using non-rigid registration. The overall nodule detection approach consists of(More)
This paper examines the detection step in automatic detection and classification of lung nodules from low-dose CT (LDCT) scans. Two issues are studied in detail: nodule modeling and simulation, and the effect of these models on the detection process. From an ensemble of nodules, specified by radiologists, we devise an approach to estimate the gray level(More)
Lung nodules from low dose CT (LDCT) scans may be used for early detection of lung cancer. However, these nodules vary in size, shape, texture, location, and may suffer from occlusion within the tissue. This paper presents an approach for segmentation of lung nodules detected by a prior step. First, regions around the detected nodules are segmented; using(More)
To more accurately separate each pulmonary nodule from its background in a low dose computer tomography (LDCT) chest image, two new adaptive probability models of visual appearance of small 2D and large 3D pulmonary nodules are used to control evolution of deformable boundaries. The appearance prior is modeled with a translation and rotation invariant(More)
A novel approach is proposed for generating data driven models of the lung nodules appearing in low dose CT (LDCT) scans of the human chest. Four types of common lung nodules are analyzed using Active Appearance Model methods to create descriptive lung nodule models. The proposed approach is also applicable for automatic classification of nodules into(More)
This work presents a novel approach in automatic detection of the lung nodules and is compared with respect to parametric nodule models in terms of sensitivity and specificity. A Statistical method is used for generating data driven models of the nodules appearing in low dose CT (LDCT) scans of the human chest. Four types of common lung nodules are analyzed(More)
The quality of the lung nodule models determines the success of lung nodule detection. This paper describes aspects of our data-driven approach for modeling lung nodules using the texture and shape properties of real nodules to form an average model template per nodule type. The ELCAP low dose CT (LDCT) scans database is used to create the required(More)
While traditional dental fillings are molded during a dental visit, dental restoration (e.g. inlays and onlays) are fabricated in a dental lab to offer a long lasting reparative solution to tooth decay or similar structural damage. Such process requires dental technicians who are highly trained experts in tooth anatomy to pick an appropriate standard tooth(More)