Salwa Elshazly

Learn More
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)
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)
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)
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)
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)
Template matching is a common approach for detection of lung nodules from CT scans. Templates may take different shapes, size and intensity distribution. The process of nodule detection is essentially two steps: isolation of candidate nodules, and elimination of false positive nodules. The processes of outlining the detected nodules and their classification(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)
In this paper, computed tomographic (CT) chest images were investigated to develop an automated system to discriminate lung cancer. These were done by analyzing Data recorded for patients with benign cancer, and also patients with malignant lung cancer were taken in account. The techniques for utilized feature extraction included features derived from(More)