Browsing by Author "Ali Imam Abidi"

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Automatic Deformity Estimation for Thoracic Section Between Inhale and Exhale Positions
(Springer India, 2016) Ali Imam Abidi; Sanjay Kumar Singh; Lalit M. Aggarwal
Corresponding control point pairs or landmarks in images can be used to define the deformation with respect to time, point of view or modality. Manual definition of the number of control points in an image, enough to define all kinds of deformation is a tedious task. Hence, automatic definition of control points is the way forward taken in this proposed work. The paper proposes an automatic registration process for tracing of the deformity path of the thoracic region based on feature detector speeded up robust feature (SURF) and moving least squares (MLS). The set of control points on the images is defined by its feature set which is obtained by using the SURF detector, which serves as input for MLS algorithm to trace the deformations of the image (thoracic image in this case). The credibility and performance of the above proposed method is demonstrated by its outstanding experimental results. © 2016, The National Academy of Sciences, India.
Deformable Registration Techniques for Thoracic CT Images An Insight into Medical Image Registration
(Springer Singapore, 2020) Ali Imam Abidi; S.K. Singh
This book focuses on novel approaches for thoracic computed tomography (CT) image registration and determination of respiratory motion models in a range of patient scenarios. It discusses the use of image registration processes to remove the inconsistencies between medical images acquired using different devices. In the context of comparative research and medical analysis, these methods are of immense value in image registration procedures, not just for thoracic CT images, but for all types of medical images in multiple modalities, and also in establishing a mean respiration motion model. Combined with advanced techniques, the methods proposed have the potential to advance the field of computer vision and help improve existing methods. The book is a valuable resource for those in the scientific community involved in modeling respiratory motion for a large number of people. © Springer Nature Singapore Pte Ltd. 2020.