Aamer Aziz

Central Queensland University, School of Health, Medical and Applied Sciences, Faculty Member

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Ruth DeSouza

University of Melbourne

Remo Caponi

University of Cologne

Ibrahim Cahit

Near East University

Timothy Morton

Rice University

Isad Saric

University of Sarajevo

Umit Unver

Yalova University

mahmoud hafiz

Al-Azhar University

Nikos Mantzakouras

National & Kapodistrian University of Athens

Sabina Begic

University of Tuzla

Prof. Dr. Hesham N Mustafa 🩺

King AbdulAziz University (KAU) Jeddah, Saudi Arabia

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Papers by Aamer Aziz

<title>Deformable associate net approach for chest CT image segmentation</title>

Medical Imaging 2005: Image Processing, 2005

ABSTRACT We propose a new deformable model Deformable Associate Net (DAN). It is represented by a... more ABSTRACT We propose a new deformable model Deformable Associate Net (DAN). It is represented by a set of nodes which are associated by deformation constrains such as topology association, inter-part association, intra-part association, and geometry to atlas association. Each node in the model is given a priority, and hence DAN is a hierarchical model in which each layer is decided by nodes with same priority. Directional edges and dynamic generated local atlases are used in energy function to incorporate knowledge about tissue and image acquisition. A fast digital topology based method is designed to check whether topology of the model is changed under deformation. The deformation procedure hierarchically combines global and local deformations. Layers with high priority deform first. Once a higher layer is deformed to its target position in an image, the nodes in this layer are fixed, and then used as reference to help lower layers deform to their initial positions. At a particular layer, the model is first deformed by using global affine transformation to fit the image roughly, and then is warped by using a local deformation to fit the image better. The proposed method has been used to segment chest CT images for thoracic surgical planning, and it is also promising for other medical applications, such as model based image registration, and model-based 3D modeling.

Extraction of the midsagittal plane from morphological neuroimages using the Kullback–Leibler’s measure

by Aamer Aziz and Bhanu Prakash K N

Medical Image Analysis, 2006

A theoretically simple and computationally efficient method to extract the midsagittal plane (MSP... more A theoretically simple and computationally efficient method to extract the midsagittal plane (MSP) from volumetric neuroimages is presented. The method works in two stages (coarse and fine) and is based on calculation of the Kullback and Leibler&amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;amp;#39;s (KL) measure, which characterizes the difference between two distributions. Slices along the sagittal direction are analyzed with respect to a reference slice to determine the coarse MSP. To calculate the final MSP, a local search algorithm is applied. The proposed method does not need any preprocessing, like reformatting, skull stripping, etc. The algorithm was validated quantitatively on 75 MRI datasets of different pulse sequences (T1WI, T2WI, FLAIR and SPGR) and MRA. The angular and distance errors between the calculated MSP and the ground truth lines marked by the expert were calculated. The average distance and angular deviation were 1.25 pixels and 0.63 degrees , respectively. In addition, the algorithm was tested qualitatively on PD, FLAIR, MRA, and CT datasets. To analyze the robustness of the method against rotation, inhomogeneity and noise, the phantom data were used.

Method and apparatus for extracting third ventricle information

Automatic Morphometry of Normal Cerebral Ventricular Dimensions from MRI

Acta Medica Nagasakiensia, Sep 1, 2005

Aamer Aziz

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