Saif Al-Shaikhli left the Institut für Informationsverabeitung.
Publications and research activities from the time after the departure are not listed here.
Saif Al-Shaikhli studied Biomedical Engineering at the Nahrain University in Baghdad. He received his Master degree from Al-Nahrain University in June 2006.
Since 2007 he has been working as a Lecturer in Baghdad university in biomedical engineering department.
Since October 2011 he has been working toward a PhD degree at the TNT Institute for information processing 'Institut für Informationverarbeitung' at the Leibniz University of Hannover.
His research interests are brain and liver segmentation, and tumor classification in MRI and CT scan medical images.
Show selected publications only
3D automatic liver segmentation using feature-constrained mahalanobis distance in CT images
Biomedical Engineering / Biomedizinische Technik (BMT), 2016
Alzheimer's disease detection via automatic 3d caudate nucleus segmentation using coupled dictionary learning with level set formulation
Computer Methods and Programs in Biomedicine, 2016
Brain tumor classification segmentation using sparse coding and dictionary learning
Biomedical Engineering / Biomedi-zinische Technik (BMT), 2016
3D Automatic Liver Segmentation Using Feature-Constrained Mahalanobis Distance in CT Images
Biomedical Engineering / Biomedizinische Technik (BMT) Journal , De Gruyter, VDE, DGBMT, Karlsruhe, Germany, September 2015, edited by Olaf Dössel. (Impact Factor: 1.458)
Automatic 3D Liver Segmentation Using Sparse Representation of Global and Local Image Information via Level Set Formulation
Technical Report, arXiv, Subjects: Computer Vision and Pattern Recognition (cs.CV), August 2015
A Novel Dictionary Learning Method For Remote Sensing Image Classification
International Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy, IEEE Xplore Digital Library, July 2015
Brain Tumor Classification and Segmentation Using Sparse Coding and Dictionary Learning
Biomedical Engineering / Biomedizinische Technik (BMT) Journal
, De Gruyter, VDE, DGBMT, Karlsruhe, Germany, July 2015, edited by Olaf Dössel. (Impact Factor: 1.458)
Multi-Region Labeling and Segmentation Using a Graph Topology Prior and Atlas Information in Brain Images
Computerized Medical Imaging and Graphics Journal (CMIG), Elsevier, Vol. 38, No. 8, pp. 725-734, December 2014, edited by Stephen Wong, Ph.D., P.E. (Impact Factor: 1.588)
Coupled Dictionary Learning for Multi-Label Brain Tumor Segmentation in Flair MRI images
Advances in Visual Computing - 10th International Symposium on Visual Computing (ISVC), accepted for publication (oral presentation), Lecture Notes in Computer Science (LNCS 8887),Springer International Publishing Switzerland, pp. 489-500, USA, December 2014, edited by George Bebis et al
Brain Tumor Classification Using Sparse Coding and Dictionary Learning
IEEE International Conference on Image Processing (ICIP), accepted for publication (oral presentation)., IEEE Xplore Digital Library, pp. 2752-2756, France (Paris), October 2014
Medical Image Segmentation Using Multi-level Set Partitioning with Topological Graph Prior
PSIVT 2013 Workshops, Springer Lecture Notes on Computer Sciences (LNCS), pp. 157--168,, Springer, October 2013
Low Level Diode Laser Accelerates Wound Healing
Laser in Medical Science, Springer London, Vol. 28, pp. 941-945, May 2013, edited by Keyvan Nouri. (Impact Factor: 2.489)
3D Brain Segmentation Using Active contour with Multi-Labeling Method
IEEE First National Conference for Engineering Sciences (FNCES), IEEE Iraq Section, November 2012
Segmentation of Prostate Gland Tumor in Ultrasound Images
International Conference in Computer Science, Baghdad, Iraq, February 2010
Segmentation of Tumor Tissue in Gray Medical Images Using Watershed Transformation Method
International Journal of Advancements in Computing Technology(IJACT), Vol. 2, pp. 123-127, Korea, January 2010
A Histological Study of the Effect of the Low Level Laser Therapy on Wound Healing
Nahrain University College of Engineering Journal (NUCEJ), Vol. 12, No. 1, pp. 80-88, January 2009
Since 2014 Member of German Association for Pattern Recognition (DAGM).