Tumour Grading and Discrimination based on Class Assignment and Quantitative Texture Analysis Techniques.
Adrien Depeursinge, O. S. Al-Kadi, J.Ross Mitchell, “Biomedical Texture Analysis: Fundamentals, Tools and Challenges,” London: Academic Press, 2017.
Al-Kadi, O. S., Ye, X., Russo, G., Mitchell, J. R., “Computational Radiomics for Cancer Characterization,”
Lausanne: Frontiers Media SA, 2022.
O. S. Al-Kadi and A. Di Ieva, “Histological fractal-based classification of brain tumors” in The Fractal Geometry of the Brain, New York: Springer-Verlag, 2016, pp. 371-391.
A. Di Ieva and O. S. Al-Kadi, “Computational fractal-based analysis of brain tumors microvascular networks” in The Fractal Geometry of the Brain, New York: Springer-Verlag, 2016, pp. 393-411.
Journals (Selected Thomson Reuters JCR indexed)
O. S. Al-Kadi, “Prediction of FDG-PET stage and uptake for non-small cell lung cancer on non-contrast enhanced CT scans via fractal analysis,” Clinical Imaging, vol. 65, pp. 54-59, 2020. (DOI: http://dx.doi.org/10.1016/j.clinimag.2020.03.005)
Omar S. Al-Kadi, “Spatio-Temporal Segmentation in 3D Echocardiographic Sequences using Fractional Brownian Motion,” IEEE Transactions on Biomedical Engineering, vol. 67(8), pp. 2286-2296, 2020. (Video examples 1, 2 and 3)
Omar S. Al-Kadi, Daniel Y.F. Chung, Constantin C. Coussios, J. Alison Noble, “Heterogeneous Tissue Characterization Using Ultrasound: A Comparison of Fractal Analysis Backscatter Models on Liver Tumors,” Ultrasound in Medicine & Biology, vol. 42(7), pp. 1612-1626, 2016.
Omar S. Al-Kadi, Daniel Y.F. Chung, Robert C. Carlisle, Constantin C. Coussios, J. Alison Noble, “Quantification of ultrasonic texture intra-heterogeneity via volumetric stochastic modeling for tissue characterization,” Medical Image Analysis, vol. 21(1), pp. 59-71, 2015. (Open access) (AudioSlides) (Download ultrasound liver tumor Dataset)
O. S. Al-Kadi, “A Multiresolution Clinical Decision Support System Based on Fractal Model Design for Classification of Histological Brain Tumours,” Computerized Medical Imaging and Graphics, vol. 41, pp. 67-79, 2015.
O. Al-Kadi, O. Al-Kadi, R. Al-Sayyed, J. Alqatawna, “Road scene analysis for determination of road traffic density,” Frontiers of Computer Science, vol. 8(4), pp. 619-628, 2014.
J. V. Raja, M. Khan, V. K. Ramachandra and O. S. Al-Kadi, “Texture analysis of computed tomography images in characterization of oral cancers involving buccal mucosa,” Dentomaxillofacial Radiology, vol. 41(6), pp. 475-480, 2012.
O. S. Al-Kadi, “Texture measures combination for improved meningioma classification of histopathological images,” Pattern Recognition, vol. 43(6), pp. 2043-2053, 2010.
O. S. Al-Kadi, “Assessment of texture measures susceptibility to noise in conventional and contrast enhanced computed tomography lung tumour images,” Computerized Medical Imaging and Graphics, vol. 34(6), pp. 494-503, 2010. (Listed one of the Top 25 Hottest articles from July to September 2010)
O. S. Al-Kadi and D. Watson, “Texture Analysis of Aggressive and non-Aggressive Lung Tumor CE CT Images,” IEEE Transactions on Biomedical Engineering, vol. 55(7), pp. 1822-1830, 2008.
O. S. Al-Kadi, D. Van De Ville and A. Depeursinge, “Multidimensional Texture Analysis for Improved Prediction of Ultrasound Liver Tumor Response to Chemotherapy Treatment,” in 19th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Greece, pp. 619-626, 2016.
O. S. Al-Kadi, “Multiscale Nakagami parametric imaging for improved liver tumor localization,” in IEEE International Conference on Image Processing (ICIP), USA, pp. 3384-3388, 2016.
R. Alomari, S. Ghosh, V. Chaudhary and O. Al-Kadi, “Local binary patterns for stromal area removal in histology images,” Proceedings of the SPIE Medical Imaging, USA, 2012.
O. S. Al-Kadi, ”Supervised texture segmentation: a comparative study,” in IEEE Jordan Conf. on Applied Electrical Engineering and Computing Technologies, Jordan, pp. 1-5, 2011.
O. S. Al-Kadi, “A fractal dimension based optimal wavelet packet analysis technique for classification of meningioma brain tumours,” in IEEE International Conference on Image Processing (ICIP), Egypt, pp. 4125-4128, 2009.
O. S. Al-Kadi and D. Watson, “Susceptibility of texture measures to noise: an application to lung tumor CT images,” in 8th International Conference on BioInformatics and BioEngineering, Greece, pp. 1-4, 2008.
O. S. Al-Kadi, “Combined statistical and model based texture features for improved image classification,” in 4th International Conference on Advances in Medical, Signal and Information Processing, Italy, pp. 175-178, 2008.
Omar S. Al-Kadi, Allen Lu, Albert J. Sinusas and James S. Duncan. “Stochastic Model-Based Left Ventricle Segmentation in 3D Echocardiography Using Fractional Brownian Motion,” in International Workshop on Statistical Atlases and Computational Models of the Heart, Spain, pp. 77-84, 2018.
Omar S. Al-Kadi, Daniel YF Chung, Constantin C Coussios, J Alison Noble,”Predicting tumour responsiveness to chemotherapy treatment in volumetric Xenograft images,” Medical Engineering Centres Annual Meeting and Bioengineering14: Cancer Engineering and Technologies, London, UK, Sep 2014.
Daniel YF Chung, Omar S. Al-Kadi, Constantin C Coussios, J Alison Noble,”Quantitative ultrasound assessment of the response of liver metastases to chemotherapy using the Nakagami shape parameter,” Medical Engineering Centres Annual Meeting and Bioengineering14, London, UK, Sep 2014.
O. S. Al-Kadi, D. Chung, A. Cifor, C. Coussios, J. A. Noble,”Predicting tumour responsiveness to chemotherapy in volumetric liver ultrasound images,” Oxford Biomedical Imaging Festival, Oxford, UK, Oct 2013.
O. S. Al-Kadi, E. Panayiotou, B. Young, D. Watson,”Fractal dimensions of non-small cell lung cancer on CT predicts FDG-PET stage and uptake,” UK Radiological Congress, Birmingham, UK, Jun 2008.
O. S. Al-Kadi, D. Watson,”Fractal analysis of CE CT lung tumours images,” The ninth Great British R&D Show, Westminster, London, UK, Mar 2007.
O. S. Al-Kadi, D. Watson,”Fractal analysis in Digital Medical Images for lung tumours blood vessels,” BSUH/BSMS 4th Annual Research Symposium in Vascular Medicine, Brighton, UK, Nov 2006.
Generating a pixel-by-pixel fractal dimension image using box counting algorithm. (Download code)
Liver tumour segmentation from 3D Radio-Frequency Ultrasound images