Omar Al-Kadi, PhD

Visiting Research Scientist

Research Interests

Algorithms; Fourier Analysis; Medical Informatics; Computer-Aided Design; Computational Biology; Imaging, Three-Dimensional; Wavelet Analysis

Research Summary

I am interested in developing novel approaches in image processing and analysis, such as texture analysis, image classification and segmentation, with a particular interest in medical imaging. I aspire to contribute to the global research effort for promoting more effective approaches in healthcare through developing intelligent techniques for early disease diagnosis and improving person’s health and well-being.

Extensive Research Description

Tumor Texture Analysis

My  work is mainly concerned with developing novel algorithms to understand how tumors affect tissue texture images. Different medical imaging modalities tend to characterize tissue with diverse types of textures, and there is no single approach to analyse all textures. A wise approach would be to investigate heterogeneity in tissue texture for signs of abnormality. A number of novel texture analysis techniques were developed to detect and differentiate between tumors subtypes early, and further assess their progression or regression.

The first texture is represented by images acquired noninvasively giving a fine texture structure, where lung tumor contrast enhanced images were acquired via computed tomography (CT) modality. Lung tumor staging predication accuracy was improved from conventional CT alone i.e. without using a PET scan, through identifying malignant aggressiveness of lung tumors by examining vascularized tumor regions that exhibit strong fractal characteristics. (more info)

- The quality of the extracted texture feature is substantial for an accurate diagnosis. Therefore the impact of noise extracted via different statistical, model and filtered-based texture analysis algorithms was assessed on non-contrast enhanced CT images. Experiments were performed on lung tumor regions of interest, and the performance of the texture analysis method was compared without and under noise presence. (more info)

- In another work, a different type of texture was dealt with for the purpose of developing an automated system for improving histopathological meningioma brain tumors discrimination acquired invasively via digital microscopy modality. Histopathological texture usually has a macro or coarse structure where the cell nuclei color, shape and orientation defines the general texture structure and play an important role in the feature extraction process (more info) and for automated classification (more info).

- My work was also concerned with a third type of texture represented in the problem of ultrasound tissue characterization. Tumor spatial and contrast resolution in ultrasound images is low as compared to other modalities, therefore a new approach for assessing subtle heterogeneities within a given mass in ultrasound image texture was proposed. Volumetric Nakagami-based shape and scale parameters were estimated from ultrasound RF, and a multi-resolution Daubechies wavelet packet transform was performed adaptively. Finally, local multi-scale textural fractal descriptors were extracted from volumetric patches. Results show improved prediction of therapy response and tumor characterization. (more info)

- Also fractal analysis of the echo signal was found to give additional information about the heterogeneity of the underlying liver tissue structure. Regions within the liver tumor tissue which responded to chemotherapy treatment were shown to exhibit different statistical properties to that of the non-respondent counterpart. (more info)

Selected Publications

  • Texture analysis of aggressive and nonaggressive lung tumor CE CT images.

    Al-Kadi OS, Watson D. Texture analysis of aggressive and nonaggressive lung tumor CE CT images. IEEE Transactions On Bio-medical Engineering 2008, 55:1822-30.

  • Assessment of texture measures susceptibility to noise in conventional and contrast enhanced computed tomography lung tumour images.

    Al-Kadi OS. Assessment of texture measures susceptibility to noise in conventional and contrast enhanced computed tomography lung tumour images. Computerized Medical Imaging And Graphics : The Official Journal Of The Computerized Medical Imaging Society 2010, 34:494-503.

  • Texture measures combination for improved meningioma classification of histopathological images O. S. Al-Kadi, “Texture measures combination for improved meningioma classification of histopathological images,” Pattern Recognition, vol. 43(6), pp. 2043-2053, 2010.
  • Texture analysis of computed tomography images in characterization of oral cancers involving buccal mucosa 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.
  • A multiresolution clinical decision support system based on fractal model design for classification of histological brain tumours.

    Al-Kadi OS. A multiresolution clinical decision support system based on fractal model design for classification of histological brain tumours. Computerized Medical Imaging And Graphics : The Official Journal Of The Computerized Medical Imaging Society 2015, 41:67-79.

  • Quantification of ultrasonic texture intra-heterogeneity via volumetric stochastic modeling for tissue characterization.

    Al-Kadi OS, Chung DY, Carlisle RC, Coussios CC, Noble JA. Quantification of ultrasonic texture intra-heterogeneity via volumetric stochastic modeling for tissue characterization. Medical Image Analysis 2015, 21:59-71.

  • Heterogeneous Tissue Characterization Using Ultrasound: A Comparison of Fractal Analysis Backscatter Models on Liver Tumors.

    Al-Kadi OS, Chung DY, Coussios CC, Noble JA. Heterogeneous Tissue Characterization Using Ultrasound: A Comparison of Fractal Analysis Backscatter Models on Liver Tumors. Ultrasound In Medicine & Biology 2016, 42:1612-26.

  • Multidimensional Texture Analysis for Improved Prediction of Ultrasound Liver Tumor Response to Chemotherapy Treatment 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.
  • Multiscale Nakagami parametric imaging for improved liver tumor localization 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.
  • Supervised texture segmentation: a comparative study 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.
  • A fractal dimension based optimal wavelet packet analysis technique for classification of meningioma brain tumours 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.
  • Susceptibility of texture measures to noise: an application to lung tumor CT images 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.
  • Combined statistical and model based texture features for improved image classification 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.

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Contact Info

Omar Al-Kadi, PhD
Office Location
The Anlyan Center
300 Cedar Street

New Haven, CT 06519
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A Breath of Fractals

Using fractal analysis for enhancing conventional CT tumor staging prediction accuracy through identifying malignant aggressiveness of lung tumors.

Histopathology texture analysis

Meningioma histopathological images and corresponding fractal dimension parametric images.