Ahmed Al-Taie

@uobaghdad.edu.iq

Computer Science Department/ College of science for women
University of Baghdad

Ahmed Al-Taie


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https://researchid.co/a.altaie

RESEARCH INTERESTS

Image processing, medical image processing, uncertainty estimation and visualisation, image segmentation.
10

Scopus Publications

63

Scholar Citations

4

Scholar h-index

2

Scholar i10-index

Scopus Publications

  • Accelerating Face Mask Detection Training Model Based on Multi-GPUs and Multi-core CPU
    Ammar Hussein Jassim, Nada Hussain Ali, Ahmed Al-Taie, Doaa Mohammad Majed
    Baghdad Science Journal, 2025
  • Diabetes Prediction Using Machine Learning
    Amer Almahdawi, Zaid S. Naama, Ahmed Al-Taie
    3rd Information Technology to Enhance E Learning and Other Application IT Ela 2022, 2022
    Diabetes is one of the increasing chronic diseases, affecting millions of people around the earth. Diabetes diagnosis, its prediction, proper cure, and management are compulsory. Machine learning-based prediction techniques for diabetes data analysis can help in the early detection and prediction of the disease and its consequences such as hypo/hyperglycemia. In this paper, we explored the diabetes dataset collected from the medical records of one thousand Iraqi patients. We applied three classifiers, the multilayer perceptron, the KNN and the Random Forest. We involved two experiments: the first experiment used all 12 features of the dataset. The Random Forest outperforms others with 98.8% accuracy. The second experiment used only five attributes of the training process. The results of the second experiment showed improvement in the performance of the KNN and the Multilayer Perceptron. The results of the second experiment showed a slight decrease in the performance of the Random Forest with 97.5% accuracy.
  • Erratum to: Combining rules using local statistics and uncertainty estimates for improved ensemble segmentation (Pattern Recognition and Image Analysis, (2017), 27, 3, (444-457), 10.1134/S105466181703004X)
    A. Al-Taie, H. K. Hahn, L. Linsen
    Pattern Recognition and Image Analysis, 2018
    The following affiliation, denoted “d,” should be included for the first author A. Al-Taie: dComputer Science Department, College of Science for Women, Baghdad University, Iraq
  • Combining rules using local statistics and uncertainty estimates for improved ensemble segmentation
    A. Al-Taie, H. K. Hahn, L. Linsen
    Pattern Recognition and Image Analysis, 2017
    Segmentation using an ensemble of classifiers (or committee machine) combines multiple classifiers’ results to increase the performance when compared to single classifiers. In this paper, we propose new concepts for combining rules. They are based (1) on uncertainties of the individual classifiers, (2) on combining the result of existing combining rules, (3) on combining local class probabilities with the existing segmentation probabilities at each individual segmentation, and (4) on using uncertainty-based weights for the weighted majority rule. The results show that the proposed local-statistics-aware combining rules can reduce the effect of noise in the individual segmentation result and consequently improve the performance of the final (combined) segmentation. Also, combining existing combining rules and using the proposed uncertainty- based weights can further improve the performance.
  • Fast uncertainty-guided fuzzy C-means segmentation of medical images
    Ahmed Al-Taie, Horst K. Hahn, Lars Linsen
    Mathematics and Visualization, 2016
    Image segmentation is a crucial step of the medical visualization pipeline. In this paper, we present a novel fast algorithm for modified fuzzy c-means segmentation of MRI data. The algorithm consists of two steps, which are executed as two iterations of a fuzzy c-means approach: the first iteration is a standard fuzzy c-means (FCM) iteration, while the second iteration is our modified FCM iteration with misclassification correction. In the second iteration, we use the classification probability vectors (uncertainties) of the neighbor pixels found by the first iteration to confirm or correct the classification decision of the current pixel. The application of the proposed algorithm on synthetic data, simulated MRI data, and real MRI data show that our algorithm is insensitive to different types of noise and outperforms the standard FCM and several versions of modified FCM algorithms in terms of accuracy and speed. In fact, our algorithm can easily be combined with many modified FCM algorithms to improve their segmentation result while reducing the computation costs (using two FCM iterations only). An optional simple post-processing step can further improve the segmentation result by correcting isolated misclassified pixels. We also show that our algorithm reduces the uncertainty in the segmentation result, by using recently proposed uncertainty estimation and visualization tools.
  • Uncertainty and Reproducibility in Medical Visualization
    L. Linsen, A. Al-Taie, Gordan Ristovski, T. Preußer, H. Hahn
    Eurorv3 2016 Eurovis Workshop on Reproducibility Verification and Validation in Visualization, 2016
    The medical visualization pipeline is affected by various sources of uncertainty. Many errors may occur and several assumptions are made in the various processing steps from the image acquisition to the rendering of the visualization output, which induce uncertainty. High uncertainty leads to low robustness of the algorithms impacting reproducibility of the results. We present how uncertainty can be mathematically described in the medical context. Moreover, in medical applications, the visualization is typically based on a segmentation of the medical images. We propose a method to capture uncertainty in image segmentation and present extensions to ensemble and multi-modal image segmentation.
  • Uncertainty estimation and visualization for multi-modal image segmentation
    Ahmed Al-Taie, H. Hahn, Lars Linsen
    Eurographics Workshop on Visual Computing for Biology and Medicine Vcbm 2015, 2015
    Multi-modal imaging allows for the integration of complementary information from multiple medical imaging modalities for an improved analysis. The multiple information channels may lead to a reduction of the uncertainty in the analysis and decision-making process. Recently, efforts have been made to estimate the uncertainty in uni-modal image segmentation decisions and visually convey this information to the medical experts that examine the image segmentation results. We propose an approach to extend uncertainty estimation and visualization methods to multi-modal image segmentations. We combine probabilistic uni-modal image segmentation results using the concept of ensemble of classifiers. The uncertainty is computed using a measure that is based on the Kullback-Leibler divergence. We apply our approach for an improved segmentation of Multiple Sclerosis (MS) lesions from multiple MR brain imaging modalities. Moreover, we demonstrate how our approach can be used to estimate and visualize the growth of a brain tumor area for imaging data taken at multiple points in time. Both the MS lesion and the area of tumor growth are detected as areas of high uncertainty due to different characteristics in different imaging modalities and changes over time, respectively.
  • Point-wise diversity measure and visualization for ensemble of classifiers with application to image segmentation
    Ahmed Al-Taie, Horst K. Hahn, Lars Linsen
    Visapp 2015 10th International Conference on Computer Vision Theory and Applications Visigrapp Proceedings, 2015
    The idea of using ensembles of classifiers is to increase the performance when compared to applying a single classifier. Crucial to the performance improvement is the diversity of the ensemble. A classifier ensemble is considered to be diverse, if the classifiers make no coinciding errors. Several studies discuss the diversity issue and its relation to the ensemble accuracy. Most of them proposed measures that are based on an ”Oracle” classification. In this paper, we propose a new probability-based diversity measure for ensembles of unsupervised classifiers, i.e., when no Oracle machine exists. Our measure uses a point-wise definition of diversity, which allows for a distinction of diverse and non-diverse areas. Moreover, we introduce the concept of further categorizing the diverse areas into healthy and unhealthy diversity areas. A diversity area is healthy for the ensemble performance, if there is enough redundancy to compensate for the errors. Then, the performance of the ensemble can be based on two parameters, the non-diversity area, i.e., the size of all regions where the classifiers of the ensemble agree, and the healthy diversity area, i.e., the size of the regions where the diversity is healthy. Furthermore, our point-wise diversity measure allows for an intuitive visualization of the ensemble diversity for visual ensemble performance comparison in the context of image segmentation.
  • Uncertainty estimation and visualization in probabilistic segmentation
    Ahmed Al-Taie, Horst K. Hahn, Lars Linsen
    Computers and Graphics Pergamon, 2014
    Probabilistic segmentation is concerned with handling imperfections of image segmentation algorithms. They assign to each voxel and each segment a probability that the voxel belongs to the segment. This is often the starting point for estimating and visualizing uncertainties in the segmentation result. We propose a novel, generally applicable uncertainty estimation approach that considers all probabilities to compute a single uncertainty value between 0 and 1 for each voxel. It is based on aspects of information theory and uses the Kullback-Leibler divergence (or the total variation divergence). We developed several forms of the proposed approach and analyze and compare their behaviors. We show the advantage over existing approaches, derive aggregated uncertainty measures that are useful for judging the accuracy of a probabilistic segmentation algorithm, and present visualization methods to highlight uncertainties in segmentation results.
  • Uncertainty-aware ensemble of classifiers for segmenting brain MRI data
    Ahmed Al-Taie, H. Hahn, Lars Linsen
    Eurographics Workshop on Visual Computing for Biology and Medicine Vcbm 2014, 2014
    Estimating and visualizing uncertainty in medical image segmentation has become an active research area due to the necessity of making medical experts aware of possibly wrong segmentation decisions. Still, to our knowledge all these methods are based on a single choice of the underlying segmentation approach. Segmentation using an ensemble of classifiers (or committee machine) use multiple classifiers to increase the performance when compared to applying a single classifier. In this paper, we propose methods to estimate uncertainties in segmentations produced by ensembles of classifiers. We investigate and compare the different combining strategies of the segmentation results of the ensemble members from an uncertainty point of view. We discuss why some combining strategies tend to perform better than others. Also, we visualize the estimated uncertainties using a color mapping in image space and propose a post-segmentations correction step to reclassify the noisy pixels in the final result based on the statistical uncertainty.

RECENT SCHOLAR PUBLICATIONS

  • Accelerating Face Mask Detection Training Model Based on Multi-GPUs and Multi-core CPU.
    AH Jassim, NH Ali, A Al-Taie, DM Majed
    Baghdad Science Journal 22 (6) , 2025
    2025
  • Predicting Students' Performance by Using Data Mining Methods
    A Jassim, A Al-Taie, ZS Naama
    Journal of Kufa for Mathematics and Computer 10 (2), 10-15 , 2023
    2023
  • Diabetes prediction using machine learning
    A Almahdawi, ZS Naama, A Al-Taie
    2022 3rd Information Technology To Enhance e-learning and Other Application … , 2022
    2022
    Citations: 8
  • Combining rules using local statistics and uncertainty estimates for improved ensemble segmentation
    A Al-Taie, HK Hahn, L Linsen
    Pattern Recognition and Image Analysis 27 (3), 444-457 , 2017
    2017
  • Uncertainty and Reproducibility in Medical Visualization.
    L Linsen, A Al-Taie, G Ristovski, T Preusser, HK Hahn
    EuroRV³@ EuroVis, 1-3 , 2016
    2016
    Citations: 3
  • Fast Uncertainty-Guided Fuzzy C-Means Segmentation of Medical Images
    A Al-Taie, HK Hahn, L Linsen
    Visualization in Medicine and Life Sciences III: Towards Making an Impact, 25-48 , 2016
    2016
  • Uncertainty estimation and visualization in segmenting uni-and multi-modal medical imaging data
    AA Al-Taie
    Ulm University, Ulm, Germany , 2015
    2015
    Citations: 1
  • Uncertainty Estimation and Visualization for Multi-modal Image Segmentation.
    A Al-Taie, HK Hahn, L Linsen
    VCBM, 21-30 , 2015
    2015
    Citations: 7
  • Point-wise diversity measure and visualization for ensemble of classifiers-with application to image segmentation
    A Al-Taie, HK Hahn, L Linsen
    International Conference on Computer Vision Theory and Applications 2, 569-576 , 2015
    2015
    Citations: 2
  • Uncertainty-aware Ensemble of Classifiers for Segmenting Brain MRI Data.
    A Al-Taie, HK Hahn, L Linsen
    VCBM, 41-50 , 2014
    2014
    Citations: 10
  • Uncertainty estimation and visualization in probabilistic segmentation
    A Al-Taie, HK Hahn, L Linsen
    Computers & Graphics 39, 48-59 , 2014
    2014
    Citations: 32

MOST CITED SCHOLAR PUBLICATIONS

  • Uncertainty estimation and visualization in probabilistic segmentation
    A Al-Taie, HK Hahn, L Linsen
    Computers & Graphics 39, 48-59 , 2014
    2014
    Citations: 32
  • Uncertainty-aware Ensemble of Classifiers for Segmenting Brain MRI Data.
    A Al-Taie, HK Hahn, L Linsen
    VCBM, 41-50 , 2014
    2014
    Citations: 10
  • Diabetes prediction using machine learning
    A Almahdawi, ZS Naama, A Al-Taie
    2022 3rd Information Technology To Enhance e-learning and Other Application … , 2022
    2022
    Citations: 8
  • Uncertainty Estimation and Visualization for Multi-modal Image Segmentation.
    A Al-Taie, HK Hahn, L Linsen
    VCBM, 21-30 , 2015
    2015
    Citations: 7
  • Uncertainty and Reproducibility in Medical Visualization.
    L Linsen, A Al-Taie, G Ristovski, T Preusser, HK Hahn
    EuroRV³@ EuroVis, 1-3 , 2016
    2016
    Citations: 3
  • Point-wise diversity measure and visualization for ensemble of classifiers-with application to image segmentation
    A Al-Taie, HK Hahn, L Linsen
    International Conference on Computer Vision Theory and Applications 2, 569-576 , 2015
    2015
    Citations: 2
  • Uncertainty estimation and visualization in segmenting uni-and multi-modal medical imaging data
    AA Al-Taie
    Ulm University, Ulm, Germany , 2015
    2015
    Citations: 1
  • Accelerating Face Mask Detection Training Model Based on Multi-GPUs and Multi-core CPU.
    AH Jassim, NH Ali, A Al-Taie, DM Majed
    Baghdad Science Journal 22 (6) , 2025
    2025
  • Predicting Students' Performance by Using Data Mining Methods
    A Jassim, A Al-Taie, ZS Naama
    Journal of Kufa for Mathematics and Computer 10 (2), 10-15 , 2023
    2023
  • Combining rules using local statistics and uncertainty estimates for improved ensemble segmentation
    A Al-Taie, HK Hahn, L Linsen
    Pattern Recognition and Image Analysis 27 (3), 444-457 , 2017
    2017
  • Fast Uncertainty-Guided Fuzzy C-Means Segmentation of Medical Images
    A Al-Taie, HK Hahn, L Linsen
    Visualization in Medicine and Life Sciences III: Towards Making an Impact, 25-48 , 2016
    2016