Browsing by Author "Barmak, Honarvar Shakibaei Asli"

Now showing 1 - 3 of 3
  • Thumbnail Image
    ItemOpen Access
    Early breast cancer detection using artificial intelligence techniques based on advanced image processing tools
    (MDPI , 2024-09-09) Zhu, Zede; Sun, Yiran; Barmak, Honarvar Shakibaei Asli
    The early detection of breast cancer is essential for improving treatment outcomes, and recent advancements in artificial intelligence (AI), combined with image processing techniques, have shown great potential in enhancing diagnostic accuracy. This study explores the effects of various image processing methods and AI models on the performance of early breast cancer diagnostic systems. By focusing on techniques such as Wiener filtering and total variation filtering, we aim to improve image quality and diagnostic precision. The novelty of this study lies in the comprehensive evaluation of these techniques across multiple medical imaging datasets, including a DCE-MRI dataset for breast-tumor image segmentation and classification (BreastDM) and the Breast Ultrasound Image (BUSI), Mammographic Image Analysis Society (MIAS), Breast Cancer Histopathological Image (BreakHis), and Digital Database for Screening Mammography (DDSM) datasets. The integration of advanced AI models, such as the vision transformer (ViT) and the U-KAN model—a U-Net structure combined with Kolmogorov–Arnold Networks (KANs)—is another key aspect, offering new insights into the efficacy of these approaches in different imaging contexts. Experiments revealed that Wiener filtering significantly improved image quality, achieving a peak signal-to-noise ratio (PSNR) of 23.06 dB and a structural similarity index measure (SSIM) of 0.79 using the BreastDM dataset and a PSNR of 20.09 dB with an SSIM of 0.35 using the BUSI dataset. When combined filtering techniques were applied, the results varied, with the MIAS dataset showing a decrease in SSIM and an increase in the mean squared error (MSE), while the BUSI dataset exhibited enhanced perceptual quality and structural preservation. The vision transformer (ViT) framework excelled in processing complex image data, particularly with the BreastDM and BUSI datasets. Notably, the Wiener filter using the BreastDM dataset resulted in an accuracy of 96.9% and a recall of 96.7%, while the combined filtering approach further enhanced these metrics to 99.3% accuracy and 98.3% recall. In the BUSI dataset, the Wiener filter achieved an accuracy of 98.0% and a specificity of 98.5%. Additionally, the U-KAN model demonstrated superior performance in breast cancer lesion segmentation, outperforming traditional models like U-Net and U-Net++ across datasets, with an accuracy of 93.3% and a sensitivity of 97.4% in the BUSI dataset. These findings highlight the importance of dataset-specific preprocessing techniques and the potential of advanced AI models like ViT and U-KAN to significantly improve the accuracy of early breast cancer diagnostics.
  • Thumbnail Image
    ItemOpen Access
    Four-term recurrence for fast Krawtchouk moments using Clenshaw algorithm
    (MDPI, 2023-04-12) Barmak, Honarvar Shakibaei Asli; Rezaei, Maryam Horri
    Krawtchouk polynomials (KPs) are discrete orthogonal polynomials associated with the Gauss hypergeometric functions. These polynomials and their generated moments in 1D or 2D formats play an important role in information and coding theories, signal and image processing tools, image watermarking, and pattern recognition. In this paper, we introduce a new four-term recurrence relation to compute KPs compared to their ordinary recursions (three-term) and analyse the proposed algorithm speed. Moreover, we use Clenshaw’s technique to accelerate the computation procedure of the Krawtchouk moments (KMs) using a fast digital filter structure to generate a lattice network for KPs calculation. The proposed method confirms the stability of KPs computation for higher orders and their signal reconstruction capabilities as well. The results show that the KMs calculation using the proposed combined method based on a four-term recursion and Clenshaw’s technique is reliable and fast compared to the existing recursions and fast KMs algorithms.
  • Thumbnail Image
    ItemOpen Access
    Moment-based image enhancement for brain tumor health monitoring
    (Cranfield University, 2022-11-08) Barmak, Honarvar Shakibaei Asli ; Asli, Shakibaei; Wang, Yuhan
    Since the stable increasing incidence of brain tumors in recent years, brain tumor detection and monitoring are being attached with more impor tance. To implement the image feature extraction approach for the current imaging system, the image mo- ments' concepts are introduced. The theory of image moments is applied for brain image analysis, which is a weighted average of the image pixels' intensities representing the characteristics of the mentioned brain images with potential tumor diseases. This paper describes several continuous and discrete moments in terms of the polynomial kernels used and distinguishes their differences regarding image recon struction and enhancement. The experimental results confirm that the proposed discrete Tchebichef and Krawtchouk moments are more robust in terms of noise and blur reduction than the existing methods, such as the Wiener filter. This process explains how th e proposed image moments technique can be applied in the health monitoring of brain tumors via image analysis procedures.