Browsing by Author "Msuya, Hubert"

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    Diffusion-steered denoising framework for suppressing multiplicative noise in ultrasonograms
    (African Journal of Applied Research, 2017-10-07) Kessy, Suzan; Msuya, Hubert; Kisangiri, Michael; Maiseli, Baraka J.
    Ultrasound imaging, a non-invasive and cost-effective imaging modality, is probably the most preferred diagnostic tool in medicine. Despite its merits, ultrasonograms are usually corrupted by multiplicative noise, a consequence that limits doctors to provide more accurate treatments and decisions. Attempts to address the problem have been made, but we have found little works that adopt the diffusion framework, which scholars have reported that it produces promising results in additive noise cases. In the current work, we have modified the classical Perona-Malik (PM) diffusion model to deal with multiplicative noise. Inspired by the ability of PM to restore semantically critical features, we have embedded a log-based regularization term, statistically modeled to mitigate multiplicative effects in the ultrasound images, into the modified PM. Additionally, the diffusivity kernel of PM has been re-designed to ensure that the diffusion process is properly steered. Modification of the PM kernel was achieved through integration of the half-quadratic diffusivity, which has a corresponding energy functional that is strictly convex, a promising mathematical property that encourages unique solutions and guarantees stability of the evolutionary system. Our interest is to emphasize regularization in flat image regions while maintaining plausible edges and contours. Subjective and quantitative evaluations demonstrate that the proposed model produces better results compared with some state-of-the-art methods. Even more importantly, our approach guarantees convergence, stability, and robustness when tested for a range of ultrasonograms. Probably the intriguing property of our framework is its ability to evolve a denoising image over a longer period without smudging or destroying its sensitive features. The proposed approach may further be extended and actualized in practical imaging devices.
  • Thumbnail Image
    Item
    Integration of a Low Cost Switching Mechanism into the NI ELVIS Ilab Shared Architecture Platform
    (IEEE, 2012) Msuya, Hubert; Mwambela, Alfred
    This paper presents the development of a low cost switching mechanism into the NI ELVIS platform for online laboratories based on the iLab Shared Architecture. This came up after experiencing the limitations on using NI ELVIS II+ platform in deploying discrete experiments using common measuring and testing instruments; as well as the use of different components on the same experimental setup. This requires switching mechanisms as a solution to implement those experiments. The designed mechanism in this work uses analogue switching devices with digital control inputs connected to the NI ELVIS II+ Digital writer instrument. The mechanism were successfully tested using implementation of diode rectification characteristics experiments showing the switching mechanism between different components; whereas switching between different experimental setups was shown using Low Pass Filter, High Pass Filter and Band Pass Filter setups.
  • Thumbnail Image
    Item
    Perona–Malik model with self-adjusting shape-defining constant
    (Information Processing Letters, 2018-09-01) Maiseli, Baraka; Msuya, Hubert; Kessy, Suzan; Kisangiri, Michael
    For decades, the Perona–Malik (PM) diffusion model has been receiving a considerable attention of scholars for its ability to restore detailed scenes. The model, despite its promising results, demands manual tuning of the shape-defining constant—a process that consumes time, prompts for human intervention, and limits flexibility of the model in real-time systems. Most works have tried to address other weaknesses of the PM model (non-convexity and non-monotonicity, which produce chances for instability and multiple solutions), but automating PM remains an open-ended question. In this work, we have introduced a new implementation approach that fully automates the PM model. In particular, the tuning parameters have been conditioned to ensure that the model guarantees convergence and is entirely convex over the scale-space domain. Experiments show that our implementation strategy is flexible, automatic, and achieves convincing results.