Over the years, the field of Medical Imagology has gained considerable importance. The number of neuroimaging studies conducted using functional magnetic resonance imaging (fMRI) has been exploding in recent years. fMRI survey gives to rise to large amounts of noisy data with a complex spatiotemporal correlation structure. Statistics play great role in clarifying the features of the data and gain results that can be used and explain by neuroscientists. Several types of artifacts can happen through a functional magnetic resonance imaging (fMRI) scanner Because of software or hardware problems, physical limitation or human physiologic phenomenon. Several of them can negatively affect di
Image registration plays a significant role in the medical image processing field. This paper proposes a development on the accuracy and performance of the Speeded-Up Robust Surf (SURF) algorithm to create Extended Field of View (EFoV) Ultrasound (US) images through applying different matching measures. These measures include Euclidean distance, cityblock distance, variation, and correlation in the matching stage that was built in the SURF algorithm. The US image registration (fusion) was implemented depending on the control points obtained from the used matching measures. The matched points with higher frequency algorithm were proposed in this work to perform and enhance the EFoV for the US images, since the maximum accurate matching po
... Show MoreMedical Ultrasound (US) has many features that make it widely used in the world. These features are safety, availability and low cost. However, despite these features, the ultrasound suffers from problems. These problems are speckle noise and artifacts. In this paper, a new method is proposed to improve US images by removing speckle noise and reducing artifacts to enhance the contrast of the image. The proposed method involves algorithms for image preprocessing and segmentation. A median filter is used to smooth the image in the pre-processing. Additionally, to obtain best results, applying median filter with different kernel values. We take the better output of the median filter and feed it into the Gaussian filter, which then
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