Speckle Noise Analysis - variant
Download 2MB Request a copy Abstract Penggunaan pencitraan ultrasonografi dalam diagnosis medis telah ditentukan dengan baik karena sifatnya yang non-invasive, biaya rendah, kemampuan membentuk pencitraan secara real time, dan kemajuan yang terus menerus berkembang dalam kualitas citra yang dihasilkan. Namun, dalam pengambilan citra ultrasonografi masih terdapat noise yang menyebabkan penurunan dari kualitas citra yang dihasilkan, salah satu yang paling mengganggu adalah speckle noise. Speckle noise telah lama diketahui sebagai suatu faktor yang membatasi pendeteksian dari citra ultrasound, terutama pada hasil citra dengan kontras yang rendah. Serta apabila dokter ingin mendapatkan batasan-batasan dari tepian dan pergerakan katup citra jantung dari hasil filter, maka digunakanlah metode Canny Edge Detection. Terdapat beberapa parameter yang digunakan sebagai analisa statistik kuantitatif untuk melihat perbedaan dari citra ultrasonografi yang telah direstorasi dengan citra ultrasonografi asli yang dijadikan sebagai referensi tersebut.Speckle Noise Analysis - And
Optics and photonics Abstract Based on porous silicon PSi microarray images, we propose a new method called the phagocytosis algorithm PGY for removing the influence of speckle noise on image gray values. In a theoretical analysis, speckle noise of different intensities is added to images, and a suitable denoising method is developed to restore the image gray level. This method can be used to reduce the influence of speckle noise on the gray values of PSi microarray images to improve the accuracy of detection and increase detection sensitivity. In experiments, the method is applied to detect refractive index changes in PSi microcavity images, and a good linear relationship between the gray level change and the refractive index change is obtained. In addition, the algorithm is applied to a PSi microarray image, and good results are obtained. Download PDF Introduction Biochips microarrays are a very important technology in the field of life science. Because of their excellent characteristics, extensive application prospects and rapid development, biochips have demonstrated great application value in disease diagnosis, drug development, genetic modification, allergen detection, and environmental protection since they were first proposed in the s. At present, in some biochip-based detection methods, the target molecules or probe molecules are marked with fluorescent markers 1 , 2 , 3. Speckle Noise AnalysisSpeckle Noise Analysis Video
Speckle Denoising in Ultrasound Images MATLAB ProjectsGet PDF Abstract Speckle noise and retinal shadows within OCT B-scans occlude important edges, fine textures and deep tissues, preventing accurate and robust diagnosis by go here and clinicians. We developed a single process that successfully removed both noise and retinal shadows from unseen single-frame B-scans within Mean average Speckle Noise Analysis magnitude AGM for the proposed algorithm was The proposed algorithm reduces the necessity for long image acquisition times, minimizes expensive hardware requirements and reduces motion artifacts in OCT images. Introduction Optical coherence tomography OCT is a well-established, noninvasive clinical imaging tool for in vivo viewing of cross-sectional images of optical nerve head ONH tissues with micrometer resolution [ 1 ].
Although there have been vast improvements in imaging resolution, speed, and depth of OCT imaging, some limitations exist.
1. Introduction
Speckle noise is a multiplicative noise inherent in coherence imaging and is caused by multiple forward and backward scattering of light waves. It frequently reduces contrast and the grainy speckle Nkise pattern has been found to limit both the axial and lateral effective image resolution [ 3 ]. Subtle but important morphological details, such as individual tissue layers [ 4 http://pinsoftek.com/wp-content/custom/stamps/chinese-culture-in-yuo-nian.php 6 ] are prevented from being identified and observed [ 7 ], making speckle noise Speckle Noise Analysis to clinical diagnosis [ 8 ]. The most common speckle removal approach adopted in commercial OCT machines is B-scan averaging [ 9 ].
Introduction
Spectralis machines Heidelberg Engineering, Heidelberg, Germany use an algorithm called automatic real time ART to combine multiple B-scans which have Speckle Noise Analysis captured at the same location [ 10 ]. In the ART algorithm, the signal-to-noise-ratio of the image is continuously increasing with approximately the square root of the number of averaged single B-scans. Although high quality images can be produced using this technique, the longer scan durations 3. This is mostly due to eye or head motions during scanning [ 12 ].
The inability of elderly or young patients to remain fixated for long periods of time further render this technique difficult to obtain 3D scans of the ONH [ 13 ] of relatively good quality. Furthermore, ART does not prevent OCT signals obtained from locations beneath retinal blood vessels from being significantly diminished due to the scattering at the blood flowing through retinal blood vessels.
This phenomenon produces artifacts in OCT images known as retinal shadows. These artifacts appear perpendicular to retinal layers, interrupting tissue layer continuity and causing errors in segmentation [ 14 ].
This in Spckle leads to inaccurate extraction of important structural metrics such as thickness of the retinal nerve fiber layer RNFLwhich is important in glaucoma monitoring [ 15 ]. Retinal shadows also reduce visibility of deep structures such as the anterior and posterior boundaries of the lamina cribrosa LCas weak, reflected signals from these structures are further attenuated by the lower incident light intensity within retinal shadows [ 16 ]. Recently, deep learning techniques have shown Speckle Noise Analysis in reducing speckle noise. Mao et al. Later inMa et al.
Devalla et al. Many other works attempted to remove speckle noise with varying success, with a common recognition of the major quality degrading factor that speckle noise inflict on OCT images [ 20 — 22 ]. Some have attempted to remove retinal shadows as well. InGirard et al. Later, inVupparaboina et al. Our more recent work [ 24 Speckle Noise Analysis used a weighted custom loss function that removed shadows from ART images and illuminated faint features within retinal shadows.
However, the above-mentioned algorithms require high quality images free from speckle noise and motion artifacts to function well, preventing users in possession of single-frame images and low-cost hardware from availing themselves to this technology. The presence of speckle noise, motion artifacts, and retinal shadows often interact and overlap, complicating processes that attempt to alleviate and remove these quality degrading phenomena [ 425 ]. Such attempts are often tedious and prone to errors, because multiple separate processes need to Speckle Noise Analysis together to remove each artifact individually, with the ordering of artifact removal causing issues for the Speckle Noise Analysis processes.
In this study, we aimed to develop an algorithm to remove both speckle noise and retinal shadows within a single step. By doing so, we will be able to reduce the cost of OCT devices by using simpler OCT imaging hardware enhanced by go here. Methods 2.]
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