Machine Learning Approach for Comparative Analysis of De-Noising Techniques in Ultrasound Images of Ovarian Tumors

Ovarian abnormalities such ovarian cysts, tumors, and polycystic ovaries are one of the serious disorders affecting women's health. In ultrasound imaging of ovarian abnormalities, noise during capturing of the image and its transmission process frequently corrupts the image. In order to make the best judgments possible at the appropriate moment, ovarian cysts in females must be accurately detected.  In computer aided diagnosis of ovarian tumors, preprocessing is a very important step. In preprocessing, de-noising of medical images is a particularly a difficult task since it must be done while maintaining image features that are essential for diagnosis. In this research work we are using various denoising filters on ultrasound images of ovarian tumors. For different noise denoising techniques, performance measures like MSE, PSNR, SSIM, and UQI etc. are calculated. According to experimental findings, Block matching 3-D filter outperforms all other methods. Radiologists can better diagnose the condition with the use of this computer-assisted system

Hybrid Speckle Noise Reduction Method for Abdominal Circumference Segmentation of Fetal Ultrasound Images

Fetal biometric size such as abdominal circumference (AC) is used to predict fetal weight or gestational age in ultrasound images. The automatic biometric measurement can improve efficiency in the ultrasonography examination workflow. The unclear boundaries of the abdomen image and the speckle noise presence are the challenges for the automated AC measurement techniques. The main problem to improve the accuracy of the automatic AC segmentation is how to remove noise while retaining the boundary features of objects. In this paper, we proposed a hybrid ultrasound image denoising framework which was a combination of spatial-based filtering method and multiresolution based method.  In this technique, an ultrasound image was decomposed into subbands using wavelet transform. A thresholding technique and the anisotropic diffusion method were applied to the detail subbands, at the same time the bilateral filtering modified the approximation subband. The proposed denoising approach had the best performance in the edge preservation level and could improve the accuracy of the abdominal circumference segmentation

Disleksi bukan satu penyakit

Disleksia bukan satu istilah yang asing dalam masalah pembelajaran. Terdapat banyak sumber yang rancak membincang dan uar-uarkan. maklumat mengenai masalah dan cara menangani disleksia. Merujuk Sheila Devaraj dan Samsilah Roslan (2006), disleksia hanya diperkenalkan secara formal di Malaysia pada tahun 1993 menerusi Seminar Kebangsaan Pengenalan kepada Disleksia yang dianjurkan oleh Rotary Club Gombak. Dalam kajian ini, penyelidik telah mengambil seramai 20 orang kanak-kanak di bawah Persatuan Disleksia Malaysia yang terletak di sekitar Kuala Lumpur sebagai sampel. Dua kaedah digunakan dalam mendapatkan data iaitu secara temu bual dan pemerhatian. Kaedah ini dianggap bersuaian kerana gambaran secara seluruhan dapat dilihat terhadap proses pengajaran dan pembelajaran pelajar-pelajar disleksia. Persoalan kajian ini ialah 1) Apakah ciri-ciri masalah yang ketara yang dihidapi oleh pelajar-pelajar disleksia dalam pembelajaran? dan 2) Apakah langkah-langkah yang belch diambil untuk membantu pelajar-pelajar bagi mengatasi masalah disleksia

Machine Learning And Image Processing For Noise Removal And Robust Edge Detection In The Presence Of Mixed Noise

The central goal of this dissertation is to design and model a smoothing filter based on the random single and mixed noise distribution that would attenuate the effect of noise while preserving edge details. Only then could robust, integrated and resilient edge detection methods be deployed to overcome the ubiquitous presence of random noise in images. Random noise effects are modeled as those that could emanate from impulse noise, Gaussian noise and speckle noise. In the first step, evaluation of methods is performed based on an exhaustive review on the different types of denoising methods which focus on impulse noise, Gaussian noise and their related denoising filters. These include spatial filters (linear, non-linear and a combination of them), transform domain filters, neural network-based filters, numerical-based filters, fuzzy based filters, morphological filters, statistical filters, and supervised learning-based filters. In the second step, switching adaptive median and fixed weighted mean filter (SAMFWMF) which is a combination of linear and non-linear filters, is introduced in order to detect and remove impulse noise. Then, a robust edge detection method is applied which relies on an integrated process including non-maximum suppression, maximum sequence, thresholding and morphological operations. The results are obtained on MRI and natural images. In the third step, a combination of transform domain-based filter which is a combination of dual tree – complex wavelet transform (DT-CWT) and total variation, is introduced in order to detect and remove Gaussian noise as well as mixed Gaussian and Speckle noise. Then, a robust edge detection is applied in order to track the true edges. The results are obtained on medical ultrasound and natural images. In the fourth step, a smoothing filter, which is a feed-forward convolutional network (CNN) is introduced to assume a deep architecture, and supported through a specific learning algorithm, l2 loss function minimization, a regularization method, and batch normalization all integrated in order to detect and remove impulse noise as well as mixed impulse and Gaussian noise. Then, a robust edge detection is applied in order to track the true edges. The results are obtained on natural images for both specific and non-specific noise-level

SPECKLE NOISE REDUCTION USING ADAPTIVE MULTISCALE PRODUCTS THRESHOLDING

Image denoising is an essential preprocessing technique in image acquisition systems. For instance, in ultrasound (US) images, suppression of speckle noise while preserving the edges is highly preferred. Thus, in this paper denoising the speckle noise by using wavelet-based multiscale product thresholding approach is presented. The underlying principle of this technique is to apply dyadic wavelet transform and performs the multiscale products of the wavelet transform. Then, an adaptive threshold is calculated and applied to the multiscale products instead of applying it on wavelet coefficient. Thereafter, the performance of the proposed technique is compared with other denoising techniques such as Lee filter, boxcar filter, linear minimum mean square error (LMMSE) filter and median filter. The result shows that the proposed technique gives a better performance in terms of PNSR and ENL value by an average gain of 1.22 and 1.8 times the noisy on, respectively and can better preserved image detail

Assessment of speckle denoising filters for digital holography using subjective and objective evaluation models

Digital holography is an emerging imaging technique for displaying and sensing three dimensional objects. The perceived image quality of a hologram is frequently corrupted by speckle noise due to coherent illumination. Although several speckle noise reduction methods have been developed so far, there are scarce quality assessment studies to address their performance and they typically focus solely on objective metrics. However, these metrics do not reflect the visual quality perceived by a human observer. In this work, the performance of four speckle reduction algorithms, namely the nonlocal means, the Lee, the Frost and the block matching 3D filters, with varying parameterizations, were subjectively evaluated. The results were ranked with respect to the perceived image quality to obtain the mean opinion scores using pairwise comparison. The correlation between the subjective results and twenty different no-reference objective quality metrics was evaluated. The experiment indicates that block matching 3D and Lee are the preferred filters, depending on hologram characteristics. The best performing objective metrics were identified for each filter.info:eu-repo/semantics/publishedVersio

Tracking of dynamic arm motion estimation and interaction with fuzzy control

Considerable effort has been put toward the development of intelligent and natural interfaces be- tween users and computer systems. In line with this endeavor, several modes of information (e.g., visual, au- dio, and pen) that are used either individually or in combination have been proposed. Multi-projector dis- play systems are gaining popularity for use in immersive virtual reality applications and scientific visualization. While recent work has addressed the issues of human interfaces to hide the distributed nature of those sys- tems, there has been relatively little work on natural interactive modalities. In this paper, based on the dis- crete characteristics of node distribution and the spatio- temporal coherence of the users movement, we propose a non-contact interactive solution for multi-projector display system. Utilizing a virtual three-dimensional interactive rectan- gular parallelepiped, we establish correspondence be- tween the virtual scene and the users arm position in- formation. For robustly tracking the users arm position, an arm motion estimation method is designed based on the fuzzy predictive control fuzzy Mamdani algorithm theory. To verify the efficiency and accuracy of the pro- posed method, we use a Kalman filter algorithm to test stabilize the output