Normal Shrink Research Articles

Self-sparse fuzzy clustering with automatic region merging approach for image segmentation

Image segmentation based on fuzzy clustering has made enormous progress in recent years. However, the performance of existing methods degrades in noisy conditions and suffers from losses of foregrounds and the emergence of backgrounds. To settle these issues, in this paper, two methods – proposed method 1 and proposed method 2 – based on self-sparse fuzzy clustering algorithm (SSFCA) are proposed. The proposed methods use a normal shrink (NS) denoising algorithm as a pre-processing step for the suppression of noise. The output of NS undergoes the segmentation block SSFCA to get the segmentation output. The segmentation output is then subjected to a post-processing block for the removal of isolated pixels. In proposed method 1, the morphological cleaning operation acts as a post-processing block, whereas it is the morphological cleaning operation along with the automatic region merging approach in proposed method 2. The automatic region merging is achieved using connected component filtering based on the area density balance strategy (CCF-ADB). The proposed methods are tested on different datasets, and experimental results demonstrate their superior performance and effectiveness as compared to the state-of-art algorithms.

Robust Fuzzy C-Means Clustering Algorithm Based on Normal Shrink and Membership Filtering for Image Segmentation

The robustness and effectiveness of image segmentation using the FCM algorithm can be improved by incorporating local spatial information into the FCM method, which is particularly noise-tolerant. However the introduction of local spatial information gives more computational complexity. Hence to overcome this problem an improved FCM clustering method is proposed which is based on a normal shrink algorithm with membership filtering. The Proposed method gives a faster and more robust result in comparison to FCM. Firstly, a normal shrink denoising algorithm is introduced to preserve the image details and noise immunity. Secondly, membership filtering is introduced, which depends only on the local spatial neighboring properties of the matrix called the membership partition matrix. The Proposed method is faster and simpler as it does not calculate the distance between pixels and cluster centers and between local spatial neighboring. Also, it is very efficient for noise immunity.

Using Discrete Wavelet Transform and Wiener filter for Image De-nosing

In this paper we proposed an algorithm for image de-nosing based on; the two level discrete wavelet transform (DWT), and Wiener filter, also this paper describe estimate noise power. At first The DWT transform noisy image into sub-bands, consist of lowfrequencyand high-frequencies, and then estimate noise power for each sub-band. The noise power is computed through two important computations; compute square of variance for each sub-band then compute the mean of the variance. After compute the variance apply the wiener filter on each sub-band by using local window nn , finally perform inverse DWT to obtain de-noised image. Our algorithm tested on the two color images and also compared with Normal Shrink filter and Wiener filter.

Image Denoising Using Multiwavelet Transform with Different Filters and Rules

<p class="0abstract">Image denoising is a technique for removing unwanted signals called the noise, which coupling with the original signal when transmitting them; to remove the noise from the original signal, many denoising methods are used. In this paper, the Multiwavelet Transform (MWT) is used to denoise the corrupted image by Choosing the HH coefficient for processing based on two different filters Tri-State Median filter and Switching Median filter. With each filter, various rules are used, such as Normal Shrink, Sure Shrink, Visu Shrink, and Bivariate Shrink. The proposed algorithm is applied Salt& pepper noise with different levels for grayscale test images. The quality of the denoised image is evaluated by using Peak Signal to Noise Ratio (PSNR). Depend on the value of PSNR that explained in the result section; we conclude that the (Tri-State Median filter) is better than (Switching Median filter) in denoising image quality, according to the results of applying rules the result of the Shrinking rule for each filter shows that the best result using first the Bivariate Shrink.</p>

Improving the performance of empirical mode decomposition via Tsallis entropy: Application to Alzheimer EEG analysis.

Alzheimer is a degenerative disorder that attacks neurons, resulting in loss of memory, thinking, language skills, and behavioral changes. Computer-aided detection methods can uncover crucial information recorded by electroencephalograms. A systematic literature search presents the wavelet transform as a frequently used technique in Alzheimer's detection. However, it requires a defined basis function considered a significant problem. In this work, the concept of empirical mode decomposition is introduced as an alternative to process Alzheimer signals. The performance of empirical mode decomposition heavily relies on a parameter called threshold. In our previous works, we found that the existing thresholding techniques were not able to highlight relevant information. The use of Tsallis entropy as a thresholder is evaluated through the combination of empirical mode decomposition and neural networks. Thanks to the extraction of better features that boost the classification accuracy, the proposed approach outperforms the state-of-the-art in terms of peak signal to noise ratio and root mean square error. Hence, our methodology is more likely to succeed than methods based on other landmarks such as Bayes, Normal and Visu shrink. We finally report an accuracy rate of 80%, while the aforementioned techniques only yield performances of 65%, 60% and 40%, respectively.

An intelligent validation system for diagnostic and prognosis of ultrasound fetal growth analysis using Neuro-Fuzzy based on genetic algorithm

Acquisition of the standard plane is the prerequisite of biometric measurement and diagnosis during the ultrasound (US) examination. Based upon the analysis of existing algorithms for the automatic fetal development measurement, a new algorithm known as Neuro-Fuzzy based on genetic algorithm is developed. Firstly, the fetal ultrasound benchmark image is auto-pre-processed using Normal Shrink Homomorphic technique. Secondly, the features are extracted using Gray Level Co-occurrence Matrix (GLCM), Grey Level Run Length Matrix (GLRLM), Intensity Histogram (IH) and Rotation Invariant Moments (IM). Thirdly, Neuro-Fuzzy using Genetic approach is used to distinguish among the fetus growth as abnormal or normal. Experimental results using benchmark and live dataset demonstrate that the developed method achieves an accuracy of 97% as compared to the state-of- art methods in terms of parameters such as Sensitivity, Specificity, Recall, F-Measure &Precision Rate. The use of area under the receiver of characteristics(AUC) and confusion matrix as assessment indicators is also cross-validated using various classification methods by achieving best accuracy rate of Support Vector Machine (SVM) i.e. 98.7% as compare to other classification methods such as KNN, Ensemble methods, Linear Discriminant Analysis(LDA) and Decision Tree whereas ROC curve covers 0.9992 SVM.

Hybridization of Adaptive Wavelet Shrinkage with Guided Filter for Speckle Reduction in Ultrasound Images

Speckle noise is introduced in ultrasound images during acquisition and it is inherited in all coherent sources. Speckle makes area of interest difficult to study for human observer and for automated diagnostic machines hence the interpretation may adversely affect the results. Numerous post processing de-speckling algorithms have been developed but along with speckle reduction the challenging task is to preserve edges or evincive detail. In this study, authors have developed a hybrid method using adaptive wavelet shrinkage and guided filter which has given us remarkable results in de-speckling and edge preservation. In the paper proposed method is compared with existing filters like Lee, Kuan, Frost, Guided filter and wavelet shrinkage techniques Visu Shrink, Bayes Shrink and Normal Shrink. The results of proposed technique were visually better than existing de-speckling techniques. The quantitative analysis also shows eminence of proposed technique over existing techniques when compared using different image quality metrics.

A Combined Detail Enhancing Algorithm and Texture Feature Extraction Method for Supervised Classification of Remote Sensing Images

In this paper, we propose a supervised classification in multispectral satellite images based on a novel detail enhancing texture feature extraction algorithm. The multispectral training and test images are first given for pre-processing, which is decomposed into low-pass approximation and high-pass multi-directional subbands by wavelet based contourlet transform. High pass subbands are easily interfered by noise. Based upon the Normal Shrink technique, thresholding is applied in high frequency images to eliminate the noise. The intra and inter scale fusion rule is used to combine the approximation and detail subbands to form the enhanced image. The co-occurrence features are calculated by forming the gray level co-occurrence matrix on training and test images. Mahalanobis distance classifier is applied on the training and test data sets for effective classification. The experiment result shows that the overall accuracy is improved to 2.2% for (Test-1) 2% for (Test-2) and 3.2 5% for (Test-3) and kappa coefficient is improved to 0.02 for (Test-1) image 0.03 for (Test-2) image and 0.03 (Test-3) image.

Review of Various Image Restoration and Denoising Methods Underwater Images

The paper presents a denoising method, based on Daubechies Wavelet transform and filters, for underwater images. The method here adapts itself to various types of image noise as well as factor that are to be derived to estimate the noise-free coefficients. In the preference of the images in any field, a single parameter can be used to balance the preservation of relevant details against the degree of noise reduction. Edges play a important role in image representation, one effective means to enhance spatial resolution is to enhance the edges. DWT, a time-scale representation of the digital signal is obtained using digital filtering techniques. Effectiveness of this method is compared with all other techniques such as median ,wiener, bayes shrink and normal shrink methods for removing noise.This paper presents a review of some significant work in the area of denoising. We will introduce the wavelet and other filters methods with homomorphics filters multi-scale analysis framework and summarize related research work in this area.

A novel wavelet seismic denoising method using type II fuzzy

Wavelet based denoising of the observed non stationary time series earthquake loading has become an important process in seismic analysis. The process of denoising ensures a noise free seismic data, which is essential to extract features accurately (max acceleration, max velocity, max displacement, etc.). However, the efficiency of wavelet denoising is decided by the identification of a crucial factor called threshold. But, identification of optimal threshold is not a straight forward process as the signal involved is non-stationary. i.e. The information which separates the wavelet coefficients that correspond to the region of interest from the noisy wavelet coefficients is vague and fuzzy. Existing works discount this fact. In this article, we have presented an effective denoising procedure that uses fuzzy tool. The proposal uses type II fuzzy concept in setting the threshold. The need for type II fuzzy instead of fuzzy is discussed in this article. The proposed algorithm is compared with four current popular wavelet based procedures adopted in seismic denoising (normal shrink, Shannon entropy shrink, Tsallis entropy shrink and visu shrink).It was first applied on the synthetic accelerogram signal (gaussian waves with noise) to determine the efficiency in denoising. For a gaussian noise of sigma=0.075, the proposed type II fuzzy based denoising algorithm generated 0.0537 root mean square error (RMSE) and 16.465 signal to noise ratio (SNR), visu shrink and normal shrink could be able to give 0.0682 RMSE with 14.38 SNR and 0.068 RMSE with 14.2 SNR, respectively. Also, Shannon and Tsallis generated 0.0602 RMSE with 15.47 SNR and 0.0610 RMSE with 15.35 SNR, respectively. The proposed method is then applied to real recorded time series accelerograms. It is found that the proposal has shown remarkable improvement in smoothening the highly noisy accelerograms. This aided in detecting the occurrence of ‘P’ and ‘S’ waves with lot more accuracy. Interestingly, we have opened a new research field by hybriding fuzzy with wavelet in seismic denoising.

Image Denoising Algorithm Using Second Generation Wavelet Transformation and Principle Component Analysis

This study proposes novel image denoising algorithm using combination method. This method combines both Wavelet Based Denoising (WBD) and Principle Component Analysis (PCA) to increase the superiority of the observed image, subjectively and objectively. We exploit the important property of second generation WBD and PCA to increase the performance of our designed filter. One of the main advantages of the second generation wavelet transformation in noise reduction is its ability to keep the signal energy in small amount of coefficients in the wavelet domain. On the other hand, one of the main features of PCA is that the energy of the signal concentrates on a very few subclasses in PCA domain, while the noise’s energy equally spreads over the entire signal; this characteristic helps us to isolate the noise perfectly. Our algorithm compares favorably against several state-of-the- art filtering systems algorithms, such as Contourlet soft thresholding, Scale mixture by WT, Sparse 3D transformation and Normal shrink. In addition, the combined algorithm achieves very competitive performance compared with the traditional algorithms, especially when it comes to investigating the problem of how to preserve the fine structure of the tested image and in terms of the computational complexity reduction as well.

Empirical Mode Decomposition and Normal Shrink Tresholding for Speech Denoising

In this paper a signal denoising scheme based on Empirical mode decomposition (EMD) is presented. the noisy signal is decomposed in an adaptive manner by the EMD algorithm which allows to obtain intrinsic oscillatory called Intrinsic mode functions (IMFs)component by a process called sifting process. The basic principle of the method is to decompose a speech signal corrupted by additive white Gaussian random noise into segments each frame is categorised as either signal-dominant or noise-dominant then reconstruct the signal with IMFs signal dominant frame previously filtered or thresholded.It is shown, on the basis of intensivesimulations that EMD improves the signal to noise ratio and address the problem of signal degradation. The denoising method is applied to real signal with different noise levels and the results compared to Winner and universal threshold of DONOHO and JOHNSTONE [11] with soft and hard tresholding.Theeffect of level noise value on the performances of the proposed denoising is analysed.

An Image Denoising Framework with Multi-resolution Bilateral Filtering and Normal Shrink Approach

In this study, an image denoising algorithm is presented, which takes into account wavelet thresholding and bilateral filtering in transform domain. The proposed algorithm gives an extension of the bilateral filter i.e., multiresolution bilateral filter, in which bilateral filtering is applied to the approximation sub bands and normal shrink is used for thresholding the wavelet coefficients of the detail sub bands of an image decomposed using a wavelet filter bank up to 2-level of decomposition. The algorithm is tested against ultrasound image of gall bladder corrupted by different types of noise namely, gaussian, speckle, poisson and impulse. The result shows that with increase in decomposition levels the proposed method is effective in eliminating noise but gives overly smoothed image. The algorithm outperforms with speckle and poisson noise at 2- level decomposition in terms of PSNR.

A Novel NSCT based Illuminant Invariant Extraction with Optimized Edge Detection Technique for Face Recognition

ABTRACT A novel integrated approach for resolving the effect of illumination changes on face recognition is proposed. This face recognition system comprises feature extraction, feature selection and recognition. For feature extraction, a normal shrink filter in NSCT domain denoising technique is applied which produces illuminant invariant for the given image. In the second phase, to capture the important geometrical structures and to reduce the feature dimensionality Ant colony Optimization algorithm is performed. This combined approach fairly detects the edges with improved quality. Finally for recognition, a graph matching algorithm is employed. This algorithm utilizes a group of feature points to explore their geometrical relationship in a graph arrangement. While applying the entire method to the yaleB database, experimental results shows that the proposed work yields the best subset of features and provides a better solution for complex illumination problems.

Estudio comparativo de técnicas de reducción de ruido en señales industriales mediante Transformada Wavelet Discreta y selección adaptativa del umbral

Noise reduction techniques are widely used for audio recording, image editing, and industrial signal processing. The idea is to reconstruct the original data from the noise-corrupted signal suppressing, all or almost all, the distortion caused by the inherent noise of the physical processes. In the present paper, we perform a comparative review of several noise reduction techniques based on adaptive threshold selection. These techniques have been extensively used for image processing. However, we aimed at evaluating their performance for industrial signal noise reduction. In particular, we analyze the behaviour of the Bayes Shrink, Normal Shrink, Modified Shrink, and Neight Shrink methods for the reduction of the Gaussian noise in industrial signals. To that aim, we perform experiments on a set of pattern signals proposed by Donoho and other representative measurements obtained from real processes in Cuban1s Nickel plants. Our results indicate that, for this kind of data, the Neigh Shrink algorithm outperforms.

Denoising of an Image Using Discrete Stationary Wavelet Transform and Various Thresholding Techniques

Image denoising has remained a fundamental problem in the field of image processing. With Wavelet transforms, various algorithms for denoising in wavelet domain were introduced. Wavelets gave a superior performance in image denoising due to its properties such as multi-resolution. The problem of estimating an image that is corrupted by Additive White Gaussian Noise has been of interest for practical and theoretical reasons. Non-linear methods especially those based on wavelets have become popular due to its advantages over linear methods. Here I applied non-linear thresholding techniques in wavelet domain such as hard and soft thresholding, wavelet shrinkages such as Visu-shrink (non-adaptive) and SURE, Bayes and Normal Shrink (adaptive), using Discrete Stationary Wavelet Transform (DSWT) for different wavelets, at different levels, to denoise an image and determine the best one out of them. Performance of denoising algorithm is measured using quantitative performance measures such as Signal-to-Noise Ratio (SNR) and Mean Square Error (MSE) for various thresholding techniques.

Wavelet based seismic signal de-noising using Shannon and Tsallis entropy

Seismograms are the vital sources of information in seismic engineering. But, these records are always contaminated with noise which has to be removed before using them in seismic applications. Recently, wavelet based techniques proved to be very effective in de-noising by achieving high SNR. However, selection of the correct threshold plays a crucial role in deciding the SNR value. It is strange that only very few thresholders exist in seismic and non-seismic studies. In this paper, we have proposed a set of novel entropy based thresholders through 2 experiments. In experiment 1, we have proposed a Shannon entropy based algorithm which has produced 11.205 SNR. In experiment 2, we used Tsallis entropy which has moderately improved the result by providing 12.23 SNR. Existing thresholders like visu and normal shrink have managed to produce 10.19 and 10.07 SNR respectively. Through our experiments, we observed that for low frequency problems (σ=0.27), the performance of both entropies matched appreciably. However, for high frequency (σ=2.7) Tsallis produced slightly better SNR and is more feasible in detecting the occurrence of P and S waves by smoothing the accelerograms.

Image Denoising using Adaptive Thresholding in Framelet Transform Domain

Noise will be unavoidable during image acquisition process and denosing is an essential step to improve the image quality. Image denoising involves the manipulation of the image data to produce a visually high quality image. Finding efficient image denoising methods is still valid challenge in image processing. Wavelet denoising attempts to remove the noise present in the imagery while preserving the image characteristics, regardless of its frequency content. Many of the wavelet based denoising algorithms use DWT (Discrete Wavelet Transform) in the decomposition stage which is suffering from shift variance. To overcome this, in this paper we proposed the denoising method which uses Framelet transform to decompose the image and performed shrinkage operation to eliminate the noise .The framework describes a comparative study of different thresholding techniques for image denoising in Framelet transform domain. The idea is to transform the data into the Framelet basis, example shrinkage followed by the inverse transform. In this work different shrinkage rules such as universal shrink(US),Visu shrink (VS), Minmax shrink(MS), Sure shrink(SS) , Bayes shrink(BS) and Normal shrink(NS) were incorporated . Results based on different noise such as Gausssian noise, Poission noise , Salt and pepper noise and Speckle noise at (??=10,20) performed in this paper and peak signal to noise ratio (PSNR) and Structural similarity index measure(SSIM) as a measure of the quality of denoising was performed.

EFFICIENT 3D PROSTATE SURFACE DETECTION FOR ULTRASOUND GUIDED ROBOTIC BIOPSY

Prostate surface detection from ultrasound images plays a key role in our recently developed ultrasound guided robotic biopsy system. However, due to the low contrast, speckle noise and shadowing in ultrasound images, this still remains a difficult task. In the current system, a 3D prostate surface is reconstructed from a sequence of 2D outlines, which are performed manually. This is arduous and the results depend heavily on the user's expertise. This paper presents a new practical method, called Evolving Bubbles, based on the level set method to semi-automatically detect the prostate surface from transrectal ultrasound (TRUS) images. To produce good results, a few initial bubbles are simply specified by the user from five particular slices based on the prostate shape. When the initial bubbles evolve along their normal directions, they expand, shrink, merge and split, and finally are attracted to the desired prostate surface. Meanwhile, to remedy the boundary leaking problem caused by gaps or weak boundaries, domain specific knowledge of the prostate and statistical information are incorporated into the Evolving Bubbles. We apply the bubbles model to eight 3D and four stacks of 2D TRUS images and the results show its effectiveness.