Mean Filter Research Articles

Diagnosis of Osteoporosis Using Transfer Learning in the Same Domain

This paper presents a system for diagnosing osteoporosis using x-rays by leveraging transfer learning in the same domain. The proposed system consists of phase 1 and phase 2; each phase includes several stages, as the pre-processing stage appropriately prepares the source image via noise reduction by the average filter, contrast enhancement using histogram equalization, and obtaining the region of interest by employing K-mean and edge detection, followed by the smudging stage through a mean filter with a large window size, which subsequently contributed to facilitating the diagnosis. The stages mentioned in both phases are similar. In phase 1, the model is trained on a large unlabeled x-ray dataset collected from different orthopedic centers to identify the general features of the image. In phase 2, fine-tune the trained model with the target dataset; this approach is beneficial when the target task has limited labeled data or when training a model from scratch is computationally expensive. It is worth noting that two datasets were used as target datasets. The accuracy of diagnosing osteoporosis using the proposed deep convolutional neural network (DCNN) model was 94.5 with the osteoporosis knee x-ray database (Dataset A). The accuracy of diagnosing osteoporosis using transfer learning in the same field was 98.91 when training the proposed DCNN model with a large unlabeled dataset and fine-tuning with the target database, osteoporosis knee x-ray database (Dataset A). The accuracy of diagnosing osteoporosis using the proposed DCNN model was 91.5 with the knee x-ray osteoporosis database (Dataset B). The accuracy of diagnosing osteoporosis using transfer learning in the same field was 96.61 when training the proposed DCNN model with a large unlabeled dataset and fine-tuning with the target knee x-ray osteoporosis database (Dataset B).

A novel adaptive contextual information-based 2D-histogram for image thresholding

This paper proposes a new two-dimensional (2D) histogram containing an image's adaptive local spatial contextual information. The correlation information between pixels is essential to achieve the optimal thresholds for image segmentation purposes. But it is an arduous task to find adaptive spatial information efficiently. So, an innovative adaptive local spatial information technique is proposed here to compute a novel 2D histogram. An adaptive contraharmonic mean (ACM) filter-based 2D-histogram is designed that contains contextual information by a unique proposed logical pseudo-energy (LPE) function. The order of contraharmonic filter Q is adaptively obtained using LPE, and the 2D Tsallis-Havrda-Charvat entropy is utilized to obtain the segmented results. Setting the Q value of the contraharmonic mean filter is tedious, and the proposed LPE function solves this problem. For the result assessments, the presented framework is compared with other maximum sum entropy-based methods, which are 2D-Kapur, 2D-Renyi, 2D-Tsallis, 2D-Masi and Parzen-window-based Kapur entropies-based image segmentation approaches. The proposed ACM-based Tsallis-Havrda-Charvat (Tsallis) entropy (ACM-Tsallis) shows excellent segmented results above local mean-based 2D-entropies methods and the latest Parzen-based Kapur entropy approach.

Image denoising using difference classifier and trimmed global mean filter adaptive approach

Image denoising using difference classifier and trimmed global mean filter adaptive approach

Oil spill detection on X-band marine radar images based on sea clutter fitting model

Oil spills could cause great harm to the natural environment. The ability to identify them accurately is critical for prompt response and treatment. We proposed a sea clutter fitting model of marine radar images for oil spill detection. The model is derived from the geometric structure of the marine radar, the expression of marine radar received power, and the rough surface scattering model of the sea surface. In the denoised marine radar image, the sea clutter fitting model is used to detect coarse oil spills. Then the fine measurement is carried out by mean filter, the Otsu method, and noise reduction. The proposed oil spill detection method was used on radar images sampled after an oil spill accident happened in a coastal region in Dalian, China, on July 21, 2010. The proposed method can detect oil spills without human intervention, and the extracted oil spills are accurate and consistent with visual interpretation.

Quantification of golgi dispersal and classification using machine learning models

The Golgi body is a critical organelle in eukaryotic cells responsible for processing and modifying proteins and lipids. Under certain conditions, such as stress, disease, or ageing, the Golgi structure alters. Therefore, understanding the mechanisms that regulate Golgi dispersion has significant research contributions to identifying disease. However, there is a lack of tools to quantify the Golgi dispersion datasets. In this paper, we aim to automate the process of quantification of Golgi dispersion and use extracted features to classify dispersed Golgi images from undispersed Golgi images using machine learning models. First, we collected confocal microscopy images of transiently transfected HeLa cells expressing Galactose-1-phosphate uridylyltransferase (GALT)- green fluorescent protein (GFP) to quantify Golgi dispersal and classification. For the quantification, we introduced automated image processing and segmentation by applying mean and Gaussian filters. Then we used Otsu thresholding on preprocessed images and watershed segmentation to refine the segmentation of dispersed Golgi particles. In the case of classification, we extracted features from the Golgi dispersal images and classified them into empty vector (EV) versus CARP1 ring mutant (CARP1 RM) and empty vector (EV) versus CARP1 wildtype (CARP1 WT) classes. Our approach used machine-learning models, including logistic regression, decision tree, random forest, Naive Bayes, k-Nearest Neighbor (KNN), and gradient boosting for dispersed Golgi image classification. The experiment results show that our quantification technique on Golgi dispersal images reached 65% classification accuracy when the system uses a gradient boosting classifier for EV vs. CARP1 WT classification. Furthermore, our approach achieved 65% classification accuracy using a random forest classifier for EV vs. CARP1 RM classification.

Digital Filtering Techniques Using Fuzzy-Rules Based Logic Control.

This paper discusses current formulations based on fuzzy-logic control concepts as applied to the removal of impulsive noise from digital images. We also discuss the various principles related to fuzzy-ruled based logic control techniques, aiming at preserving edges and digital image details efficiently. Detailed descriptions of a number of formulations for recently developed fuzzy-rule logic controlled filters are provided, highlighting the merit of each filter. Fuzzy-rule based filtering algorithms may be designed assuming the tailoring of specific functional sub-modules: (a) logical controlled variable selection, (b) the consideration of different methods for the generation of fuzzy rules and membership functions, (c) the integration of the logical rules for detecting and filtering impulse noise from digital images. More specifically, we discuss impulse noise models and window-based filtering using fuzzy inference based on vector directional filters as associated with the filtering of RGB color images and then explain how fuzzy vector fields can be generated using standard operations on fuzzy sets taking into consideration fixed or random valued impulse noise and fuzzy vector partitioning. We also discuss how fuzzy cellular automata may be used for noise removal by adopting a Moore neighbourhood architecture. We also explain the potential merits of adopting a fuzzy rule based deep learning ensemble classifier which is composed of a convolutional neural network (CNN), a recurrent neural networks (RNN), a long short term memory neural network (LSTM) and a gated recurrent unit (GRU) approaches, all within a fuzzy min-max (FMM) ensemble. Fuzzy non-local mean filter approaches are also considered. A comparison of various performance metrics for conventional and fuzzy logic based filters as well as deep learning filters is provided. The algorhitms discussed have the following advantageous properties: high quality of edge preservation, high quality of spatial noise suppression capability especially for complex images, sound properties of noise removal (in cases when both mixed additive and impulse noise are present), and very fast computational implementation.

Quarter match non-local means algorithm for noise removal

The notion of improving plays out in many forms in our lives. We look for better quality, faster speed, and leisurelier connections. To achieve our desired goals, we must ask questions. How to make a process stronger? How to make a process more efficient? How to make a process more effective? Image denoising plays a vital role in many professions and understanding how noise can be present in images has led to multiple denoising techniques. These techniques include total variation regularization, non-local regularization, sparse representation, and low-rank minimization just to name a few. Many of these techniques exist because of the concept of improvement. First, we have a change (problem). This change invokes thoughts and questions. How these changes occur and how they are handled play an essential role in the realization or malfunction of that process. With this understanding, first, we look to fully understand the process to achieve success. As it relates to image denoising, the non-local means is incredibly effective in image reconstruction. In particular, the non-local means filter removes noise and sharpens edges without losing too many fine structures and details. Also, the non-local means algorithm is amazingly accurate. Consequently, the disadvantage that plagues the non-local means filtering algorithm is the computational burden and it is due to the non-local averaging. In this paper, we investigate innovative ways to reduce the computational burden and enhance the effectiveness of this filtering process. Research examining image analysis shows there is a battle between noise reduction and the preservation of actual features, which makes the reduction of noise a difficult task. For exploration, we propose a quarter-match non-local means denoising filtering algorithm. The filters help to classify a more concentrated region in the image and thereby enhance the computational efficiency of the existing non-local means denoising methods and produce an enriched comparison for overlying in the restoration process. To survey the constructs of this new algorithm, the authors use the original non-local means filtering algorithm, which is coined, “State of the Art” and other selective processes to test the effectiveness and efficiency of the new model. When comparing the original non-local means with the new quarter match filtering algorithm, on average, we can reduce the computational cost by half, while improving the quality of the image. To further test our new algorithm, medical resonance (MR) and synthetic aperture radar (SAR) images are used as specimens for real-world applications.

Analisis Perbandingan Algoritme Penghapusan Noise pada Citra X-Ray Paru - Paru

Pulmonary X-ray is a medical diagnostic method used to produce internal lung images. However, the X-ray process is often interrupted when capturing images, resulting in noisy image results. This condition diminishes the clarity of information contained in the lung X-ray images. Therefore, noise removal or denoising is essential. Denoising is a fundamental image processing technique aimed at improving image quality for optimal information transmission. This study applies denoising methods to 20 datasets of pulmonary X-ray images using Median, Mean, Gaussian, Bilateral, and Wiener filters, with Python and the OpenCV Library. Error measurement for noise filtering is conducted using Peak Signal-to-Noise Ratio and Mean Square Error methods. The research results show that the median filter stands out as an excellent denoising method, outperforming others with a Peak Signal-to-Noise Ratio of 37.6444 and a Mean Square Error of 11.3339 for Salt and Pepper Noise. Keywords: Denoising; Filtering; MSE; PSNR; X-Ray.

Sintigrafik Görüntülerden Tiroid Nodülleri için Bilgisayar Destekli Tanı Sistemi

In modern medicine, image recognition via segmentation of anatomical regions and automatic classification of diseases using medical images has a growing potential role in diagnosis of various diseases. Scintigraphy of thyroid is one of the established imaging modalities for diagnosis of thyroid gland disorders. In our study, the speckle noise was reduced in the scintigraphy images with the optimized Bayesian nonlocal mean filter. The thyroid gland was automatically segmented by local based active contour method and the thyroid gland pathologies were classified with convolutional neural networks (CNN). The proposed computer aided diagnosis (CAD) system was compared with Pyramid of Histograms of Orientation Gradients (PHOG), Gray Level Co occurrence Matrix (GLCM), Local Configuration Pattern (LCP) and Bag of Feature (BoF) methods. The common pathological patterns of scintigraphic images of the thyroid gland were successfully classified by CNN with an overall success rate of 91.19%. The comparative methods were PHOG, GLCM, LCP and BoF methods which provided overall success rates of 7.61%, 86.04%, 88.91% and 85.72% respectively. The proposed CNN based automatic diagnosis system provided promising results compared to handcrafted methods.

A constructive non-local means algorithm for low-dose computed tomography denoising with morphological residual processing.

Low-dose computed tomography (LDCT) has attracted significant attention in the domain of medical imaging due to the inherent risks of normal-dose computed tomography (NDCT) based X-ray radiations to patients. However, reducing radiation dose in CT imaging produces noise and artifacts that degrade image quality and subsequently hinders medical disease diagnostic performance. In order to address these problems, this research article presents a competent low-dose computed tomography image denoising algorithm based on a constructive non-local means algorithm with morphological residual processing to achieve the task of removing noise from the LDCT images. We propose an innovative constructive non-local image filtering algorithm by means of applications in low-dose computed tomography technology. The nonlocal mean filter that was recently proposed was modified to construct our denoising algorithm. It constructs the discrete property of neighboring filtering to enable rapid vectorized and parallel implantation in contemporary shared memory computer platforms while simultaneously decreases computing complexity. Subsequently, the proposed method performs faster computation compared to a non-vectorized and serial implementation in terms of speed and scales linearly with image dimension. In addition, the morphological residual processing is employed for the purpose of edge-preserving image processing. It combines linear lowpass filtering with a nonlinear technique that enables the extraction of meaningful regions where edges could be preserved while removing residual artifacts from the images. Experimental results demonstrate that the proposed algorithm preserves more textural and structural features while reducing noise, enhances edges and significantly improves image quality more effectively. The proposed research article obtains better results both qualitatively and quantitively when compared to other comparative algorithms on publicly accessible datasets.

An error-bounded median filter for correcting ECG baseline wander.

The baseline wander (BLW) in electrocardiogram (ECG) is a common disturbance that has a significant influence on the ECG wave pattern recognition. Many methods, such as IIR filter, mean filter, etc., can be used to correct BLW; However, most of them work on the original ECG signals. Compressed ECG data are economic for data storage and transmission, and if the baseline correction can be processed on them, it will be more efficient than we decompress them first and then do such correction. In this paper, we propose a new type of median filter CM_Filter, which works on the synopses of straight lines achieved from ECG by piecewise linear approximation (PLA) under maximum error bound. In CM_Filter, a heuristic strategy "Quick-Finding" is deduced by a property of straight lines in order to get the quality-assured median values from the synopses. The extended experimental tests demonstrate that the proposed filter is very efficient in execution time, and effective for correcting both slow and abrupt ECG baseline wander.

Infrared image enhancement based on adaptive non-local filter and local contrast

High-quality infrared images urgently needed in military and civilian fields are always associated with appropriate contrast. However, the main challenge to obtain high-quality infrared images lies in enhancement of the contrast effectively without over-enhancement of the background and noise. Hereby, an effective infrared image enhancement approach is proposed and based on an adaptive non-local filter and local contrast. An input infrared image is detected by noisy adaptive detection to separate noisy pixels, which are filtered by non-local means filter to acquire denoised image. The grayscale histogram of the denoised image is divided into foreground and background based on the local minima value. The foreground part is enhanced by local contrast weighted distribution and max entropy gamma correction. The background part is processed by linear mapping to project the grayscale to an appropriate region. Finally, the enhanced infrared image is obtained by remapping the processed foreground and background histogram. Extensive experiments on public and homemade datasets demonstrate that our method achieves 46.8183 on image clarity, which expresses its superiority for infrared image enhancement.

Gradient bald vulture optimization enabled multi-objective Unet++ with DCNN for prostate cancer segmentation and detection

Prostate cancer (PCa) represents the general type of cancer and is considered the third leading reason of death worldwide. As a combined part of computer-aided detection (CAD) applications, magnetic resonance imaging (MRI) is extensively studied for the precise detection of PCa. However, various issues rely on MRI, which includes the complexity of interpretation and increased time. Thus, deep learning-based tumor detection and segmentation methods attempt to be imperative techniques for radiologists to execute their tasks more precisely. The objective is to present Gradient Bald vulture optimization (GBVO)-based Deep Convolution Neural Networks (DCNN) with U-Net++ for segmenting and detecting prostate cancer. Initially, image pre-processing is done using Non-Local Means (NLM) filter. After pre-processing, image segmentation is carried out using the proposed optimized multi-objective Unet++, where the objective function in Unet++ is modified using pixel-wise cross entropy and the Jaccard coefficient. In addition, the training of Unet++ is done using the newly designed Gradient Bald Eagle Optimization (GBEO), which is a combination of Stochastic Gradient Descent (SGD) and Bald Eagle optimization (BEO). Finally, cancer detection is done using DCNN. DCNN is trained using GBVO, which is the integration of the African Vultures Optimization Algorithm (AVOA) and GBEO. The proposed method outperformed state-of-the-art techniques. The developed method achieved the highest accuracy of 0.916, a false negative ratio (FNR) of 0.104, a false positive ratio (FPR) of 0.100, and a negative predictive value (NPV) of 0.903.

An improved fuzzy c-means-raindrop optimizer for brain magnetic resonance image segmentation

<p>The performance of healthcare systems, particularly regarding disease diagnosis and treatment planning, depends on the segmentation of medical images. Fuzzy c-means (FCM) is one of the most widely used clustering techniques for image segmentation due to its simplicity and effectiveness. FCM, on the other hand, has the disadvantages of being noise-sensitive, quickly settling on local optimal solutions, and being sensitive to initial values. This paper suggests a fuzzy c-means clustering improved with a nature-inspired raindrop optimizer for lesion extraction in brain magnetic resonance (MR) images to get around this constraint. In the preprocessing stage, the possible noises in a digital image, such as speckles, gaussian, etc., are eliminated by a hybrid filter—A combination of Gaussian, mean, and median filters. This paper presents a comparative analysis of FCM clustering and FCM-raindrop optimization (FCM-RO) approach. The algorithm performance is evaluated for images subjected to various possible noises that may affect an image during transmission and storage. The proposed FCM-RO approach is comparable to other methods now in use. The suggested system detects lesions with a partition coefficient of 0.9505 and a partition entropy of 0.0890. Brain MR images are analyzed using MATLAB software to find and extract malignancies. Image data retrieved from the public data source Kaggle are used to assess the system’s performance.</p>

A Comparative Study on Denoising from Facial Images Using Convolutional Autoencoder

Denoising is one of the most important preprocesses in image processing. Noises in images can prevent extracting some important information stored in images. Therefore, before some implementations such as image classification, segmentation, etc., image denoising is a necessity to obtain good results. The purpose of this study is to compare the deep learning techniques and traditional techniques on denoising facial images considering two different types of noise (Gaussian and Salt&Pepper). Gaussian, Median, and Mean filters have been specified as traditional methods. For deep learning methods, deep convolutional denoising autoencoders (CDAE) structured on three different optimizers have been proposed. Both accuracy metrics and computational times have been considered to evaluate the denoising performance of proposed autoencoders, and traditional methods. The utilized standard evaluation metrics are the peak signal to noise ratio (PSNR) and structural similarity index measure (SSIM). It has been observed that overall, while the traditional methods gave results in shorter times in terms of computation times, the autoencoders performed better concerning the evaluation metrics. The CDAE based on the Adam optimizer has been shown the best results in terms of PSNR and SSIM metrics on removing both types of noise.

An approach to privacy-aware image analysis on edge devices using CNNs

Abstract This paper presents our approach to a privacy preserving person detection algorithm on edge computing devices. It utilizes a commonly used neural network architecture (VGG16) to encode an image by passing it through a fixed number of layers of the network on the edge device. The resulting feature vector can then be transmitted over the network to a more powerful computer (e.g. in the cloud) to be passed through the remaining layers of VGG16. As a result, the transmitted feature vector is only an abstract representation of the image. However, our research has shown that it is in fact possible to reconstruct most images by their respecting feature vectors if a potential attacker can tap into the network transmission by using a decoder network. Our approach to mitigate that risk is to preprocess the transmitted feature vector randomly by different manipulation methods. We used methods like mean filter or random null value insertion to manipulate the feature vector before it gets transmitted and show that those methods are able to counter the reconstruction capabilities of a decoder network while still preserving the capabilities of the original VGG16 network. The actual VGG16 network was used for object detection.We used the faces in the wild dataset and utilized two different approaches to confirm our approach. First, we used the VGGFace-Network on the decoded images and tried to let it identify people from the former mentioned dataset. The second approach used a group of 26 participants who had to match the decoder image to one of five images. In our experiments, we found different combinations of manipulation and number of layers on the edge device to preserve the detection capability of VGG16 (e.g. object detection) while preventing VGGFace and the participating group from identifying the shown people.

Fast In-Loop Single-Tube Open-Circuit Fault Diagnosis for Voltage Source Inverter Based on Duality of Current-Time Pairs

In this article, the method extracting fault characteristics by utilizing the duality of current-time pairs is proposed to diagnose the single switch tube open-circuit (OC) fault for the inverter in the traction motor drive system. This method develops a neural-network-liked structure for OC fault diagnosis. The input layer is a fixed-period integral mean (FPIM) filter within one carrier cycle to smooth sampled discrete current-time pairs for effectively counteracting current PWM-notches and noise interference. The filtered current-time pairs are fed forward to the middle layer including functional activation functions. One of them is to normalize the current such that the diagnosis algorithm can be self-adaptive to different loads. Another one is to eliminate the singular problem near the current peak. Finally, the diagnostic functions are constructed by the dual-difference of discrete current-time pairs to quickly diagnose and locate the single-tube OC fault. It does not require additional sensors and other auxiliary detection circuits, and can accurately locate faults with few current signal nodes. It greatly reduces fault detection time and avoids triggering a vicious chain reaction due to detection delays. In addition, it also has excellent robustness under varying frequency conditions since it is only concerned with the local property. This algorithm can be embedded in the closed-loop system due to its simple structure and low computation and storage burden. The effectiveness of the proposed method is verified through experiments.

A Method for Enhancing PET Scan Images Using Nonlocal Mean Filter

Medical images are an important source of information for both diagnosing and treating diseases. In many cases, the images produced by a Positron Emission Tomography (PET) scan are used to assess the effectiveness of a particular treatment. This paper presents a method for whole-body PET image denoising using a spatially-guided non-local means filter. The proposed method starts with clustering the images into regions. To estimate the noise, a Bayesian with automatic settings of the parameters was used. Then, only patches that belong to regions were collected and processed. The performance was compared to two methods; Gaussian and conventional Non-Local Means (NLM). The Jaszczak phantom and PET/ Computed Tomography (CT) for whole-body were involved in the benchmarking. The obtained results showed that in the Jaszczak phantom, the Signal-to-Noise Ratio (SNR) was significantly improved. Additionally, the proposed method improved the contrast and SNR compared to conventional NLM and Gaussian. Finally, the proposed method, in clinical whole-body PET, can be considered as another way of the post-reconstruction filter.

Color Image De-noising Based on Mean, Median, and Gaussian filters

Color Image De-noising Based on Mean, Median, and Gaussian filters

An adaptive optimum weighted mean filter and bilateral filter for noise removal in cardiac MRI images

Medical diagnosis greatly benefits from the use of MRI images. There have been many MRI image enhancement algorithms proposed over the years to aid doctors in disease diagnosis. Conventional image enhancing methods, nevertheless, may also boost the noise that was already there in the captured picture, which might cause distortion, which is unfavourable for the disease diagnosis. Therefore, an appropriate technique for noise suppression is necessary. An attempt has been made to incorporate a two-step filtering based algorithm for noise removal in cardiac MRI images while maintaining the images crucial compositional elements, such as their borders and textures. A combination of adaptive optimum weighted mean filter (AOWMF) and bilateral filter (BF) has been implemented to reduce impulsive noise and Gaussian noise that are both present as mixed noise. The fundamental idea of the AOWMF approach includes the detection of noise pixels using Crow Optimization Algorithm (COA), and replacing them with an optimum weighted mean value depending on a criterion of maximization fitness function. Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) have been used to assess the effectiveness of the employed noise reduction technique. The result analysis are experimented in MATLB which reveals that proposed method is able to achieve high PSNR and SSIM and further, this method has the potential to enhance more detail structures of the input cardiac MRI images than existing methods.