Using Hybrid Segmentation Method to Diagnosis and Predict Brain Malfunction

An efficient filter must have the quality of removing noise at the same time preserving the information. On the other hand, noise contaminates in the field of signal processing and communication due to various unwanted nonlinear effects. So study of the characteristics of different types of noise is necessary. Also, how various types of filters response to these noises are also important? In this paper, we have studied the characteristics of four types of noise namely Salt & pepper noise, Gaussian noise, Speckle noise and Poisson noise and analyzed the performance of four basic types of filters such as Mean filter, Median filter, Wiener filter and Gaussian filter to suppress the noises mentioned previously.

Digital images, particularly medical images, are prone to various noise attacks, such as additive, multiplicative, etc. Salt and pepper noise (SPN) is a type of multiplicative noise that degrades image quality and complicates identification, potentially leading to inaccurate diagnoses. Removing SPN is crucial yet challenging as noise removal can also eliminate important edge details. This study addresses the challenge of removing SPN without losing edge details. Existing SPN filtering algorithms, such as Average Filter (AF), Median Filter (MF), Switched MF (SMF), Adaptive MF (AMF), and Decision-based MF (DMF), perform poorly under high SPN attacks. We propose two effective SPN filters, CA_SPN1 and CA_SPN2, under the framework of Cellular Automata (CA) and MF. CA_SPN1 excels at high SPN levels, while CA_SPN2, a combination of CA_SPN1 and AMF, is effective across all SPN levels. Quality evaluations using Image Quality Assessment (IQA) metrics – Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM), Figure of Merit (FOM), and Perceptual Similarity (PSIM) – demonstrate that the proposed algorithms restore images more effectively while preserving edge details compared to state-of-the-art algorithms. Furthermore, experimental results confirm the superior restoration quality of the proposed filters, particularly under high SPN conditions.

Image De-noising is a problem of prime importance in the field of Image Processing ranging from Medical Imaging to Satellite imaging. The main purpose of an Image de-noising algorithm is to reduce the noise level to improve both the interpretability and visual aspect of the images. This paper propose to indicate the suitability of different Multi-resolution transforms, viz Wavelets, Ridgelets and Curvelets in de-noising various imaging modalities corrupted by Random noise, Gaussian noise, Speckle Noise, Salt and Pepper Noise and Poisson noise. Though the comparative study is based on various imaging modalities, due relevance is given to medical images like Computed Tomography (CT), Magnetic resonance Imaging (MRI) and X-ray images. Experiments are conducted on various image data sets namely Natural, Satellite and Medical Images with the Multi-resolution transforms using two existing thresholding strategies, namely Soft and Hard Thresholding. A comprehensive evaluation of three different types of Multiresolution transforms corrupted with different types of noise is provided and the quality of de-noising is measured in terms of Peak Signal to Noise ratio (PSNR). Experimental results indicate that the Curvelets reveal superior performance over wavelets and Ridgelets in terms of PSNR value and perceptible quality.

Image Denoising Using Hybrid Filter

Lung Cancer in today’s world is one of the major widespread dangerous diseases which is the subject of maximum deaths every year. Accurate detection of lung cancer could boost the endurance rate. Medical image processing has a significant impact on the recognition of lung tumors using Computer Tomography (CT) scan images. Images from a CT scan are widely used because they provide comprehensive imaging of tumor progression inside the lungs. Although different types of noise might be experienced while doing CT scans, producing it a monotonous task for recognizing tumors in the lung. Elimination of noise in CT images is a challenging task for medical diagnoses. The presence of noise in an image is inevitable. Hence reducing noise from the CT scan image is critical for further analysis. Hence various filtering techniques have been used that denoise and enhance the image and help in further evaluation of CT images for accurate lung cancer detection. This paper analyses the noises of different kinds in the CT images and different noise removal techniques which help in improving the accuracy of segmentation and feature extraction as they remove unwanted noise and contribute to the accurate detection of lung cancer. The various filtering methods are analyzed with salt along with pepper noise and speckle noise. The performances of different filters are computed in terms of metrics for evaluation like PSNR, SSIM, MSE, and SNR. The experimental results show that the median filter is more efficient in comparison to other filtering methods in eliminating noises that exist in lung CT images by owning fewer MSE values of 214.8522, high SNR value of 19.36304, SSIM value 0.595997, and high PSNR value of 24.80941.

In this paper, we present four denoising algorithms dedicated to the division of focal plane (DoFP) polarization image sensors, including the average filtering, median filtering, Wiener filtering and wavelet threshold denoising algorithms. Compared with the previous implementations solely based on the Gaussian noise model, this paper, for the first time, covers the non-Gaussian noises, such as the salt & pepper noise and Poisson noise. According to our extensive experimental results, the wavelet threshold denoising outperforms for suppressing the Gaussian noise; while the median filtering and the Wiener filtering outperform for suppressing the low-density salt & pepper noise and Poisson noise, respectively.

Noise in signals and images can be removed through different de-noising<br>techniques such as mean filtering, median filtering, total variation and filtered variation<br>techniques etc. Wavelet based<br>de-noising is one of the major techniques used for noise removal. In the first part of our<br>work, wavelet transform based logarithmic shrinkage technique is used for de-noising of<br>images, corrupted by noise (during under-sampling in the frequency domain). The logarithmic<br>shrinkage technique is applied to under-sampled Shepp–Logan Phantom image.<br>Experimental results show that the logarithmic shrinkage technique is 7–10% better in<br>PSNR values than the existing classical techniques. In the second part of our work we denoise<br>the noisy, under-sampled phantom image, having salt and pepper, Gaussian, speckle<br>and Poisson noises through the four thresholding techniques and compute their correlations<br>with the original image. They give the correlation values close to the noisy image. By<br>applying median or wiener filter in parallel with the thresholding techniques, we get<br>30–35% better results than only applying the thresholding techniques individually. So, in<br>the second part we recover and de-noise the sparse under-sampled images by the combination<br>of shrinkage functions and median filtering or wiener filtering.

Speckle Noise is a natural characteristic of medical ultrasound images. It is a term used for the granular form that appears in B-Scan and can be considered as a kind of multiplicative noise. Speckle Noise reduces the ability of an observer to distinguish fine details in diagnostic testing. It also limits the effective implementation of image processing such as edge detection, segmentation and volume rendering in 3 D. Therefore; treatment methods of speckle noise were sought to improve the image quality and to increase the capacity of diagnostic medical ultrasound images. Such as median filters, Wiener and linear filters (Persona & Malik, SRAD ... ..).The method used in this work is 2-D translation invariant forward wavelet transform, it is used in image processing, including noise reduction applications in medical imaging.

In this paper, we focus on the robustness in noise tolerance of spatial domain optical flow and we present a performance study of bidirectional confidential with median filter on spatial domain optical flow (spatial correlation, local based, and global based) under non Gaussian noise where several noise tolerance models on spatial domain optical flow are used in comparison. The experiment results are investigated on robustness under noisy condition by using non Gaussian noise (Poisson Noise, Salt &amp; Pepper noise, and Speckle Noise) in contamination with several standard sequences. The experiment concentrates on Error Vector Magnitude (EVM) as a performance indicator where the result in motion vector (MV) of each method is used to compare with the ground truth vector in the experimental performance analysis.

A fast image segmentation method with multilevel threshold of two-dimensional entropy was proposed based on the firefly algorithm to overcome the large amount of calculation and long computing time.Firstly,the principle of two-dimensional entropy threshold segmentation was analyzed,and the single threshold segmentation of two-dimensional entropy was extended to multilevel threshold segmentation.Then,the bionic mechanism and searching optimization process of the firefly algorithm were analyzed,and the multilevel threshold segmentation method of two-dimensional entropy combined with firefly algorithm was proposed.Finally,typical image segmentation experiments by using the proposed method were performed and the results were compared with those of two-dimensional entropy exhaustive segmentation method and the multilevel threshold segmentation method of two-dimensional entropy based on Particle Swarm Optimization(PSO).Experimental results show that the speeds of the proposed method in single threshold segmentation,dual-threshold segmentation and thethree threshold segmentation are 3.91,1 040.32and 8 128.85times faster than those of the two-dimensional entropy exhaustive segmentation method respectively.Moreover,the threshold selection accuracy and running speed of the proposed method are both better than those of the multilevel threshold segmentation method of two-dimensional entropy based on PSO.Therefore,the fast image segmentation method with multilevel threshold of two-dimensional entropy based on firefly algorithm can quickly and efficiently resolve complex and multi-target image segmentation problems.

TEM (Transmission Electron Microscopy) is an important morphological characterization tool for Nanomaterials. Quite often a microscopy image gets corrupted by noise, which may arise in the process of acquiring the image, or during its transmission, or even during reproduction of the image. Removal of noise from an image is one of the most important tasks in image processing. Denoising techniques aim at reducing the statistical perturbations and recovering as well as possible the true underlying signal. Depending on the nature of the noise, such as additive or multiplicative type of noise, there are several approaches towards removing noise from an image. Image De-noising improves the quality of images acquired by optical, electro-optical or electronic microscopy. This paper compares five filters on the measures of mean of image, signal to noise ratio, peak signal to noise ratio &amp; mean square error. In this paper four types of noise (Gaussian noise, Salt &amp; Pepper noise, Speckle noise and Poisson noise) is used and image de-noising performed for different noise by various filters (WFDWT, BF, HMDF, FDE, DVROFT). Further results have been compared for all noises. It is observed that for Gaussian Noise WFDWT &amp; for other noises HMDF has shown the better performance results.

In crop disease image segmentation, traditional convolutional neural networks have the problem of low image accuracy. For this reason, this paper proposes a segmentation method that combines conditional random fields with segnet networks. First, input the training set in the data set into the segnet network for training, and obtain the initial segmentation result and the network with updated parameters; secondly, the image segmented by the segnet network is sent to the conditional random field in the form of pixels and classification vectors corresponding to the pixels At the same time, construct an energy function to represent the relative relationship between pixels, and then keep training; in order to get the conditional random field in the training set, you need to adjust and optimize the image segmented by the segnet network to get it; then in the verification set, the segnet network and the weight parameters of the conditional random field model are further adjusted and optimized to obtain the final conditional random field and segnet network; finally, the final conditional random field and segnet network are applied to the crop disease image segmentation. The test results show that compared with the Otsu threshold and Grabcut segmentation methods, the segmentation accuracy P, recall rate R, and comprehensive evaluation index F of this method are all the maximum, and the average processing time of a single image is 0.86 s; compared with pspnet and deeplab methods, Under the premise that the segmentation accuracy P, the recall rate R, and the comprehensive evaluation index F are basically unchanged, the average processing time of a single image is significantly reduced. The results show that the best comprehensive effect can be achieved by segmenting crop disease images in complex environments using the method in this paper.

Nowadays, the rapid development of film, television, animation and other multimedia industries requires more efficient video processing. Under the dual pressure of cost and time limitation, higher requirements are put forward for rapid video synthesis. Combining the commonly used RGB color space and HSV color space, a fast image segmentation method is proposed. Firstly, a Mask is calculated by using the threshold segmentation method. In order to reduce the influence of black on the final result, inrange used the second Mask to make the composition. The final result is multiplied by the original image, and the effect of black on threshold segmentation is effectively eliminated on the premise of not reducing efficiency and image quality, so as to achieve the most ideal effect. Compared with the previous real-time image segmentation processing, this method has been greatly improved in the degree of code simplicity, operation efficiency and cost. In the aspect of color extraction, as it is completed in the RGB color space domain, there is almost no color missing or redundant. The real-time segmentation and splicing of images can meet the basic needs of film and television shooting and greatly improve its efficiency.

Neutrosophic set (NS) is a powerful tool to deal with indeterminacy. In this paper, the neutrosophic set is applied to the image domain and a novel edge detection technique is proposed. Noise reduction of images is a challenging task in image processing. Salt and pepper noise is one kind of noise that affects a grayscale image significantly. Generally, the median filter is used to reduce salt and pepper noise; it gives optimum results while compared to other image filters. Median filter works only up to a certain level of noise intensity. Here we proposed a neighborhood-based image filter called nbd-filter, it works perfectly for gray image regardless of noise intensity. It reduces salt and pepper noise significantly at any noise level and produces a noise-free image. Further, we proposed an edge detection algorithm based on the neutrosophic set, it detectsedges efficiently for images corrupted by noise and noise-free images. Since most of the real-life images consists of indeterminate regions, neutrosophy is a perfect tool for edge detection. The main advantage of the proposed edge detector is, it is a simple and efficient technique and detect edges more efficient than conventional edge detectors.

A novel method based on Wiener filter for denoising Poisson noise from medical X-Ray images