Application of fuzzy logic in finding the optimal filter in optoacoustics problems

Denoising is an important step in the early stage of signal preprocessing in optoacoustic applications. The efficiency of such modern noise removal methods as wavelet or curvlet filtering depends significantly on the numerical combinations and forms of wavelet transform parameters, and the multidimensional extension of such filters is rather non-trivial. These issues are serious obstacle for using of these highly effective filters in the tasks of optoacoustic reconstruction, especially in real laboratorial or medical practice. The objective of our study was to find the optimal filter, convenient for use in laboratorian and medical practice, when the types of noise are a priori unknown, and the filter settings should not take much time. In the offered work spatial filters which have only one parameter of adjustment - the size of a window are considered. Three-dimensional extensions of such well-established denoising techniques, as mean filter, median filter, their adaptive variants (Wiener spatial filter and modified median filter), as well as iterative truncated arithmetic mean filter were analyzed. The proposed filters were tested on a test set that contains versions of Shepp-Logan's three-dimensional phantom with mixtures of Gaussian and alpha-stable noise, as well as speckle noise. The identification of the best filter for simultaneous suppression of these types of interference was carried out using the theory of fuzzy sets. In our tests, a modified median filter and an iterative truncated arithmetic mean filter were rated as the best choice when the goal is to minimize aberrations when noise is not known a priory.

Download Full-text

An Efficient Two step Algorithm for Despeckling the Ultrasound Image

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.f8308.088619 ◽

2019 ◽

Vol 8 (6) ◽

pp. 1074-1078

Keyword(s):

Median Filter ◽

Ultrasound Image ◽

Speckle Noise ◽

Noise Removal ◽

Ultrasound Images ◽

Mean Filter ◽

Image Area ◽

Step Algorithm ◽

Image Details ◽

Average Filter

The speckle noise presence in ultrasound images is a critical concern in medical image processing. It degrades the important features captured in an image and decreases the physician’s capacity to understand the image accurately. In recent years, numerous techniques have been proposed to de-noise the ultrasound images. In this paper, a new speckle noise removal algorithm has been proposed for medical ultrasound images. Based on the concepts of fuzzy logic and Coefficient of variation, the proposed algorithm first classifies the image area into three different regions such as homogeneous, edge and detail region. Next, average filter, median filter and an adaptive mean filter are employed to partition the unwanted noise from the pixels of different regions. Filter selection depends on the features of a region. The proposed algorithm develops image quality by removing maximum unwanted noise while protecting the important image details

Download Full-text

A New Idea of Fast Three-Dimensional Median Filtering for Despeckling of Optical Coherence Tomography Images

Image Processing & Communications ◽

10.1515/ipc-2015-0037 ◽

2015 ◽

Vol 20 (3) ◽

pp. 25-34 ◽

Cited By ~ 5

Author(s):

Jarosław Gocławski ◽

Joanna Sekulska-Nalewajko

Keyword(s):

Optical Coherence Tomography ◽

Graphics Processing Unit ◽

Median Filter ◽

Three Dimensional ◽

Speckle Noise ◽

Noise Removal ◽

Processing Unit ◽

Optical Coherence ◽

Median Filtering ◽

Local Intensity

Abstract Median filtering has been widely used in image processing for noise removal because it can significantly reduce the power of noise while limiting edge blurring. This filtering is still a challenging task in the case of three-dimensional images containing up to a billion of voxels, especially for large size filtering windows. The authors encountered the problem when applying median filter to speckle noise reduction in optical coherence tomography images acquired by the Spark OCT systems. In the paper a new approach to the GPU (Graphics Processing Unit) based median smoothing has been proposed, which uses two-step evaluation of local intensity histograms stored in the shared memory of a graphic device. The solution is able to output about 50 million voxels per second while processing the neighbourhood of 125 voxels by Quadro K6000 graphic card configured on the Kepler architecture.

Download Full-text

Speckle noise removal in interference patterns by cross absolute filter: comparison with median filter

10.1117/12.212150 ◽

1995 ◽

Author(s):

Christophe Gorecki ◽

Jacek Mandziuk

Keyword(s):

Median Filter ◽

Speckle Noise ◽

Noise Removal

Download Full-text

A Median Filter With Evaluating of Temporal Ultrasound Image for Impulse Noise Removal for Kidney Diagnosis

Journal of Applied Science and Technology Trends ◽

10.38094/jastt1217 ◽

2020 ◽

Vol 1 (2) ◽

pp. 71-77

Author(s):

Rasheed Ihsan ◽

Saman Almufti ◽

Ridwan Marqas

Keyword(s):

Information Quality ◽

Kidney Diseases ◽

Median Filter ◽

Ultrasound Image ◽

Speckle Noise ◽

Noise Removal ◽

Ultrasound Images ◽

Examination Methods ◽

Detection And Diagnosis ◽

Temporal Dimensions

Ultrasound imaging helps the doctor to view the tissues and organs in the body's abdominal area with no ionization risks compared to other internal organ examination methods dependent on radiation. It offers highly precise renal imaging of suspected acute kidney diseases. This paper proposes temporary filtering methods to improve ultrasound images from ultrasonic kidney video. The proposed filters focus on the detection and diagnosis of kidney disease by processing consecutive images of the acquired kidney video. Extending the spatial median image filters to temporal dimensions after the picture frames are manually clipped and aligned in MATLAB by image processing Toolbox to suppress speckle noise, and enhance a doctor's diagnostic information quality.

Download Full-text

Speckle and Rician Noise Removal from Medical Images and Ultrasound Images

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.e5993.018520 ◽

2020 ◽

Vol 8 (5) ◽

pp. 1851-1854

Keyword(s):

Noise Reduction ◽

Medical Images ◽

Signal To Noise Ratio ◽

Median Filter ◽

Speckle Noise ◽

Noise Removal ◽

Difficult Problem ◽

Ultrasound Images ◽

Simple Method ◽

Different Types

In medical images, medical images are corrupted by different types of noise. It is important to get a precise picture and accurately observe the correspondence. Removing noise from medical images has become a very difficult problem in the field of the medical image. The most well-known noise reduction method, which is usually based on the local statistics of medical images, is efficient because of the noise reduction of medical images. In paper, an efficient and simple method for noise reduction from medical images is presented. The paper proposes a filtering system to combine both the Median filter and Gaussian filter to remove the Speckle noise form Medical and Ultrasound images. The image quality is measured through statistical quantities: Peak signal to noise ratio (PSNR). Experimental results show that the proposed system removes Speckle noise from medical images.

Download Full-text

PERFORMANCE ANALYSIS OF NOISE REMOVAL TECHNIQUES FOR FACIAL IMAGES- A COMPARATIVE STUDY

BSSS Journal of Computer ◽

10.51767/jc1201 ◽

2021 ◽

Vol 12 (1) ◽

pp. 1-10

Author(s):

Anshika Jain ◽

◽

Maya Ingle

Keyword(s):

Comparative Study ◽

Gaussian Noise ◽

Signal To Noise Ratio ◽

Median Filter ◽

Wiener Filter ◽

Noise Removal ◽

Poisson Noise ◽

Signal To Noise ◽

Mean Filter ◽

Facial Images

Image de-noising has been a challenging issue in the field of digital image processing. It involves the manipulation of image data to produce a visually high quality image. While maintaining the desired information in the quality of an image, elimination of noise is an essential task. Various domain applications such as medical science, forensic science, text extraction, optical character recognition, face recognition, face detection etc. deal with noise removal techniques. There exist a variety of noises that may corrupt the images in different ways. Here, we explore filtering techniques viz. Mean filter, Median filter and Wiener filter to remove noises existing in facial images. The noises of our interest are namely; Gaussian noise, Salt & Pepper noise, Poisson noise and Speckle noise in our study. Further, we perform a comparative study based on the parameters such as Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR) and Structure Similarity Index Method (SSIM). For this research work, MATLAB R2013a on Labeled faces in Wild (lfw) database containing 120 facial images is used. Based upon the aforementioned parameters, we have attempted to analyze the performance of noise removal techniques with different types of noises. It has been observed that MSE, PSNR and SSIM for Mean filter are 44.19 with Poisson noise, 35.88 with Poisson noise and 0.197 with Gaussian noise respectively whereas for that of Median filter, these are 44.12 with Poisson noise, 46.56 with Salt & Pepper noise and 0.132 with Gaussian noise respectively. Wiener filter when contaminated with Poisson, Salt & Pepper and Gaussian noise, these parametric values are 44.52, 44.33 and 0.245 respectively. Based on these observations, we claim that the Median filtering technique works the best when contaminated with Poisson noise while the error strategy is dominant. On the other hand, Median filter also works the best with Salt & Pepper noise when Peak Signal to Noise Ratio is important. It is interesting to note that Median filter performs effectively with Gaussian noise using SSIM.

Download Full-text

Improvement of the Traditional Canny Edge Detection Algorithm by using Combination of Arithmetic Mean Filter, Harmonic Mean Filter and Geometric Mean Filter

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.b6448.129219 ◽

2019 ◽

Vol 9 (2) ◽

pp. 2392-2399

Keyword(s):

Edge Detection ◽

Noise Removal ◽

Detection Algorithm ◽

Arithmetic Mean ◽

Harmonic Mean ◽

Canny Edge Detection ◽

Edge Detection Algorithm ◽

Mean Filter ◽

Canny Edge ◽

Canny Edge Detection Algorithm

Canny Edge Detection Algorithm was very popular on the computer vision area which used to preserve the edges of the image. Due to the defect of the Canny Edge Detection Algorithm like no efficiency on noise removal, some improvement on the Canny Edge Detection Algorithm was done by the researchers. On this paper, a new enhanced Canny Edge Detection Algorithm will be propose which replaces the Gaussian Filter with combination of Arithmetic Mean Filter, Harmonic Mean Filter and Geometric Mean Filter. The replace of Gaussian Filter with combination of Arithmetic Mean Filter, Harmonic Mean Filter and Geometric Mean Filter is to improve the performance of Canny Edge Detection Algorithm on noise removal. A comparison between Canny Edge Detection Algorithm proposed by this paper, Canny Edge Detection Algorithm proposed by (Ilkin, Tafralı, &Sahin, 2017) and traditional Canny Edge Detection Algorithm will be done. The comparison will done by using eight images with different type and size which corrupted by noise. The performance of three algorithms will be determined by using the Peak Signal to Noise Ratio (henceforth, PSNR) value which uses as a quantitative measure. From the result, the Canny Edge Detection proposed by this paper will provide a better performance on noise removal and which will give a better impact on preserve the edges of the images corrupted by noise.

Download Full-text