A new fuzzy logic filter for image enhancement

Farbiz, Farzam; Menhaj, Mohammad Bagher; Motamedi, Seyed Ahmad; Hagan, Matthew

doi:10.1109/3477.826951

Cited by 71 publications

(29 citation statements)

References 29 publications

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“…In this algorithm evolutionary optimization processes were followed. Farbiz et al [14] proposed a method based on fuzzy-logic-control which is used to eliminate impulsive noise and smooth Gaussian noise and also preserve edges and image details. Jing Li et al [15] implemented color based grayscale-fused image enhancement algorithm to stick out the meaningful information of a certain sensor with the high resolution of the grayscale-fused image.…”

Section: Related Workmentioning

confidence: 99%

Noise Suppression Scheme using Median Filter in Gray and Binary Images

Chandra¹,

Kanagalakshmi²

2011

IJCA

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The Quality factor always integrates performance of everything. The performance of a fingerprint image matching process relies on the quality of the fingerprint image. The poor quality image does not define a good structure of the fingerprint image; and the noisy images affect the reliability of Automated Authentication and the Identification systems. To get a good quality image, noises should be suppressed. In this paper, a new scheme is generated to eliminate noise using Median filter. We made an attempt to apply the new scheme on both the gray-scale image and the binary image in order to get better and accurate fingerprint features for further process. The experimental result shows the expected measures such as effectiveness and performance on the median filter using the statistical correlation factor and the computational time among the gray and binary images. The proposed algorithm was implemented using MATLAB 7.10.0 tool. The experimental results show that the algorithm for the enhancement of gray level image gives effectiveness and no loss of information, whereas there is some loss of information in the binary image.

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Section: Related Workmentioning

confidence: 99%

Noise Suppression Scheme using Median Filter in Gray and Binary Images

Chandra¹,

Kanagalakshmi²

2011

IJCA

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“…In contrast correction or enhancement in images, fuzzy logic is employed as a simple means of transforming local gray-level variations into the fuzzy domain by creating a fuzzy image using the membership function [4,6,15]. Considering the nonuniform and fuzziness of contrast difference within the radiograph itself and between subsequent radiographs, fuzzy logic achieves better contrast normalization and computational efficiency than conventional methods.…”

Section: Introductionmentioning

confidence: 99%

A robust generalized fuzzy operator approach to film contrast correction in digital subtraction radiography

Leung

2006

Med Bio Eng Comput

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Digital subtraction radiography requires close matching of the contrast in each pair of X-ray images to be subtracted. Previous studies have shown that nonparametric contrast/brightness correction methods using the cumulative density function (CDF) and its improvements, which are based on gray-level transformation associated with the pixel histogram, perform well in uniform contrast/brightness difference conditions. However, for radiographs with nonuniform contrast/ brightness, the CDF produces unsatisfactory results. In this paper, we propose a new approach in contrast correction based on the generalized fuzzy operator with least square method. The result shows that 50% of the contrast/brightness errors can be corrected using this approach when the contrast/brightness difference between a radiographic pair is 10 U. A comparison of our approach with that of CDF is presented, and this modified GFO method produces better contrast normalization results than the CDF approach.

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“…The image segmentation basically to detect objects and boundaries such as lines and curves in the image. Besides, this technique can provide clear images and there is no need to set a common point and it suitable for other parts of human body [7][8][9]. In this recent study, the active countor and level set method were also implemented to segment the ultrasound pancreas image.…”

Section: Introductionmentioning

confidence: 99%

Which Method Is Providing Correct Geometry of Pancreas in Filtering and Segmenting Ultrasound Images?

Mh¹,

Mf²,

Derus³

et al. 2017

Nucl Med Biomed Imaging

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Pancreas is a small organ approximately six inches long, located at upper abdomen and adjacent to the small intestine. It is located at the back and deep into the human body. Therefore, it difficult to obtain a pancreas image clearly using ultrasound machine. In order to solve this problem, MATLAB software was used to filter and segment the ultrasound pancreas images using various methods. The images were selected based on high quality images produced from five subjects using AplioMX device from Toshiba ultrasound machine with a 3.5MHz convex transducer.In this study, there were various technique of filter used including Kaun filter, Wiener filter, Frost filter and Anisotropic Diffusion filter in order to reduce spackle noise of ultrasound pancreas images. Then, the filtered images were measured using Mean Square Error (MSE), Power Signal to Noise Ratio (PSNR), Average Difference (AD), Normalized Cross-Correlation (NCC), Maximum Difference (MD), Structural Content (SC), and Normalized Absolute Error (NAE) formula to evaluate the best quality images before undergo the segmentation process. As the result, Wiener filter was selected. In the segmentation process, the active countor method and level set method were evaluated. Then, the area of binary image and error percentage were calculated. As a conclusion, it shows that the filtering process using Wiener filter and segmentation method using level sets method has been successfully done to produce the best geometry of pancreas image.

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A new fuzzy logic filter for image enhancement

Cited by 71 publications

References 29 publications

Noise Suppression Scheme using Median Filter in Gray and Binary Images

Noise Suppression Scheme using Median Filter in Gray and Binary Images

A robust generalized fuzzy operator approach to film contrast correction in digital subtraction radiography

Which Method Is Providing Correct Geometry of Pancreas in Filtering and Segmenting Ultrasound Images?

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