Denoising and Contrast Enhancement of Medical Image using Wavelet Thresholding Approach

Well-defined image can assist user to identify region of interest during segmentation. However, complex medical image is usually characterized by poor tissue contrast and low background luminance. The contrast improvement can lift image visual quality, but the fundamental contrast enhancement methods often overlook the sudden jump problem. In this work, the proposed bihistogram Bezier curve contrast enhancement introduces the concept of “adequate contrast enhancement” to overcome sudden jump problem in knee magnetic resonance image. Since every image produces its own intensity distribution, the adequate contrast enhancement checks on the image’s maximum intensity distortion and uses intensity discrepancy reduction to generate Bezier transform curve. The proposed method improves tissue contrast and preserves pertinent knee features without compromising natural image appearance. Besides, statistical results from Fisher’s Least Significant Difference test and the Duncan test have consistently indicated that the proposed method outperforms fundamental contrast enhancement methods to exalt image visual quality. As the study is limited to relatively small image database, future works will include a larger dataset with osteoarthritic images to assess the clinical effectiveness of the proposed method to facilitate the image inspection.

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Medical Image Contrast Enhancement via Wavelet Homomorphic Filtering Transform

TELKOMNIKA (Telecommunication Computing Electronics and Control) ◽

10.12928/telkomnika.v14i3.3118 ◽

2016 ◽

Vol 14 (3) ◽

pp. 1203 ◽

Cited By ~ 1

Author(s):

Xinmin Zhou ◽

Ying Zheng ◽

Lina Tan ◽

Junchan Zhao

Keyword(s):

Contrast Enhancement ◽

Medical Image ◽

Image Contrast ◽

Image Contrast Enhancement ◽

Homomorphic Filtering

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Denoising of MRI Brain Images using Adaptive Clahe Filtering Method

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.a1018.1091s19 ◽

2019 ◽

Vol 9 (1S) ◽

pp. 91-95

Keyword(s):

Contrast Enhancement ◽

Medical Image ◽

Signal To Noise Ratio ◽

Medical Image Analysis ◽

Medical Field ◽

Brain Images ◽

Process Computer ◽

Cancerous Cells ◽

Mri Brain Images

Image processing is a method of making the quality of an image better after removing unwanted information from image in various applications and domains to process computer effectively. Enhancement is, used to improve the quality effects of an image for further analysis. Enhancement of image can be done by filtering, de noising and contrast enhancement. Even though contrast enhancement of images is applied in different fields it is used effectively in the medical field. Medical Imaging is now recently used in most of the applications like Radiography, MRI, Nuclear medicine, Ultrasound Imaging, Tomography, Cardiograph, and Fundus Imagery and so on. The main problem in analysis of medical images is the poor contras .in medical image analysis the detection of tumor, cancerous cells, malignant or benign has to be classified effectively. In this paper various spatial domain techniques and their effectiveness in terms of quality improvement are discussed. The measuring metrics used for comparing different methods are parameters Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE), DICE coefficient, etc,.

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