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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control)
Anthony Y. Aidoo
Eastern Connecticut State University
Computer Science & IT

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A total variation-undecimated wavelet approach to chest radiograph image enhancement Matilda Wilson; James B. H. Acquah; Anthony Y. Aidoo
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 4: August 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v17i4.11911

Abstract

Most often medical images such as X-Rays have a low dynamic range and many of their targeted features are difficult to identify. Intensity transformations that improve image quality usually rely onwavelet denoising and enhancement typically use the technique of thresholding to obtain better quality medical images. A disadvantage of wavelet thresholding is that even though it adequately removes noise in an image, it introduces unwanted artifacts into the image near discontinuities. We utilize a total variation method and an undecimated wavelet image enhancing algorithm for improving the image quality of chest radiographs. Our approach achieves a high level chest radiograph image deniosing in lung nodules detection while preserving the important features. Moreover, our method results in a high image sensitivity that reduces the average number of false positives on a test set of medical data.
Chest radiograph image enhancement with wavelet decomposition and morphological operations Anthony Y. Aidoo; Matilda Wilson; Gloria A. Botchway
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 5: October 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v17i5.11964

Abstract

Medical image processing algorithms significantly affect the precision ofdisease diagnostic process. This makes it crucial to improve the quality of a medical image with the goal to enhance perceivability of the points of interest in order to obtain accurate diagnosis of a patient.  Despite the reliance of various medical diagnostics on utilize X-rays, they are usually plagued by dark and low contrast properties. Sought-after  details in X-rays can only be accessed by means of digital image processing techniques, despite the fact that these techniques are far from being  perfect. In this paper, we implement a wavelet decomposition and reconstruction technique to enhance radiograph properties, some of which include contrast and noise, by using a series of morphological erosion and dilation to improve the visual quality of the chest radiographs for the detection of cancer nodules.
Co-Authors Gloria A. Botchway James B. H. Acquah Matilda Wilson Matilda Wilson