33. Improvement of Image Quality in Digital Angiography : The 4th report : gradient image-processing

AbstractDiabetic eye disease (DED) is a cluster of eye problem that affects diabetic patients. Identifying DED is a crucial activity in retinal fundus images because early diagnosis and treatment can eventually minimize the risk of visual impairment. The retinal fundus image plays a significant role in early DED classification and identification. An accurate diagnostic model’s development using a retinal fundus image depends highly on image quality and quantity. This paper presents a methodical study on the significance of image processing for DED classification. The proposed automated classification framework for DED was achieved in several steps: image quality enhancement, image segmentation (region of interest), image augmentation (geometric transformation), and classification. The optimal results were obtained using traditional image processing methods with a new build convolution neural network (CNN) architecture. The new built CNN combined with the traditional image processing approach presented the best performance with accuracy for DED classification problems. The results of the experiments conducted showed adequate accuracy, specificity, and sensitivity.

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Comparison of Full-Reference Image Quality Models for Optimization of Image Processing Systems

International Journal of Computer Vision ◽

10.1007/s11263-020-01419-7 ◽

2021 ◽

Author(s):

Keyan Ding ◽

Kede Ma ◽

Shiqi Wang ◽

Eero P. Simoncelli

Keyword(s):

Image Processing ◽

Image Quality ◽

Reference Image ◽

Quality Models ◽

Full Reference

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Image quality and dose differences caused by vendor-specific image processing of neonatal radiographs

Pediatric Radiology ◽

10.1007/s00247-016-3663-2 ◽

2016 ◽

Vol 46 (11) ◽

pp. 1606-1613 ◽

Cited By ~ 5

Author(s):

William F. Sensakovic ◽

M. Cody O’Dell ◽

Haley Letter ◽

Nathan Kohler ◽

Baiywo Rop ◽

...

Keyword(s):

Image Processing ◽

Image Quality

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Improving Image Quality and Reducing Drift Problems via Automated Data Acquisition and Averaging in a Cs-Corrected TEM

Microscopy Today ◽

10.1017/s1551929500062362 ◽

2008 ◽

Vol 16 (6) ◽

pp. 36-39 ◽

Cited By ~ 1

Author(s):

E. Voelkl ◽

B. Jiang ◽

Z.R. Dai ◽

J.P Bradley

Keyword(s):

Image Processing ◽

Image Quality ◽

Data Acquisition ◽

Information Content ◽

Exposure Time ◽

Image Acquisition ◽

Ccd Camera ◽

Automated Data Acquisition ◽

Processing Steps

Image acquisition with a CCD camera is a single-press-button activity: after selecting exposure time and adjusting illumination, a button is pressed and the acquired image is perceived as the final, unmodified proof of what was seen in the microscope. Thus it is generally assumed that the image processing steps of e.g., “darkcurrent correction” and “gain normalization” do not alter the information content of the image, but rather eliminate unwanted artifacts.

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Retinal Digital Image Quality Improvement as A Diabetes Retinopatic Disease Detection Effort

Journal of Electrical Technology UMY ◽

10.18196/jet.v4i2.8590 ◽

2020 ◽

Vol 4 (2) ◽

pp. 53-60

Author(s):

Latifah Listyalina ◽

Yudianingsih Yudianingsih ◽

Dhimas Arief Dharmawan

Keyword(s):

Image Processing ◽

Diabetic Retinopathy ◽

Image Quality ◽

Digital Image ◽

Signal To Noise Ratio ◽

Histogram Equalization ◽

Vital Role ◽

Retinal Images ◽

Signal To Noise ◽

Noise Ratio

Image processing is a technical term useful for modifying images in various ways. In medicine, image processing has a vital role. One example of images in the medical world, namely retinal images, can be obtained from a fundus camera. The retina image is useful in the detection of diabetic retinopathy. In general, direct observation of diabetic retinopathy is conducted by a doctor on the retinal image. The weakness of this method is the slow handling of the disease. For this reason, a computer system is required to help doctors detect diabetes retinopathy quickly and accurately. This system involves a series of digital image processing techniques that can process retinal images into good quality images. In this research, a method to improve the quality of retinal images was designed by comparing the methods for adjusting histogram equalization, contrast stretching, and increasing brightness. The performance of the three methods was evaluated using Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), and Signal to Noise Ratio (SNR). Low MSE values and high PSNR and SNR values indicated that the image had good quality. The results of the study revealed that the image was the best to use, as evidenced by the lowest MSE values and the highest SNR and PSNR values compared to other techniques. It indicated that adaptive histogram equalization techniques could improve image quality while maintaining its information.

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Can a channelized Hotelling observer assess image quality in acquired mammographic images of an anthropomorphic breast phantom including image processing?

Medical Physics ◽

10.1002/mp.13342 ◽

2019 ◽

Vol 46 (2) ◽

pp. 714-725 ◽

Cited By ~ 1

Author(s):

C. Balta ◽

R. W. Bouwman ◽

I. Sechopoulos ◽

M. J. M. Broeders ◽

N. Karssemeijer ◽

...

Keyword(s):

Image Processing ◽

Image Quality ◽

Mammographic Images ◽

Hotelling Observer ◽

Breast Phantom ◽

Channelized Hotelling Observer

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Image Processing Architectures

VFAST Transactions on Software Engineering ◽

10.21015/vtse.v13i2.508 ◽

2018 ◽

pp. 68-74

Keyword(s):

Image Processing ◽

Signal Processing ◽

Image Quality ◽

Comparative Study ◽

Focal Plane ◽

Detailed Review ◽

Communication Problem ◽

Fast Processing ◽

Processing Architectures ◽

The Comparative Study

One type of signal processing is Image processing in which the input used as an image and the output might also be an image or a set of features that are related to the image. Images are handled as a 2D signal using image processing methods. For the fast processing of images, several architectures are suitable for different responsibilities in the image processing practices are important. Various architectures have been used to resolve the high communication problem in image processing systems. In this paper, we will yield a detailed review about these image processing architectures that are commonly used for the purpose of getting higher image quality. Architectures discussed are FPGA, Focal plane SIMPil, SURE engine. At the end, we will also present the comparative study of MSIMD architecture that will facilitate to understand best one.

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