scholarly journals Choosing the Optimal Spatial Domain Measure of Enhancement for Mammogram Images

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Karen Panetta ◽  
Arash Samani ◽  
Sos Agaian
Keyword(s):  
Image Quality ◽  
Image Enhancement ◽  
Visual Image ◽  
Imaging Systems ◽  
Medical Professionals ◽  
Optimal Operating ◽  
Negative Effect ◽  
Detection And Diagnosis ◽  
Mammogram Images

Medical imaging systems often require image enhancement, such as improving the image contrast, to provide medical professionals with the best visual image quality. This helps in anomaly detection and diagnosis. Most enhancement algorithms are iterative processes that require many parameters be selected. Poor or nonoptimal parameter selection can have a negative effect on the enhancement process. In this paper, a quantitative metric for measuring the image quality is used to select the optimal operating parameters for the enhancement algorithms. A variety of measures evaluating the quality of an image enhancement will be presented along with each measure’s basis for analysis, namely, on image content and image attributes. We also provide guidelines for systematically choosing the proper measure of image quality for medical images.

Perceptual Quality Assessment of Low-light Image Enhancement

2021 ◽  
Vol 17 (4) ◽  
pp. 1-24
Author(s):  
Guangtao Zhai ◽  
Wei Sun ◽  
Xiongkuo Min ◽  
Jiantao Zhou
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Enhancement ◽  
Dynamic Range ◽  
State Of The Art ◽  
Perceptual Quality ◽  
Low Light ◽  
Light Image ◽  
Light Enhancement

Low-light image enhancement algorithms (LIEA) can light up images captured in dark or back-lighting conditions. However, LIEA may introduce various distortions such as structure damage, color shift, and noise into the enhanced images. Despite various LIEAs proposed in the literature, few efforts have been made to study the quality evaluation of low-light enhancement. In this article, we make one of the first attempts to investigate the quality assessment problem of low-light image enhancement. To facilitate the study of objective image quality assessment (IQA), we first build a large-scale low-light image enhancement quality (LIEQ) database. The LIEQ database includes 1,000 light-enhanced images, which are generated from 100 low-light images using 10 LIEAs. Rather than evaluating the quality of light-enhanced images directly, which is more difficult, we propose to use the multi-exposure fused (MEF) image and stack-based high dynamic range (HDR) image as a reference and evaluate the quality of low-light enhancement following a full-reference (FR) quality assessment routine. We observe that distortions introduced in low-light enhancement are significantly different from distortions considered in traditional image IQA databases that are well-studied, and the current state-of-the-art FR IQA models are also not suitable for evaluating their quality. Therefore, we propose a new FR low-light image enhancement quality assessment (LIEQA) index by evaluating the image quality from four aspects: luminance enhancement, color rendition, noise evaluation, and structure preserving, which have captured the most key aspects of low-light enhancement. Experimental results on the LIEQ database show that the proposed LIEQA index outperforms the state-of-the-art FR IQA models. LIEQA can act as an evaluator for various low-light enhancement algorithms and systems. To the best of our knowledge, this article is the first of its kind comprehensive low-light image enhancement quality assessment study.

Image quality of digital mammography images produced using wet and dry laser imaging systems

Radiography ◽  
2006 ◽  
Vol 12 (1) ◽  
pp. 13-19
Author(s):  
K. Al Khalifah ◽  
A. Brindhaban ◽  
R. AlArfaj ◽  
O. Jassim
Keyword(s):  
Image Quality ◽  
Digital Mammography ◽  
Imaging Systems ◽  
Laser Imaging

scholarly journals Classical and Fuzzy Based Image Enhancement Techniques for Banana Root Disease Diagnosis: A Review and Validation

2020 ◽  
Vol 13 (1) ◽  
pp. 50-62
Author(s):  
D. Suryaprabha ◽  
J. Satheeshkumar ◽  
N. Seenivasan
Keyword(s):  
Image Quality ◽  
Frequency Domain ◽  
Image Enhancement ◽  
Plant Root ◽  
Histogram Equalization ◽  
Disease Diagnosis ◽  
Root Disease ◽  
Classical Point ◽  
Enhancement Algorithm

A vital step in automation of plant root disease diagnosis is to extract root region from the input images in an automatic and consistent manner. However, performance of segmentation algorithm over root images directly depends on the quality of input images. During acquisition, the captured root images are distorted by numerous external factors like lighting conditions, dust and so on. Hence it is essential to incorporate an image enhancement algorithm as a pre-processing step in the plant root disease diagnosis module. Image quality can be improved either by manipulating the pixels through spatial or frequency domain. In spatial domain, images are directly manipulated using their pixel values and alternatively in frequency domain, images are indirectly manipulated using transformations. Spatial based enhancement methods are considered as favourable approach for real time root images as it is simple and easy to understand with low computational complexity. In this study, real time banana root images were enhanced by attempting with different spatial based image enhancement techniques. Different classical point processing methods (contrast stretching, logarithmic transformation, power law transformation, histogram equalization, adaptive histogram equalization and histogram matching) and fuzzy based enhancement methods using fuzzy intensification operator and fuzzy if-then rule based methods were tried to enhance the banana root images. Quality of the enhanced root images obtained through different classical point processing and fuzzy based methods were measured using no-reference image quality metrics, entropy and blind image quality index. Hence, this study concludes that fuzzy based method could be deployed as a suitable image enhancement algorithm while devising the image processing modules for banana root disease diagnosis.

scholarly journals Appropriate Contrast Enhancement Measures for Brain and Breast Cancer Images

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Suneet Gupta ◽  
Rabins Porwal
Keyword(s):  
Breast Cancer ◽  
Contrast Enhancement ◽  
Brain Cancer ◽  
Medical Images ◽  
Subsequent Treatment ◽  
Imaging Systems ◽  
Medical Professionals ◽  
Quantitative Metrics ◽  
Proper Diagnosis

Medical imaging systems often produce images that require enhancement, such as improving the image contrast as they are poor in contrast. Therefore, they must be enhanced before they are examined by medical professionals. This is necessary for proper diagnosis and subsequent treatment. We do have various enhancement algorithms which enhance the medical images to different extents. We also have various quantitative metrics or measures which evaluate the quality of an image. This paper suggests the most appropriate measures for two of the medical images, namely, brain cancer images and breast cancer images.

Analysis and Evaluation of Image Fusion Algorithms

2015 ◽  
Vol 6 (2) ◽  
Author(s):  
Neeraj Kumar ◽  
Vikas Kumar Mishra ◽  
C. L.P. Gupta
Keyword(s):  
Image Quality ◽  
Image Fusion ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Imaging Systems ◽  
Reference Image ◽  
Analysis And Evaluation ◽  
Reference Methods ◽  
Objectively Measured

There is an increasing need for performance tools or quality assessment in order to compare the results obtained with different algorithms of image fusion. This analysis can be used to select a specific algorithm for a defined fusion dataset. The image quality is a characteristic of an image that measures the perceived image degradation (typically, compared to an ideal or perfect picture). Imaging systems may introduce a certain amount of distortion or artifacts in the signal, hence the quality assessment is an important problem. There are several techniques and measures that can be objectively measured and evaluated automatically by a computer program. Therefore, they may be classified as complete reference methods (FR) and the No-reference methods (NR). In the methods of image quality assessment FR, the quality of a test image is evaluated by comparing a reference image that is supposed to have perfect quality. NR measures attempt to assess the quality of an image without any reference to the original.

scholarly journals An Underwater Image Enhancement Algorithm Based on MSR Parameter Optimization

2020 ◽  
Vol 8 (10) ◽  
pp. 741
Author(s):  
Kai Hu ◽  
Yanwen Zhang ◽  
Feiyu Lu ◽  
Zhiliang Deng ◽  
Yunping Liu
Keyword(s):  
Image Quality ◽  
Image Enhancement ◽  
Environmental Changes ◽  
Gravitational Search Algorithm ◽  
Reference Image ◽  
Natural Image ◽  
Enhancement Effect ◽  
Underwater Image ◽  
Enhancement Algorithm

The quality of underwater images is often affected by the absorption of light and the scattering and diffusion of floating objects. Therefore, underwater image enhancement algorithms have been widely studied. In this area, algorithms based on Multi-Scale Retinex (MSR) represent an important research direction. Although the visual quality of underwater images can be improved to some extent, the enhancement effect is not good due to the fact that the parameters of these algorithms cannot adapt to different underwater environments. To solve this problem, based on classical MSR, we propose an underwater image enhancement optimization (MSR-PO) algorithm which uses the non-reference image quality assessment (NR-IQA) index as the optimization index. First of all, in a large number of experiments, we choose the Natural Image Quality Evaluator (NIQE) as the NR-IQA index and determine the appropriate parameters in MSR as the optimization object. Then, we use the Gravitational Search Algorithm (GSA) to optimize the underwater image enhancement algorithm based on MSR and the NIQE index. The experimental results show that this algorithm has an excellent adaptive ability to environmental changes.

Sign in / Sign up

Export Citation Format

Share Document