Enhancement CT Scan Image and Study Electronic, Structural and Vibrational Properties of Iobenguane

2021 ◽  
Vol 19 (48) ◽  
pp. 79-88
Author(s):  
Ahlam Majead Kadhim ◽  
Huda Muhamed Jawad ◽  
Shaimaa H. Abd muslim
Keyword(s):  
Ct Scan ◽  
Signal To Noise Ratio ◽  
Histogram Equalization ◽  
Blocking Agent ◽  
Diagnostic Techniques ◽  
General Information ◽  
Hydrogen Atoms ◽  
Low Contrast ◽  
Result Show ◽  
Electron Deficiency

This work is divided into two parts first part study electronic structure and vibration properties of the Iobenguane material that is used in CT scan imaging. Iobenguane, or MIBG, is an aralkylguanidine analog of the adrenergic neurotransmitter norepinephrine and a radiopharmaceutical. It acts as a blocking agent for adrenergic neurons. When radiolabeled, it can be used in nuclear medicinal diagnostic techniques as well as in neuroendocrine antineoplastic treatments. The aim of this work is to provide general information about Iobenguane that can be used to obtain results to diagnose the diseases. The second part study image processing techniques, the CT scan image is transformed to frequency domain using the LWT. Two methods of contrast enhancement of medical images Histogram Equalization and Adaptive Histogram Equalization used to improvement images properties. Canny edge detection operator used as a comparison tool between enhancement methods. The result show the absorbance of iobengaune in the range (1000 – 0 cm-1) of these single bonds from C-C, C-N, C-I, and C-O High absorbency and sharp peak of Maximum wavelength absorbed (640.66 nm) and the biggest energy (1.9353 eV). And half width is (0.333 eV) at half height is (2685.83cm-1). Electrostatic potential, electron deficiency were especially marked in rings benzene compounds exclusively of carbon and hydrogen atoms (focusing on areas of carbon), establishing this area as more electropositive. From the results of measures many functions like signal to noise ratio, mean, entropy and histogram of image, CT Scan images are best enhanced obtained using AHE technique in frequency. The dark regions of enhanced CT Scan images became clarity for input CT Scan image that having low contrast.

Research on Enhanced of Mine-Underground Picture

2012 ◽  
Vol 490-495 ◽  
pp. 548-552
Author(s):  
Meng Ling Zhao ◽  
Min Xia Jiang
Keyword(s):  
Image Quality ◽  
Signal To Noise Ratio ◽  
Ccd Camera ◽  
Histogram Equalization ◽  
Uniform Illumination ◽  
Improve Image Quality ◽  
Low Contrast ◽  
Enhancement Method ◽  
Field Information ◽  
Time Image

Because of the based on S3C6410 Field information recorder mine- underground non-uniform illumination and mine- underground non-uniform illumination that a large of noise collected and transferred,image is low contrast ,dim and dark. Based on the theory of Donoho's wavelet threshold denoising, several typical wavelet threshold denoising methods are compared.the best denoising effect of peak signal to noise ratio is obtained. The image enhancement method that combination of the adaptive thresholding denoising and histogram equalization is proposed. The experiment result shows that the method has a good denoising performance, which removed the readout noise of CCD Camera,at the same time, image quality is improved .So the wavelet enhancement in image processing of mine- underground can improve image quality.

scholarly journals Perbandingan Metode Contrast Enhancement pada Citra CT-Scan Kanker Paru-paru

Gravitasi ◽  
2020 ◽  
Vol 19 (2) ◽  
pp. 24-28
Author(s):  
Nurhidayah ◽  
Bannu Abdul Samad ◽  
Bualkar Abdullah
Keyword(s):  
Ct Scan ◽  
Contrast Enhancement ◽  
Mean Square Error ◽  
Signal To Noise Ratio ◽  
Median Filter ◽  
Histogram Equalization ◽  
Mean Square ◽  
Signal To Noise ◽  
Adaptive Histogram Equalization ◽  
Noise Ratio

Abstrak: Di Indonesia kanker paru menjadi penyebab kematian kedua setelah kanker payudara. Angka mortalitas yang cukup tinggi, maka penentuan diagnosis lebih awal memegang peranan yang sangat penting dalam manajemen terapi. Kelemahan CT-Scan dalam mendiagnosa kanker paru-paru disebabkan oleh kontras citra yang rendah dan derau pada citra. Pada penelitian ini akan membandingkan metode contrast enhancement berbasis histogram equalization dan contrast limited adaptive histogram equalization untuk meningkatkan kualitas citra dengan menggunakan software Matlab. Namun, sebelumnya dilakukan reduksi noise dengan menggunakan metode median filter. Kinerja dari setiap metode dihitung dengan mencari nilai MSE (Mean Square Error) dan PSNR (Peak Signal to Noise Ratio) citra. Dari nilai MSE dan PSNR yang di dapatkan diperoleh nilai MSE dan PSNR terbaik pada metode contrast limited adaptive histogram equalization dengan nilai 653,434 dB dan 245,547 dB.

Enhancing MRI Brain Images Using Contourlet Transform and Adaptive Histogram Equalization

2021 ◽  
Vol 11 (12) ◽  
pp. 3024-3027
Author(s):  
J. Murugachandravel ◽  
S. Anand
Keyword(s):  
Signal To Noise Ratio ◽  
Histogram Equalization ◽  
Contourlet Transform ◽  
Brain Images ◽  
Low Contrast ◽  
Mri Brain ◽  
Adaptive Histogram Equalization ◽  
Output Image ◽  
The Given ◽  
Mri Brain Images

Human brain can be viewed using MRI images. These images will be useful for physicians, only if their quality is good. We propose a new method called, Contourlet Based Two Stage Adaptive Histogram Equalization (CBTSA), that uses Nonsubsampled Contourlet Transform (NSCT) for smoothing images and adaptive histogram equalization (AHE), under two occasions, called stages, for enhancement of the low contrast MRI images. The given MRI image is fragmented into equal sized sub-images and NSCT is applied to each of the sub-images. AHE is imposed on each resultant sub-image. All processed images are merged and AHE is applied again to the merged image. The clarity of the output image obtained by our method has outperformed the output image produced by traditional methods. The quality was measured and compared using criteria like, Entropy, Absolute Mean Brightness Error (AMBE) and Peak Signal to Noise Ratio (PSNR).

Image Quality Enhancing by Efficient Histogram Equalization

2014 ◽  
Vol 2 (2) ◽  
pp. 47-58
Author(s):  
Ismail Sh. Baqer
Keyword(s):  
Image Quality ◽  
Contrast Enhancement ◽  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Histogram Equalization ◽  
Gray Level ◽  
Signal To Noise ◽  
Squared Error ◽  
Noise Ratio

A two Level Image Quality enhancement is proposed in this paper. In the first level, Dualistic Sub-Image Histogram Equalization DSIHE method decomposes the original image into two sub-images based on median of original images. The second level deals with spikes shaped noise that may appear in the image after processing. We presents three methods of image enhancement GHE, LHE and proposed DSIHE that improve the visual quality of images. A comparative calculations is being carried out on above mentioned techniques to examine objective and subjective image quality parameters e.g. Peak Signal-to-Noise Ratio PSNR values, entropy H and mean squared error MSE to measure the quality of gray scale enhanced images. For handling gray-level images, convenient Histogram Equalization methods e.g. GHE and LHE tend to change the mean brightness of an image to middle level of the gray-level range limiting their appropriateness for contrast enhancement in consumer electronics such as TV monitors. The DSIHE methods seem to overcome this disadvantage as they tend to preserve both, the brightness and contrast enhancement. Experimental results show that the proposed technique gives better results in terms of Discrete Entropy, Signal to Noise ratio and Mean Squared Error values than the Global and Local histogram-based equalization methods

scholarly journals Non-Linear Hopped Chaos Parameters-Based Image Encryption Algorithm Using Histogram Equalization

Entropy ◽  
2021 ◽  
Vol 23 (5) ◽  
pp. 535
Author(s):  
Karim H. Moussa ◽  
Ahmed I. El Naggary ◽  
Heba G. Mohamed
Keyword(s):  
Image Encryption ◽  
Initial Conditions ◽  
Signal To Noise Ratio ◽  
Chaotic Map ◽  
Histogram Equalization ◽  
Encryption Algorithm ◽  
Multimedia Contents ◽  
Encryption Schemes ◽  
Change Intensity ◽  
Increasing Demand

Multimedia wireless communications have rapidly developed over the years. Accordingly, an increasing demand for more secured media transmission is required to protect multimedia contents. Image encryption schemes have been proposed over the years, but the most secure and reliable schemes are those based on chaotic maps, due to the intrinsic features in such kinds of multimedia contents regarding the pixels’ high correlation and data handling capabilities. The novel proposed encryption algorithm introduced in this article is based on a 3D hopping chaotic map instead of fixed chaotic logistic maps. The non-linearity behavior of the proposed algorithm, in terms of both position permutation and value transformation, results in a more secured encryption algorithm due to its non-convergence, non-periodicity, and sensitivity to the applied initial conditions. Several statistical and analytical tests such as entropy, correlation, key sensitivity, key space, peak signal-to-noise ratio, noise attacks, number of pixels changing rate (NPCR), unified average change intensity randomness (UACI), and others tests were applied to measure the strength of the proposed encryption scheme. The obtained results prove that the proposed scheme is very robust against different cryptography attacks compared to similar encryption schemes.

scholarly journals Multi-Feature Guided Low-Light Image Enhancement

2021 ◽  
Vol 11 (11) ◽  
pp. 5055
Author(s):  
Hong Liang ◽  
Ankang Yu ◽  
Mingwen Shao ◽  
Yuru Tian
Keyword(s):  
Image Enhancement ◽  
Signal To Noise Ratio ◽  
Low Light ◽  
Low Contrast ◽  
Light Image ◽  
Affine Model ◽  
The Neural Network ◽  
Low Visibility ◽  
Model Robustness ◽  
Traditional Image

Due to the characteristics of low signal-to-noise ratio and low contrast, low-light images will have problems such as color distortion, low visibility, and accompanying noise, which will cause the accuracy of the target detection problem to drop or even miss the detection target. However, recalibrating the dataset for this type of image will face problems such as increased cost or reduced model robustness. To solve this kind of problem, we propose a low-light image enhancement model based on deep learning. In this paper, the feature extraction is guided by the illumination map and noise map, and then the neural network is trained to predict the local affine model coefficients in the bilateral space. Through these methods, our network can effectively denoise and enhance images. We have conducted extensive experiments on the LOL datasets, and the results show that, compared with traditional image enhancement algorithms, the model is superior to traditional methods in image quality and speed.

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

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.

Contrast Enhancement Using Optimum Threshold Selection

2020 ◽  
Vol 8 (3) ◽  
pp. 96-118
Author(s):  
Geeta Rani ◽  
Monika Agarwal
Keyword(s):  
Contrast Enhancement ◽  
Maximum Entropy ◽  
Dynamic Range ◽  
Threshold Value ◽  
Histogram Equalization ◽  
Threshold Method ◽  
Essential Information ◽  
Low Contrast ◽  
Brightness Preservation ◽  
Double Threshold

In the recent era, a boom was observed in the field of information retrieval from images. Digital images with high contrast are sources of abundant information. The gathered information is useful in the precise detection of an object, event, or anomaly captured in an image scene. Existing systems do uniform distribution of intensities and apply intensity histogram equalization. These improve the characteristics of an image in terms of visual appearance. The problem of over enhancement and the increase in noise level produces undesirable visual artefacts. The use of Otsu's single threshold method in existing systems is inefficient for segmenting an image with multiple objects and complex background. Additionally, these are incapable to improve the yield of the maximum entropy and brightness preservation. The aforementioned limitations motivate us to propose an efficient statistical pipelined approach, the Range Limited Double Threshold Weighted Histogram Equalization (RLDTWHE). This approach is an integration of Otsu's double threshold, dynamic range stretching, weighted distribution, adaptive gamma correction, and homomorphic filtering. It provides optimum contrast enhancement by selecting the best appropriate threshold value for image segmentation. The proposed approach is efficient in the enhancement of low contrast medical MRI images and digital natural scene images. It effectively preserves all essential information recorded in an image. Experimental results prove its efficacy in terms of maximum entropy preservation, brightness preservation, contrast enhancement, and retaining the natural appearance of an image.

An Improved Cuckoo Search based Optimal Ranged Brightness Preserved Histogram Equalization and Contrast Stretching Method

2017 ◽  
Vol 8 (1) ◽  
pp. 1-29 ◽  
Author(s):  
Krishna Gopal Dhal ◽  
Md. Iqbal Quraishi ◽  
Sanjoy Das
Keyword(s):  
Objective Function ◽  
Optimization Problems ◽  
Signal To Noise Ratio ◽  
Cuckoo Search ◽  
Histogram Equalization ◽  
Chaotic Sequence ◽  
Population Diversity ◽  
Optimal Range ◽  
Novel Variant ◽  
Contrast Stretching

This paper is organized into two main parts. In the first part, two methods have been discussed to preserve the original brightness of the image which are Parameterized transformation function and a novel variant of modified Histogram Equalization (HE) method. In this study both methods have been formulated as optimization problems to increase the efficiency of the corresponding methods within reasonable time. In the second part, a novel modified version of Cuckoo Search (CS) algorithm has been devised by using chaotic sequence, population diversity information etc to solve those formulated optimization problems. A new Co-occurrence matrix's features based objective function is also devised to preserve the original brightness. Peak-signal to noise ratio (PSNR) acts as objective function to find optimal range of enhanced images. Experimental results prove the supremacy of the proposed CS over traditional CS algorithm.

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