scholarly journals Panoramic Dental Radiography Image Enhancement Using Multiscale Mathematical Morphology

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3110
Author(s):  
Julio César Mello Román ◽  
Vicente R. Fretes ◽  
Carlos G. Adorno ◽  
Ricardo Gariba Silva ◽  
José Luis Vázquez Noguera ◽  
...  
Keyword(s):  
Contrast Enhancement ◽  
Mathematical Morphology ◽  
Panoramic Radiography ◽  
Histogram Equalization ◽  
Dental Radiography ◽  
Panoramic Radiographs ◽  
Image Histogram ◽  
Multi Scale ◽  
Dental Radiographs

Panoramic dental radiography is one of the most used images of the different dental specialties. This radiography provides information about the anatomical structures of the teeth. The correct evaluation of these radiographs is associated with a good quality of the image obtained. In this study, 598 patients were consecutively selected to undergo dental panoramic radiography at the Department of Radiology of the Faculty of Dentistry, Universidad Nacional de Asunción. Contrast enhancement techniques are used to enhance the visual quality of panoramic dental radiographs. Specifically, this article presents a new algorithm for contrast, detail and edge enhancement of panoramic dental radiographs. The proposed algorithm is called Multi-Scale Top-Hat transform powered by Geodesic Reconstruction for panoramic dental radiography enhancement (MSTHGR). This algorithm is based on multi-scale mathematical morphology techniques. The proposal extracts multiple features of brightness and darkness, through the reconstruction of the marker (obtained by the Top-Hat transformation by reconstruction) starting from the mask (obtained by the classic Top-Hat transformation). The maximum characteristics of brightness and darkness are added to the dental panoramic radiography. In this way, the contrast, details and edges of the panoramic radiographs of teeth are improved. For the tests, MSTHGR was compared with the following algorithms: Geodesic Reconstruction Multiscale Morphology Contrast Enhancement (GRMMCE), Histogram Equalization (HE), Brightness Preserving Bi-Histogram Equalization (BBHE), Dual Sub-Image Histogram Equalization (DSIHE), Minimum Mean Brightness Error Bi-Histogram Equalization (MMBEBHE), Quadri-Histogram Equalization with Limited Contrast (QHELC), Contrast-Limited Adaptive Histogram Equalization (CLAHE) and Gamma Correction (GC). Experimentally, the numerical results show that the MSTHGR obtained the best results with respect to the Contrast Improvement Ratio (CIR), Entropy (E) and Spatial Frequency (SF) metrics. This indicates that the algorithm performs better local enhancements on panoramic radiographs, improving their details and edges.

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

DIGITAL MAMMOGRAM ENHANCEMENT USING KAPUR MEASURE OF ENTROPY AND MATHEMATICAL MORPHOLOGY

2013 ◽  
Vol 25 (03) ◽  
pp. 1350029 ◽  
Author(s):  
Baljit Singh Khehra ◽  
Amar Partap Singh Pharwaha
Keyword(s):  
Breast Cancer ◽  
Image Analysis ◽  
Early Detection ◽  
Contrast Enhancement ◽  
Mathematical Morphology ◽  
Signal To Noise Ratio ◽  
Histogram Equalization ◽  
Signal To Noise ◽  
Mammogram Image ◽  
Background Contrast

Mammography is the most reliable, effective, low cost and highly sensitive method for early detection of breast cancer. Mammogram analysis usually refers to the processing of mammograms with the goal of finding abnormality presented in the mammogram. Mammogram enhancement is one of the most critical tasks in automatic mammogram image analysis. Main purpose of mammogram enhancement is to enhance the contrast of details and subtle features while suppressing the background heavily. In this paper, a hybrid approach is proposed to enhance the contrast of microcalcifications while suppressing the background heavily, using fuzzy logic and mathematical morphology. First, mammogram is fuzzified using Gaussian fuzzy membership function whose bandwidth is computed using Kapur measure of entropy. After this, mathematical morphology is applied on fuzzified mammogram. Mathematical morphology provides tools for the extraction of microcalcifications even if the microcalcifications are located on a nonuniform background. Main advantage of Kapur measure of entropy over Shannon entropy is that Kapur measure of entropy has α and β parameters that can be used as adjustable values. These parameters can play an important role as tuning parameters in the image processing chain for the same class of images. Experiments have been conducted on images of mini-Mammogram Image Analysis Society (MIAS) database (UK). Experiment results of the proposed approach are compared with histogram equalization (HE), contrast limited adaptive histogram equalization (CLAHE) and fuzzy histogram hyperbolization (FHH) which are well-established image enhancement techniques. In order to validate the results, several different kinds of standard test images (fatty, fatty-glandular and dense-glandular) of mini-MIAS database are considered. Objective image quality assessment parameters: Target-to-background contrast enhancement measurement based on standard deviation (TBCSD), target-to-background contrast enhancement measurement based on entropy (TBCE), contrast improvement index (CII), peak signal-to-noise ratio (PSNR) and average signal-to-noise ratio (ASNR) are used to evaluate the performance of proposed approach. The experimental results show that the proposed approach performs well. This study can be a part of developing a computer-aided diagnosis (CAD) system for early detection of breast cancer.

scholarly journals Feature and Contrast Enhancement of Mammographic Image Based on Multiscale Analysis and Morphology

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Shibin Wu ◽  
Shaode Yu ◽  
Yuhan Yang ◽  
Yaoqin Xie
Keyword(s):  
Contrast Enhancement ◽  
Mathematical Morphology ◽  
Multiscale Analysis ◽  
Histogram Equalization ◽  
Mammographic Images ◽  
Adaptive Histogram Equalization ◽  
Gaussian Pyramid ◽  
Low Pass ◽  
Multiscale Transform ◽  
Improvement Index

A new algorithm for feature and contrast enhancement of mammographic images is proposed in this paper. The approach bases on multiscale transform and mathematical morphology. First of all, the Laplacian Gaussian pyramid operator is applied to transform the mammography into different scale subband images. In addition, the detail or high frequency subimages are equalized by contrast limited adaptive histogram equalization (CLAHE) and low-pass subimages are processed by mathematical morphology. Finally, the enhanced image of feature and contrast is reconstructed from the Laplacian Gaussian pyramid coefficients modified at one or more levels by contrast limited adaptive histogram equalization and mathematical morphology, respectively. The enhanced image is processed by global nonlinear operator. The experimental results show that the presented algorithm is effective for feature and contrast enhancement of mammogram. The performance evaluation of the proposed algorithm is measured by contrast evaluation criterion for image, signal-noise-ratio (SNR), and contrast improvement index (CII).

scholarly journals Clinical Evaluation of Dose Reduction on Image Quality of Panoramic Radiographs

2008 ◽  
Vol 9 (5) ◽  
pp. 34-41 ◽  
Author(s):  
Meryem Toraman Alkurt ◽  
Likay Peker ◽  
Gülten Usalan ◽  
Bülent Altunkaynak
Keyword(s):  
Image Quality ◽  
Dose Reduction ◽  
Clinical Evaluation ◽  
Panoramic Radiography ◽  
Panoramic Radiographs ◽  
Tube Current ◽  
Digital Panoramic Radiography ◽  
Significant Difference ◽  
Panoramic Images

Abstract Aim The purpose of this study was to evaluate the effect of tube current reduction on image quality using medium and regular intensifying screens as well as a digital system for panoramic radiography. Methods and Materials A total of 150 panoramic images of 75 patients were obtained in the study. The initial images were taken at standard exposure settings, and secondary images were exposed with the tube current reduced at different rates. Results There was no statistically significant difference (p>0.05) between the two exposures for Group 3 (the rate of dose reduction 25%) while a statistically significant difference (p<0.05) was found in Group 4 (the rate of dose reduction 50%) using medium intensifying screens for all observers. No statistically significant difference was found between the two exposures on digital panoramic images. Conclusion According to the results of this study a dose reduction of 25% was achieved for medium intensifying screens and for digital panoramic images without any loss of image quality. Clinical Significance A substantial reduction in radiation exposure can be achieved in conventional panoramic radiography using a medium intensifying screen and in digital panoramic radiography without any loss of image quality needed for radiological evaluation of anatomical structures and pathological conditions. Citation Alkurt MT, Peker I, Usalan G, Altunkaynak B. Clinical Evaluation of Dose Reduction on Image Quality of Panoramic Radiographs. J Contemp Dent Pract 2008 July; (9)5:034-041.

scholarly journals Contrast enhancement of infrared images using Adaptive Histogram Equalization (AHE) with Contrast Limited Adaptive Histogram Equalization (CLAHE)

2018 ◽  
Vol 16 (37) ◽  
pp. 127-135
Author(s):  
Loay Kadom Abood
Keyword(s):  
Contrast Enhancement ◽  
Infrared Imaging ◽  
Experimental Tests ◽  
Histogram Equalization ◽  
Night Vision ◽  
Infrared Images ◽  
Adaptive Histogram Equalization ◽  
General Quality ◽  
Improvement Procedures

The objective of this paper is to improve the general quality of infrared images by proposes an algorithm relying upon strategy for infrared images (IR) enhancement. This algorithm was based on two methods: adaptive histogram equalization (AHE) and Contrast Limited Adaptive Histogram Equalization (CLAHE). The contribution of this paper is on how well contrast enhancement improvement procedures proposed for infrared images, and to propose a strategy that may be most appropriate for consolidation into commercial infrared imaging applications.The database for this paper consists of night vision infrared images were taken by Zenmuse camera (FLIR Systems, Inc) attached on MATRIC100 drone in Karbala city. The experimental tests showed significant improvements.

scholarly journals Real-Time Jellyfish Classification and Detection Based on Improved YOLOv3 Algorithm

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8160
Author(s):  
Meijing Gao ◽  
Yang Bai ◽  
Zhilong Li ◽  
Shiyu Li ◽  
Bozhi Zhang ◽  
...  
Keyword(s):  
Real Time ◽  
Detection Method ◽  
Histogram Equalization ◽  
Detection Accuracy ◽  
Data Set ◽  
Marine Fishery ◽  
Multi Scale ◽  
Underwater Image ◽  
Neural Network Theory

In recent years, jellyfish outbreaks have frequently occurred in offshore areas worldwide, posing a significant threat to the marine fishery, tourism, coastal industry, and personal safety. Effective monitoring of jellyfish is a vital method to solve the above problems. However, the optical detection method for jellyfish is still in the primary stage. Therefore, this paper studies a jellyfish detection method based on convolution neural network theory and digital image processing technology. This paper studies the underwater image preprocessing algorithm because the quality of underwater images directly affects the detection results. The results show that the image quality is better after applying the three algorithms namely prior defogging, adaptive histogram equalization, and multi-scale retinal enhancement, which is more conducive to detection. We establish a data set containing seven species of jellyfishes and fish. A total of 2141 images are included in the data set. The YOLOv3 algorithm is used to detect jellyfish, and its feature extraction network Darknet53 is optimized to ensure it is conducted in real-time. In addition, we introduce label smoothing and cosine annealing learning rate methods during the training process. The experimental results show that the improved algorithms improve the detection accuracy of jellyfish on the premise of ensuring the detection speed. This paper lays a foundation for the construction of an underwater jellyfish optical imaging real-time monitoring system.

Entropy based Range Optimized Brightness Preserved Histogram-Equalization for Image Contrast Enhancement

2016 ◽  
Vol 6 (1) ◽  
pp. 59-72 ◽  
Author(s):  
Krishna Gopal Dhal ◽  
Sankhadip Sen ◽  
Kaustav Sarkar ◽  
Sanjoy Das
Keyword(s):  
Contrast Enhancement ◽  
Signal To Noise Ratio ◽  
Histogram Equalization ◽  
Image Contrast ◽  
Experimental Results ◽  
Signal To Noise ◽  
Image Histogram ◽  
Image Contrast Enhancement ◽  
Noise Ratio

In this study the over-enhancement problem of traditional Histogram-Equalization (HE) has been removed to some extent by a variant of HE called Range Optimized Entropy based Bi-Histogram Equalization (ROEBHE). In ROEBHE image histogram has been thresholded into two sub-histograms i.e. histograms corresponding to background and foreground. The threshold is calculated by maximizing the sum of the entropy of these two sub-histograms. The range for equalization has been optimized by maximizing the Peak-Signal to Noise ratio (PSNR). The experimental results prove that ROEBHE has prevailed over existing methods and PSNR is a better range optimizer than Absolute Mean Brightness Error (AMBE).

scholarly journals Contrast Enhancement of MRI Images using AHE and CLAHE Techniques

2019 ◽  
Vol 9 (2) ◽  
pp. 2442-2445 ◽  
Keyword(s):  
Image Processing ◽  
Contrast Enhancement ◽  
Image Enhancement ◽  
Medical Image ◽  
Histogram Equalization ◽  
Mri Scan ◽  
Adaptive Histogram Equalization ◽  
Shadowing Effect ◽  
Suitable Technique

Medical images require image enhancement, a category of image processing which provides better visualization that make diagnostic more accurate. The most commonly used method for improving the quality of medical image is Contrast enhancement.The main objective is to eliminate the use of contrast dye during the process of MRI scan and to find the parameters MSE, PSNR, AMBE and contrast and compare the result. The histogram equalization (HE) is the widely accepted method which is not productive when the contrast nature differs across the image. Adaptive Histogram Equalization (AHE) overcomes this limitation by considering and developing the mapping for each pixel from the histogram in a neighboring window. Another suitable technique is CLAHE. CLAHE is a refinement of AHE where the enhancement calculation is modified by imposing a user specified level to the height of local histogram. The enhancement is thereby reduced in very uniform areas of the image, which prevents over enhancement of noise and reduces the edge shadowing effect of unlimited AHE. After enhancing the image using AHE and CLAHE the comparison of their parameters is performed.

scholarly journals Identification of Suitable Contrast Enhancement Technique for Improving the Quality of Astrocytoma Histopathological Images.

2021 ◽  
Vol 20 (1) ◽  
pp. 84-98
Author(s):  
FAHMI AKMAL DZULKIFLI
Keyword(s):  
Image Processing ◽  
Contrast Enhancement ◽  
Histogram Equalization ◽  
Enhancement Technique ◽  
Brightness Preservation ◽  
Microscopic Images ◽  
Histopathological Images ◽  
Contrast Stretching

Contrast enhancement plays an important part in image processing. In histology, the application of a contrast enhancement technique is necessary since it can help pathologists in diagnosing the sample slides by increasing the visibility of the morphological and features of cells in an image. Various techniques have been proposed to enhance the contrast of microscopic images. Thus, this paper aimed to study the effectiveness of contrast enhancement techniques in enhancing the Ki67 images of astrocytoma. Three contrast enhancement techniques consist of contrast stretching, histogram equalization, and CLAHE techniques were proposed to enhance the sample images. The performance of each technique was compared by computing seven quantitative measures. The CLAHE technique was preferred for enhancing the contrast of the astrocytoma images. This technique produces good results especially in contrast enhancement, edge conservation and enhancement, brightness preservation, and minimum distortions to the enhanced images. 

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