Effect of Direct Statistical Contrast Enhancement Technique on Document Image Binarization

Background:Contrast enhancement plays an important role in the image processing field.Contrast correction has performed an adjustment on the darkness or brightness of the input image and increases the quality of the image.Objective:This paper proposed a novel method based on statistical data from the local mean and local standard deviation.Method:The proposed method modifies the mean and standard deviation of a neighbourhood at each pixel and divides it into three categories:background,foreground,and problematic(contrast&luminosity)region.Experimental results from both visual and objective aspects show that the proposed method can normalize the contrast variation problem effectively compared to Histogram Equalization(HE),Difference of Gaussian(DoG),and Butterworth Homomorphic Filtering(BHF).Seven(7)types of binarization methods were tested on the corrected image and produced a positive and impressive result.Result:Finally,a comparison in terms of Signal Noise Ratio(SNR),Misclassification Error(ME),F-measure,Peak Signal Noise Ratio(PSNR),Misclassification Penalty Metric(MPM),and Accuracy was calculated.Each binarization method shows an incremented result after applying it onto the corrected image compared to the original image.The SNR result of our proposed image is 9.350 higher than the three(3)other methods.The average increment after five(5)types of evaluation are:(Otsu=41.64%,Local Adaptive=7.05%,Niblack=30.28%,Bernsen=25%,Bradley=3.54%,Nick=1.59%,Gradient-Based=14.6%).Conclusion:The results presented in this paper effectively solve the contrast problem and finally produce better quality images.

Electrical Tree Image Segmentation Using Hybrid Multi Scale Line Tracking Algorithm

Electrical trees are an aging mechanismmost associated with partial discharge(PD)activities in crosslinked polyethylene(XLPE)insulation of high-voltage(HV)cables.Characterization of electrical tree structures gained considerable attention from researchers since a deep understanding of the tree morphology is required to develop new insulation material.Two-dimensional(2D)optical microscopy is primarily used to examine tree structures and propagation shapes with image segmentation methods.However,since electrical trees can emerge in different shapes such as bush-type or branch-type,treeing images are complicated to segment due to manifestation of convoluted tree branches,leading to a high misclassification rate during segmentation.Therefore,this study proposed a new method for segmenting 2D electrical tree images based on the multi-scale line tracking algorithm(MSLTA)by integrating batch processing method.The proposed method,h-MSLTA aims to provide accurate segmentation of electrical tree images obtained over a period of tree propagation observation under optical microscopy.The initial phase involves XLPE sample preparation and treeing image acquisition under real-time microscopy observation.The treeing images are then sampled and binarized in pre-processing.In the next phase,segmentation of tree structures is performed using the h-MSLTA by utilizing batch processing in multiple instances of treeing duration.Finally,the comparative investigation has been conducted using standard performance assessment metrics,including accuracy,sensitivity,specificity,Dice coefficient and Matthew’s correlation coefficient(MCC).Based on segmentation performance evaluation against several established segmentation methods,h-MSLTA achieved better results of 95.43%accuracy,97.28%specificity,69.43%sensitivity rate with 23.38%and 24.16%average improvement in Dice coefficient and MCC score respectively over the original algorithm.In addition,h-MSLTA produced accurate measurement results of global tree parameters of length and width in comparison with the ground truth image.These results indicated that the proposed method had a solid performance in terms of segmenting electrical tree branches in 2D treeing images compared to other established techniques.

Contrast Correction Using Hybrid Statistical Enhancement on Weld Defect Images

Luminosity and contrast variation problems are among the most challenging tasks in the image processing field,significantly improving image quality.Enhancement is implemented by adjusting the dark or bright intensity to improve the quality of the images and increase the segmentation performance.Recently,numerous methods had been proposed to normalise the luminosity and contrast variation.A new approach based on a direct technique using statistical data known as Hybrid Statistical Enhancement(HSE)is presented in this study.TheHSE method uses themean and standard deviation of a local and global neighbourhood and classified the pixel into three groups;the foreground,border,and problematic region(contrast&luminosity).The datasets,namely weld defect images,were utilised to demonstrate the effectiveness of the HSE method.The results from the visual and objective aspects showed that the HSE method could normalise the luminosity and enhance the contrast variation problem effectively.The proposed method was compared to the two(2)populor enhancement methods which is Homomorphic Filter(HF)and Difference of Gaussian(DoG).To prove the HSE effectiveness,a few image quality assessments were presented,and the results were discussed.The HSE method achieved a better result compared to the other methods,which are Signal Noise Ratio(8.920),Standard Deviation(18.588)and Absolute Mean Brightness Error(9.356).In conclusion,implementing the HSE method has produced an effective and efficient result for background correction and quality images improvement.

Improvement method for cervical cancer detection: A comparative analysis

Cervical cancer is a prevalent and deadly cancer that affects women all over the world.It affects about 0.5 million women anually and results in over 0.3 million fatalities.Diagnosis of this cancer was previously done manually,which could result in false positives or negatives.The researchers are still contemplating how to detect cervical cancer automatically and how to evaluate Pap smear images.Hence,this paper has reviewed several detection methods from the previous researches that has been done before.This paper reviews pre-processing,detection method framework for nucleus detection,and analysis performance of the method selected.There are four methods based on a reviewed technique from previous studies that have been running through the experimental procedure using Matlab,and the dataset used is established Herlev Dataset.The results show that the highest performance assessment metric values obtain from Method 1:Thresholding and Trace region boundaries in a binary image with the values of precision 1.0,sensitivity 98.77%,specificity 98.76%,accuracy 98.77%and PSNR 25.74%for a single type of cell.Meanwhile,the average values of precision were 0.99,sensitivity 90.71%,specificity 96.55%,accuracy 92.91%and PSNR 16.22%.The experimental results are then compared to the existing methods from previous studies.They show that the improvement method is able to detect the nucleus of the cell with higher performance assessment values.On the other hand,the majority of current approaches can be used with either a single or a large number of cervical cancer smear images.This study might persuade other researchers to recognize the value of some of the existing detection techniques and offer a strong approach for developing and implementing new solutions.

Zero-Index Metamaterial Superstrates UWB Antenna for Microwave Imaging Detection

Metamaterials(MTM)can enhance the properties of microwaves and also exceed some limitations of devices used in technical practice.Note that the antenna is the element for realizing a microwave imaging(MWI)system since it is where signal transmission and absorption occur.Ultra-Wideband(UWB)antenna superstrates with MTM elements to ensure the signal transmitted from the antenna reaches the tumor and is absorbed by the same antenna.The lack of conventional head imaging techniques,for instance,Magnetic Resonance Imaging(MRI)and Computerized Tomography(CT)-scan,has been demonstrated in the paper focusing on the point of failure of these techniques for prompt diagnosis and portable systems.Furthermore,the importance ofMWIhas been addressed elaborately to portray its effectiveness and aptness for a primary tumor diagnosis.Other than that,MTM element designs have been discussed thoroughly based on their performances towards the contributions to the better image resolution of MWI with detailed reasonings.This paper proposes the novel design of a Zeroindex Split RingResonator(SRR)MTMelement superstrate with a UWB antenna implemented in MWI systems for detecting tumor.The novel design of the MTM enables the realization of a high gain of a superstrate UWB antenna with the highest gain of 5.70 dB.Besides that,the MTM imitates the conduct of the zeroreflection phase on the resonance frequency,which does not exist.An antenna with an MTM unit is of a 7×4 and 10×5 Zero-index SRR MTM element that acts as a superstrate plane to the antenna.Apart from that,Rogers(RT5880)substrate material is employed to fabricate the designed MTM unit cell,with the following characteristics:0.51mm thickness,the loss tangent of 0.02,as well as the relative permittivity of 2.2,with Computer Simulation Technology(CST)performing the simulation and design.Both MTM unit cells of 7×4 and 10×5 attained 0°with respect to the reflection phase at the 2.70 GHz frequency band.The first design,MTM Antenna Design 1,consists of a 7×4 MTM unit cell that observed a rise of 5.70 dB with a return loss(S11)−20.007 dB at 2.70 GHz frequency.The second design,MTM Antenna Design 2,consists of 10×5 MTM unit cells that recorded a gain of 5.66 dB,having the return loss(S11)−19.734 dB at 2.70 GHz frequency.Comparing these two MTM elements superstrates with the antenna,one can notice that the 7×4 MTM element shape has a low number of the unit cell with high gain and is a better choice than the 10×5 MTM element in realizing MTM element superstrates antenna for MWI.