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.

A Hybrid Modified Sine Cosine Algorithm Using Inverse Filtering and Clipping Methods for Low Autocorrelation Binary Sequences

The essential purpose of radar is to detect a target of interest and provide information concerning the target’s location,motion,size,and other parameters.The knowledge about the pulse trains’properties shows that a class of signals is mainly well suited to digital processing of increasing practical importance.A low autocorrelation binary sequence(LABS)is a complex combinatorial problem.The main problems of LABS are low Merit Factor(MF)and shorter length sequences.Besides,the maximum possible MF equals 12.3248 as infinity length is unable to be achieved.Therefore,this study implemented two techniques to propose a new metaheuristic algorithm based on Hybrid Modified Sine Cosine Algorithm with Cuckoo Search Algorithm(HMSCACSA)using Inverse Filtering(IF)and clipping method to achieve better results.The proposed algorithms,LABS-IF and HMSCACSA-IF,achieved better results with two large MFs equal to 12.12 and 12.6678 for lengths 231 and 237,respectively,where the optimal solutions belong to the skew-symmetric sequences.The MF outperformed up to 24.335%and 2.708%against the state-of-the-art LABS heuristic algorithm,xLastovka,and Golay,respectively.These results indicated that the proposed algorithm’s simulation had quality solutions in terms of fast convergence curve with better optimal means,and standard deviation.

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.

Cervical cancer situation in Malaysia:A systematic literature review

Cervix cancer is one of Malaysia’s most significant cancers for women(around 12.9%,with an age-standardised incidence rate of 19.7 per 100,000).It was higher than other Asian,West,and even worldwide nations.The National Strategic Plan for Cancer Control Program 2016–2020(Health Ministry)was presented to minimize cancer and mortality.The high incidence of cervical cancer in Malaysia is mainly due to women’s insufficient knowledge about its prevention and importance.Compared with traditional literature reviews,the systemic analysis provides many advantages.A clearer review process,a more prominent field of study,and essential priorities that can manage research bias can all help to enhance these reviews.However,better integration,cooperation,and coordination between government and private sector as well as NGOs and professional organisations are essential for optimal cancer control and treatment across the country.

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.