Hat Transformation Research Articles

Earlier smart prediction of diabetic retinopathy from fundus image under innovative ResNet optimization maneuver

AbstractDiabetic retinopathy (DR) is a complication of diabetes that causes blindness and the early detection of diabetics using retinopathy images remains a challenging task. Hence, a novel, earlier smart prediction of diabetic retinopathy from fundus image under Innovative ResNet Optimization is introduced to effectively detect the earlier stage of DR from Fundus image. Initially, the fundus image is scaled during preprocessing and converted into a grayscale format. As the existing studies neglect some deserving unique features that are crucial for predicting the earliest signs of DR, a novel Fractional Radon Transform with Visibility Graph is introduced for extracting the novel features such as microaneurysms count, dot and blot hemorrhages count, statistical measures, and retinal layer thickness, in which a Generalized Cosine Fractional Radon Transform is used to capture the image’s fine-scale texture information thereby effectively capturing the statistical measures, while a weighted Horizontal Visibility Graph is made to examine the apparent spatial relationships between pixel pairs in the image based on the values of the pixels’ gray levels. Further, the existing works failed to identify the small fine dark areas that were ignored throughout the morphological opening process. In order to overcome this issue, a Morphological Black Hat Transform with Optimized ResNet Algorithm is implemented, where segmentation is made through Enriched Black Hat Transform-based Morphological operation to identify fine dark regions among the pixels inside the eye samples, and the classification is done by using ResNet-driven S-GOA (Socio Grasshopper Optimization Algorithm), to optimally predict the stages of DR. The result obtained showed that the proposed model outperforms existing techniques with high performance and accuracy.

Synthesis of Primary Amines via Hydrogen Atom Transfer-Initiated Cyclization/Reduction Cascade of Unsaturated Nitriles.

We report a hydrogen atom transfer-initiated cyclization/reduction cascade for the synthesis of primary amines from δ,ε- and ε,ζ-unsaturated nitriles. The HAT transformation employs Mn(acac)3 as a catalyst and utilizes air as an oxidant along with NaBH4 as a dual-purpose reductant toward the olefin and subsequently C═N. Aromatic and aliphatic nitriles incorporating mono-, di-, and trisubstituted olefins are substrates for the reaction. Starting materials bearing malonates are transformed into the corresponding bicyclic lactams, enabling the rapid buildup of structural complexity.

Extraction and restoration of scratched murals based on hyperspectral imaging—a case study of murals in the East Wall of the sixth grotto of Yungang Grottoes, Datong, China

Restoring the murals' various kinds of deteriorations is urgently necessary given the growing awareness of the need to protect cultural relics. Virtual restoration starts with an accurate extraction of deterioration. It is challenging to precisely extract scratches from murals because of their intricate information. Hyperspectral images are used to accentuate scratches of mural in this paper. First, a technique for improving information was put forth that involved the transformation of Principal Component Analysis (PCA) and a high-pass filter. Second, by using multi-scale bottom hat transformation, Otsu threshold segmentation, and non-deterioration mask, the deterioration information was extracted from the enhanced result. Third, the morphological transformation and connected component analysis were used to denoise the extracted results. Additionally, the scratched image was repaired using an improved exemplar-based region filling method. The results of deterioration information under different enhancement methods were discussed, and the deterioration extraction method proposed in this paper was contrasted with other deterioration extraction methods. The extraction accuracy was greatly increased by the suggested method. Additionally, we assessed the accuracy of various virtual restoration techniques for image restoration and discovered that our suggested restoration method did a good job of maintaining the structural integrity of the mural's information.

A novel chaotic weighted EHO-based methodology for retinal vessel segmentation

ABSTRACT Retinal image segmentation process deals withproblems like spurious vascularisation and thin vessel detection. In thispaper, a three-step methodology has been proposed for retinal vesselsegmentation. In first step, RGB to YIQ conversion is performed. In second step, Y component enhancement is performed. A novel Chaotic weighted Elephant HerdingOptimization (CWEHO) has been proposed to optimize the clip limit and blocksize values of Contrast Limited Adaptive Histogram Equalization (CLAHE). CWEHO-based CLAHE along with morphological operations, non-local means filter, and median filter is applied to enhanceretinal images. In third step, thin and thick vessel segmentation is performed.Top hat transformation, otsu thresholding algorithm, and vessel point selectionare applied for thick vessel extraction. The first-order Gaussian derivative in conjunction with the matchfilter is used to extract thin vessels. DRIVE and HRF datasets are used toassess the effectiveness of proposed methodology. The average values ofsegmentation accuracy, specificity, sensitivity, and Mathew CorrelationCoefficient (MCC) are observed to be 0.9650, 0.9895, 0.7007, 0.7650,respectively, for observer1 and 0.9696, 0.9912, 0.7390, 0.7901 for observer2using DRIVE dataset. Similarly, 0.9592, 0.9839, 0.6850, and 0.7116,respectively, metrics for HRF dataset. Compared to state-of-the-art methods, the proposed segmentation methodology providesbetter results .

Coordination-Induced Radical Generation: Selective Hydrogen Atom Abstraction via Controlled Ti-C σ-Bond Homolysis.

A method for the generation of transient alkyl radicals via homolytic Ti-C bond cleavage was developed by employing a tailor-made organotitanium half-cage complex. In contrast to established metal-mediated radical initiation protocols via thermal or photochemical M-C σ-bond homolysis, radical formation is triggered solely by coordination of a solvent molecule (thf) to a titanium(IV) center. During the reaction, the nonstabilized alkyl radical is formed along with a persistent titanium(III) metalloradical, thus taming the former transient radical (persistent radical effect). Radical coupling and hydrogen atom abstraction (HAT) reactions have been explored not only experimentally but also computationally and by means of kinetic analysis. Exploiting these findings led to the development of selective HAT transformations, for example, with 9,10-dihydroanthracene. Deuterium labeling studies using selectively deuterated alkyls and 9,10-dihydroanthracene-d4 confirmed a radical pathway, which was underpinned by developing a radical-radical cross-coupling reaction for transferring the alkyl radical to a stable Sn-centered radical. To set the stage for an application in organic synthesis, a 5-endo-trig radical cyclization based on our methodology was established, and a dihydroxylated sesquiterpene was thus prepared in high diastereomeric excess.

The Combination of Black Hat Transform and U-Net in Image Enhancement and Blood Vessel Segmentation in Retinal Images

Diabetic Retinopathy (DR) is a disorder of the eye caused by damage to blood vessels in the retina. Damage to the retinal blood vessels can be analyzed by segmenting the blood vessels on the image. This study proposes a combination of image enhancement and blood vessel segmentation in retinal images. Retinal image enhancement is carried out using the black hat transform method to obtain a detailed view of blood vessels in retinal images. Segmentation of blood vessels in retinal images is carried out using the U-Net architecture. The results of image enhancement are measured using MSE and PSNR. This study has an MSE value below 0.05 and a PSNR above 90dB. The MSE and PSNR values obtained show that the black hat transform method is very good at image enhancement. Segmentation has an accuracy value above 0.95 and a sensitivity value above 0.85. In addition, the specificity value and f1-score are above 0.8. This shows that the proposed stages of image enhancement and blood vessel segmentation are able to accurately recognize blood vessel features in retinal images.

Quantification of soot nanostructure produced from a diesel engine fueled with C3 ketone

The soot emission is an urgent problem that deserves extensive studies to suppress it. The smallest ketone was investigated in this study as a soot suppressor in a diesel engine. The fringe analysis approach was used to quantify the nanostructure characteristics of soot. At first, the image processing parameters were updated for this study, and their effects were explored and interpreted. Image resolution was found to be a crucial parameter in the image analysis process with a spatial resolution of 0.0417 nm/pixel as the best adjustment for the present study. The contrast-limited adaptive histogram equalization approach provided a perfect histogram equalization. A Gaussian low-pass filter with a standard deviation of 1.5 and a filter size of 11 produced the best results. The bottom hat transformation with a structuring element of a five pixels size and zero corners produced the best fringes' representation. A threshold level of 0.8 as Otsu's level provided a better nanostructure's representation. Second, the images of soot from acetone/diesel blends were analyzed using the updated parameters. The addition of acetone reduced the formation of soot in a diesel engine. The fringe length, tortuosity, intensity and alignment were all decreased, while fringe spacing was increased with acetone.

Comparison of Vehicle License Plate Detection Algorithms and LP Character Segmentation and Recognition using Image Processing

In the last couple of decades, the number of vehicles has increased drastically, consequently, it is becoming difficult to keep track of each vehicle for purpose of law enforcement and traffic management. License Plate Detection is used increasingly nowadays for the same. The system performing the task of License Plate detection is known as the LPR system which generally consists of three steps: Detection of the License plate, Segmentation of License plate characters, and Recognition of the characters of the License Plate (LP). But in real-world scenarios, the various lighting conditions, camera angle, and rotation degrades the accuracy of License Plate region detection, which in turn causes inaccurate segmentation and recognition of the license plate characters hence leading to low accuracy of the LPR systems. Therefore, it is vital to consider the most promising algorithm or technique for LP detection. In this paper, we will be analyzing and comparing five different methods for license plate detection: Morphological reconstruction, Sobel Operator, Top Hat Transform, Histogram processing, and Canny Edge detection. We will be experimentally applying these techniques on real-time captured vehicle images, using the Bounding Box algorithm for character segmentation, performing license plate character recognition using Template matching, and subsequentially evaluating and demonstrating the LPR system that promises the most accurate and efficient results.

Image Pre-processing techniques comparison : COVID-19 detection through Chest X-Rays via Deep Learning

The COVID-19 pandemic had a particularly devastating effect, spreading rapidly over the world and infecting about 36 million individuals. Chest radiography is a critical component that aids in the early detection of a variety of diseases. With the spread of the pandemic, training Convolutional Neural Networks (CNN) to detect and identify COVID-19 from chest X-rays is becoming more popular. However, there are few publicly available and medically validated datasets for COVID-19 infected chest X-Rays, resulting in the model failing to generalize successfully. It is critical to pre-process and enrich the data used to train the model in order to achieve this aim. Global Histogram Equalization (GHE), Contrast Limited Adaptive Histogram Equalization (CLAHE), and Top Bottom Hat Transform are some of the pre-processing techniques available. In this study, we examine and compare all of these pre-processing methods in order to determine which is best for building a CNN model that can accurately classify an image as infected with COVID-19 or Viral Pneumonia.

Quantum Image Segmentation Based on Grayscale Morphology

The classical image segmentation algorithm based on grayscale morphology can effectively segment images with uneven illumination, but with the increase of the image data, the real-time problem will emerge. In order to solve this problem, a quantum image segmentation algorithm is proposed in this paper, which can use quantum mechanism to simultaneously perform morphological operations on all pixels in a grayscale image, and then quickly segment the image into a binary image. In addition, several quantum circuit units, including dilation, erosion, bottom hat transformation, top hat transformation, etc., are designed in detail, and then they are combined together to construct the complete quantum circuits for segmenting the NEQR images. For a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$2^{n} \times 2^{n}$</tex-math></inline-formula> image with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$q$</tex-math></inline-formula> grayscale levels, the complexity of our algorithm can be reduced to O <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$(n^{2}+q)$</tex-math></inline-formula> , which is an exponential speedup than the classic counterparts. Finally, the experiment is conducted on IBM Q to show the feasibility of our algorithm in the noisy intermediate-scale quantum (NISQ) era.

Denoising of ECG Signal using UFIR Smoothing with Notch Filter

Abstract: The electrical activity of the heart is test with an electrocardiogram (ECG). The fundamental information for the taking decision about various types of heart diseases identified by electrocardiogram. There have been numerous attempts over decades to extract the characteristics of the heartbeat through ECG records with high accuracy and efficiency using a variety of strategies and techniques. In this paper a novel scheme is acquainted, the problem is solved by isolated time space using q-lag unbiased finite impulse response (UFIR), then the received time changing of optimal average horizon for the shape of the ECG signal. A complete statistical analysis is furnished by normalized histogram and statistical classifiers, P wave features extraction based on the detected fiducial points is deliberated. In this concept by utilizing QRS detection, morphological top-bottom hat transformation and notch filters is ameliorated PSNR and latency constraints, furnishes high accuracy and reduced elapsed time. Keywords: Electrocardiogram (ECG) denoising, unbiased finite impulse response (UFIR) filtering, P wave feature extraction, normalized histogram, QRS complex detection.

Image Pre-processing techniques comparison: COVID-19 detection through Chest X-Rays via Deep Learning

The COVID-19 pandemic has had a very devastating effect and has spread rapidly across the world affecting close to 36 million people. Chest radiography is a very important feature which is used for early diagnosis of various diseases. With the increasing pandemic, there is a growing popularity of training Convolutional Neural Networks (CNN) to diagnose and detect COVID-19 from Chest X-Rays. However, publicly available and medically verified datasets for COVID-19 infected chest X-Rays are scarce, which results in the model not generalizing properly. For this purpose, it is important to pre-process and augment the data being used to train the model. Various pre-processing techniques exist like Global Histogram Equalization (GHE), Contrast Limited Adaptive Histogram Equalization (CLAHE) and Top Bottom Hat Transform. In this review, we study and compare all these pre-processing techniques to understand which is the most suitable for developing a CNN model which can classify an image as being infected with COVID-19 or Viral Pneumonia with high efficacy.

Optimal feature selection-based diabetic retinopathy detection using improved rider optimization algorithm enabled with deep learning

This proposal tempts to develop automated DR detection by analyzing the retinal abnormalities like hard exudates, haemorrhages, Microaneurysm, and soft exudates. The main processing phases of the developed DR detection model is Pre-processing, Optic Disk removal, Blood vessel removal, Segmentation of abnormalities, Feature extraction, Optimal feature selection, and Classification. At first, the pre-processing of the input retinal image is done by Contrast Limited Adaptive Histogram Equalization. The next phase performs the optic disc removal, which is carried out by open-close watershed transformation. Further, the Grey Level thresholding is done for segmenting the blood vessels and its removal. Once the optic disk and blood vessels are removed, segmentation of abnormalities is done by Top hat transformation and Gabor filtering. Further, the feature extraction phase is started, which tends to extract four sets of features like Local Binary Pattern, Texture Energy Measurement, Shanon’s and Kapur’s entropy. Since the length of the feature vector seems to be long, the feature selection process is done, which selects the unique features with less correlation. Moreover, the Deep Belief Network (DBN)-based classification algorithm performs the categorization of images into four classes normal, earlier, moderate, or severe stages. The optimal feature selection is done by the improved meta-heuristic algorithm called Modified Gear and Steering-based Rider Optimization Algorithm (MGS-ROA), and the same algorithm updates the weight in DBN. Finally, the effectual performance and comparative analysis prove the stable and reliable performance of the proposed model over existing models. The performance of the proposed model is compared with the existing classifiers, such as, NN, KNN, SVM, DBN and the conventional Heuristic-Based DBNs, such as PSO-DBN, GWO-DBN, WOA-DBN, and ROA-DBN for the evaluation metrics, accuracy, sensitivity, specificity, precision, FPR, FNR, NPV, FDR, F1 score, and MC. From the results, it is exposed that the accuracy of the proposed MGS-ROA-DBN is 30.1% higher than NN, 32.2% higher than KNN, and 17.1% higher than SVM and DBN. Similarly, the accuracy of the developed MGS-ROA-DBN is 13.8% superior to PSO, 5.1% superior to GWO, 10.8% superior to WOA, and 2.5% superior to ROA.

Research on Feature Extraction Algorithm of Palm Main Line Based on Bottom Hat Transformation

Aiming at the problem that the main line of palm is not easy to extract, and it is easy to be affected by illumination and uneven color of palm, this paper proposes a feature extraction algorithm based on base The bottom transformation. The bottom cap transform can eliminate the influence of uneven illumination and extract the main line of the palm. The collected palm image is first grayscaled and bottomed, and then binarized, the palm area is separated, the palm area is found, the palm boundary is separated by the Sobel edge detector, and the fingertip valley coordinates are determined. The palm area is divided, the knuckles and palm prints are found, and the vertical projection is used to match the palm print and the knuckle pattern. The experimental results show that the method can eliminate the influence of uneven illumination and uneven color of palm, and it has fast speed and strong anti-noise ability. It is suitable for large-scale palm database to achieve coarse screening.

Research on an Infrared Multi-Target Saliency Detection Algorithm under Sky Background Conditions

Aiming at solving the problem of incomplete saliency detection and unclear boundaries in infrared multi-target images with different target sizes and low signal-to-noise ratio under sky background conditions, this paper proposes a saliency detection method for multiple targets based on multi-saliency detection. The multiple target areas of the infrared image are mainly bright and the background areas are dark. Combining with the multi-scale top hat (Top-hat) transformation, the image is firstly corroded and expanded to extract the subtraction of light and shade parts and reconstruct the image to reduce the interference of sky blurred background noise. Then the image obtained by a multi-scale Top-hat transformation is transformed from the time domain to the frequency domain, and the spectral residuals and phase spectrum are extracted directly to obtain two kinds of image saliency maps by multi-scale Gauss filtering reconstruction, respectively. On the other hand, the quaternion features are extracted directly to transform the phase spectrum, and then the phase spectrum is reconstructed to obtain one kind of image saliency map by the Gauss filtering. Finally, the above three saliency maps are fused to complete the saliency detection of infrared images. The test results show that after the experimental analysis of infrared video photographs and the comparative analysis of Receiver Operating Characteristic (ROC) curve and Area Under the Curve (AUC) index, the infrared image saliency map generated by this method has clear target details and good background suppression effect, and the AUC index performance is good, reaching over 99%. It effectively improves the multi-target saliency detection effect of the infrared image under the sky background and is beneficial to subsequent detection and tracking of image targets.

Blood vessel detection from Retinal fundas images using GIFKCN classifier

In this paper, an automated method for blood vessel detection from retinal fundus images is proposed. Initially, the method extracts the green layer of the retinal image as it contains the requisite information. The noise is removed using a noise removal filter this step will lead to the reduction of artifacts in the final results. The image features are highlighted through morphological operations i.e., top hat and bottom hat transformation on the preprocessed image. The resultant image is then clustered into two clusters representing vessel and non-vessel using generalized improved Fuzzy Kohonen Clustering Network (GIFKCN). The experiments are performed on the Digital Retinal Images for Vessel Extraction (DRIVE) database. The effectiveness of the proposed technique is analyzed using performance metrics like accuracy and sensitivity. A comparative study of the proposed technique with some of the well-known existing methods is done. The comparison shows that the proposed method has improved significantly.

A New Method for Detecting Architectural Distortion in Mammograms by NonSubsampled Contourlet Transform and Improved PCNN

Breast cancer is the leading cause of cancer death in women, and early detection can reduce mortality. Architectural distortion (AD) is a feature of clinical manifestations for breast cancer, however, due to its complex structure and low detection accuracy, which cause a high mortality of breast cancer. In order to improve the accuracy of AD detection and reduce the mortality of breast cancer, this paper proposes a new method by combining the non-subsampled contourlet transform (NSCT) with the improved pulse coupled neural network (PCNN). Firstly, the top–bottom hat transformation and the exponential transformation are employed to enhance the image. Secondly, the NSCT is employed to expand the overall contrast of the mammograms and filter out the noise. Finally, the improved PCNN by the maximum inter-class variance threshold selection method is employed to complete the AD detection. This proposed approach is tested on the public and authoritative database—Digital Database for Screening Mammography (DDSM). The specificity of the method is 98.73%, the accuracy is 93.16%, and the F1-score is 79.80%, and the area under curve (AUC) of the receiver operating characteristic (ROC) curve is 0.93, these results clearly demonstrate that the proposed method is comparable with those methods in recent literatures. This proposed method is simple, furthermore it can achieve high accuracy and help doctors to perform computer-aided detection of AD effectively.

Histopathological Image Classification Scheme for Breast Tissues to Detect Mitosis

Histopathological images are widely utilized by the pathologists to determine the severity of the cancer. Usually, the pathologists analyse histopathological images manually, which demands more time and effort. The quality of image interpretation can be improvised with the help of a computerized assisting system for image analysis. This idea enhances the accuracy of the system as the computerized system helps in making decisions, which the pathologist may recheck. Understanding the benefits, this article presents a histopathological image classification scheme meant for breast tissues in detecting the mitotic cells. The goal is achieved by preprocessing the image by performing top and bottom hat transformation followed by stain normalization. The nuclei are then located with the help of Localized Active Contour Model (LACM) combined with Lion Optimization (LO) algorithm. The shape, statistical and texture features are extorted from the areas of interest and the differentiation of mitotic cells from the nonmitotic ones is performed by Support Vector Machine (SVM) classifier. The work efficiency is tested with respect to the standard performance measures such as accuracy, sensitivity, specificity and time consumption.

Detection of Hard Exudates in Retinopathy Images

The tissue layer located at the back of the eye is known as retina which converts the incoming light into nerve signals and those signals are sent to the brain for understanding. The damage onto the retina is termed as retinopathy and that may lead to vision weakening or vision loss. The hard exudates are small white or yellowish white deposits with their edges being clear and sharp. In the proposed methods we take color image of retina then extract the green channel of that image then apply top hat transformation and bottom hat transformation on that image. The DIARETDB1 and High-Resolution Fundus (HRF) databases are used for performance evaluation of the proposed method. The proposed technique achieves accuracy 97%, sensitivity 95%, and specificity 96% and it takes average 5.6135 second for detection of hard exudates in an image.

A Radiomic feature-based Nipple Detection Algorithm on Digital Mammography.

In the diagnosis and detection of breast lesions, the nipple is an important anatomical landmark which can be used for the registration on multiview mammograms. In this study, we propose a new detection algorithm for nipples on digital mammography (DM) by applying pixel classification based on geometric and radiomic features extracted from breast boundary regions. The imaging characteristics of nipples are closely related to the visibility on mammograms. To locate the nipple on mammogram, a searching area is first determined based on the breast boundary and chest wall orientation. Two different approaches are developed for obvious and subtle nipples, respectively. For obvious nipples, top hat transformation is employed to detect the nipple region, whose geometric center is regarded as the nipple position. For subtle nipples, the curved searching area near the breast boundary is mapped onto a Cartesian plane through a revised rubber band straightening transformation. On the straightened searching area, the geometric and radiomic features are calculated along the normal direction of the breast boundary, and a random forest classifier is trained for subtle nipple localization. Seven hundred and twenty-one DMs were collected for the evaluation of the proposed algorithm. The locations of nipples are manually identified by an experienced radiologist as the reference standard. The average Euclidean distance between the computed nipple position and the reference standard was 2.69mm (obvious) and 7.81mm (subtle), respectively. A total of 97.61% of the obvious nipples (613/628) and 88.17% of the subtle nipples (82/93) were detected within a 10-mm radius centered from the reference standard. The evaluation results show that the proposed method is effective for nipple detection on DM, especially for subtle nipple detection.