Grayscale Research Articles

Intracranial hematoma segmentation on head CT based on multiscale convolutional neural network and transformer

AbstractIntracranial hematoma, a severe brain injury caused by trauma or cerebrovascular disease, can result in blood accumulation and compression of brain tissue. Untreated cases can cause headaches, impaired consciousness, and even brain tissue damage or death. Therefore, early and accurate diagnosis is crucial. Traditional segmentation methods require physicians with extensive clinical experience and expertise to manually mark out the hematoma region, but for hematoma cases with irregular shapes and uneven grey levels, this process is cumbersome, and the segmentation results are not good. Existing deep learning‐based methods are more likely to perform binary segmentation, considering all hematomas as a class and segmenting them, but this segmentation cannot capture more detailed information and lacks the analysis of different types of hematomas. To address these problems, an ICH segmentation network combining CNN and Transformer Encoder is proposed for accurate segmentation of different types of hematomas. The network incorporated edge information and long‐range context into the segmentation process. Experimental results using the CQ500 dataset demonstrate comparable performance to existing methods, with mIoU (0.8705), TPR (0.9273), mAP (0.9300), and DSC (0.9286) as the best metrics achieved by this paper's method.

Image Quality Restoration on Historical Artifacts Using Histogram Equalization and Contrast Stretching Methods

This research was carried out to improve the image quality of historical artifacts that experience quality degradation such as noise, unclear or dark image contrast, and poor sharpness. Contrast Stretching and Histogram Equalization are image quality improvement methods to expand and even out image grayscale. This research aims to build a system for improving image quality on historical artifacts using contrast stretching and histogram equalization methods. The sample used in this research is RGB image data with 10 test images in .jpg format, while the process uses the Matlab programming language. The results of improving image quality using the contrast stretching and histogram equalization methods from sample 1 to image sample 10 showed the best results in test image_citra_1 with contrast stretching results based on PSNR = 32.61 dB and MSE = 35.86 Db and PSNR = 19.13 Db and MSE = 794.76 in histogram equalization calculations.

Carotid Artery Wall Echogenicity Measured by Gray Scale Median of the Intima-Media Complex From Childhood to Young Adulthood.

Echogenicity of the carotid arterial wall, measured by gray scale median of the intima-media complex (IM-GSM), is a novel subclinical atherosclerosis marker with lower values indicating greater lipid deposition. Our longitudinal study investigated IM-GSM from childhood to adulthood and its associated risk factors. A total of 240 participants from the Southern California CHS (Children's Health Study) underwent carotid artery ultrasounds in 2008 (mean age±SD): (11.2±0.6 years), and again around 2022 (24.2±1.6 years) to assess IM-GSM, carotid artery intima-media thickness, and carotid artery distensibility. Questionnaires and anthropometric and blood pressure measurements were completed by participants at both times. Mean and SD of IM-GSM were 108.2±24.6 in childhood and 75.6±15.8 in adulthood. Each 1-year increase in age was associated with -2.52 change in IM-GSM (95% CI, -2.76 to -2.27). Childhood and adulthood IM-GSMs were highly correlated (β=0.13 [95% CI, 0.05-0.22]). In childhood, Hispanic ethnicity, lower parental education levels and prenatal father smoking were significantly associated with lower IM-GSM. In adulthood, higher systolic blood pressure, carotid artery intima-media thickness, hypertension, and lower distensibility were significantly associated with lower IM-GSM. Weight status exhibited a consistent association with both childhood and adulthood IM-GSM. During the transition from childhood to adulthood, individuals who shifted from normal weight to overweight/obese or normal blood pressure to hypertension or experienced an increase in carotid artery intima-media thickness displayed lower levels of IM-GSM in adulthood. IM-GSM decreases with age. Maintaining healthy weight and blood pressure levels in children could potentially aid in preventing subclinical atherosclerosis.

Surface roughness analysis of side-polished fiber based on the importance of texture features

In order to explore the use of side-polished fibre (SPF) for microprobe-type "lab-on-fibre", this study presents an analysis of the surface roughness in side-polished fiber (SPF) using the gray level co-occurrence matrix (GLCM) texture feature analysis method. Experimental results show that the flat areas of the SPF polished surface exhibit texture characteristics with high mean values in contrast and entropy, and low mean values in the angular second moment (ASM), homogeneity, and correlation. Employing the random forest (RF) feature importance ranking method based on the Gini coefficient and out-of-bag (OOB) error estimation, this study assesses the sensitivity of various GLCM texture parameters in classifying different roughness levels of the SPF polished surfaces. A feature subset comprising variance, ASM, entropy, and contrast is identified as optimal. Utilizing this subset, the paper conducts an RF classification validation experiment on the roughness of the SPF polished surfaces, with results showing an RF classification accuracy of 95.65%.This research provides evidence for exploring the impact of rough polished surfaces in SPF optic sensors on the light coupling mechanism with environmental materials and its influence on sensor sensitivity. It lays the foundation for exploring the precise identification of high-sensitivity areas on SPF polished surfaces.

Correlation between GLCM-based texture features of the lateral pterygoid muscle and cognitive function in patients with idiopathic normal pressure hydrocephalus: a preliminary report.

The potential relationship between mastication ability and cognitive function in idiopathic normal pressure hydrocephalus (iNPH) patients is unclear. This report investigated the association between mastication and cognitive function in iNPH patients using the gray level of the co-occurrence matrix on the lateral pterygoid muscle. We analyzed data from 96 unoperated iNPH patients who underwent magnetic resonance imaging (MRI) between December 2016 and February 2023. Radiomic features were extracted from T2 MRI scans of the lateral pterygoid muscle, and muscle texture parameters were correlated with the iNPH grading scale. Subgroup analysis compared the texture parameters of patients with normal cognitive function with those of patients with cognitive impairment. The mini-mental state examination score correlated positively with the angular second moment (P < 0.05) and negatively with entropy (P < 0.05). The dementia scale (Eide's classification) correlated negatively with gray values (P < 0.05). Gray values were higher in the cognitive impairment group (64.7 ± 16.6) when compared with the non-cognitive impairment group (57.4 ± 13.3) (P = 0.005). Entropy was higher in the cognitive impairment group (8.2 ± 0.3) than in the non-cognitive impairment group (8.0 ± 0.3) (P < 0.001). The area under the receiver operating characteristic curve was 0.681 (P = 0.003) and 0.701 (P < 0.001) for gray value and entropy, respectively. Our findings suggest an association between heterogeneity of mastication and impaired cognitive function in iNPH patients and highlight muscle texture analysis as a potential tool for predicting cognitive impairment in these patients.

Hybrid Feature Mammogram Analysis: Detecting and Localizing Microcalcifications Combining Gabor, Prewitt, GLCM Features, and Top Hat Filtering Enhanced with CNN Architecture.

Breast cancer is a prevalent malignancy characterized by the uncontrolled growth of glandular epithelial cells, which can metastasize through the blood and lymphatic systems. Microcalcifications, small calcium deposits within breast tissue, are critical markers for early detection of breast cancer, especially in non-palpable carcinomas. These microcalcifications, appearing as small white spots on mammograms, are challenging to identify due to potential confusion with other tissues. This study hypothesizes that a hybrid feature extraction approach combined with Convolutional Neural Networks (CNNs) can significantly enhance the detection and localization of microcalcifications in mammograms. The proposed algorithm employs Gabor, Prewitt, and Gray Level Co-occurrence Matrix (GLCM) kernels for feature extraction. These features are input to a CNN architecture designed with maxpooling layers, Rectified Linear Unit (ReLU) activation functions, and a sigmoid response for binary classification. Additionally, the Top Hat filter is used for precise localization of microcalcifications. The preprocessing stage includes enhancing contrast using the Volume of Interest Look-Up Table (VOI LUT) technique and segmenting regions of interest. The CNN architecture comprises three convolutional layers, three ReLU layers, and three maxpooling layers. The training was conducted using a balanced dataset of digital mammograms, with the Adam optimizer and binary cross-entropy loss function. Our method achieved an accuracy of 89.56%, a sensitivity of 82.14%, and a specificity of 91.47%, outperforming related works, which typically report accuracies around 85-87% and sensitivities between 76 and 81%. These results underscore the potential of combining traditional feature extraction techniques with deep learning models to improve the detection and localization of microcalcifications. This system may serve as an auxiliary tool for radiologists, enhancing early detection capabilities and potentially reducing diagnostic errors in mass screening programs.

Studi kualitas pewarnaan alami kulit bawang merah pada kain batik

Shallots are a plant that is often used in human life. The waste product from shallots that can be used is shallot skins, where the shallot skins are usually just thrown away. This research uses a study of the quality of natural coloring of shallot skin on batik cloth. This research aims to determine the quality of the natural color of shallot skin on batik cloth. The process of researching the quality of shallot skin dye is that a cloth sample is attached to wax and then colored using shallot skin dye which has been extracted and fixed using an alum fixator. After that, the fabric sample is subjected to a night release process. Test samples in the laboratory through color fastness tests to wax release, color fastness tests to washing, color fastness tests to rubbing and color fastness tests to sunlight. Evaluate the resulting values ​​using gray scale and staining scale tools. The results of color fastness to waxing, washing, rubbing and sunlight are very low. This is because the anthocyanin and flaponoid compounds carried by shallot skin tend to be few or the coloring substances produced are very light.

New method for rapid detection and simultaneous determination of prohibited adrenergic drugs in sports using thin layer chromatography and image processing

This study presents a new method for simultaneous determination of fenoterol, procaterol, clenbuterol, terbutaline and isoprenaline drugs using thin-layer chromatography assisted by image analysis (HPTLC-IA) combined with sensitive detection modes. Separation of the selected drugs was obtained using HPTLC Silica gel 60 F254 plates and mixture of isopropyl alcohol, ethyl acetate and ammonia (20:25:1.5 v/v/v) as mobile phase. UV detection mode combined with the use of 2,2-diphenyl-1-picrylhydrazyl (DPPH•) and 2,2'-azino-bis (3-ethylbenzothiazoline)-6-sulfonic acid (ABTS+•) radicals were applied for the first time for sensible detection of the separated drugs. Grey, green, red and blue scales respectively were selected for image analysis and accurate quantification of spot area. According to the obtained result, the use of DPPH• and ABTS+• reagents revealed a higher sensitivity in detection and accurate quantification of the analyzed drugs. Lower limit of detection (LOD = 0.07 ± 0.03 µg/spot; 0.09 ± 0.03 µg/spot and 0.10 ± 0.04 µg/spot) was obtained for fenoterol, procaterol and terbutaline using the ABTS+• radical and red scale selection for image processing. With the exception of isoprenaline, the proposed HPTLC-IA method showed a good separation of fenoterol, procaterol, clenbuterol and terbutaline drugs from other compounds in urine samples. Good recovery was obtained for these drugs in spiked urine samples.

Advanced skin disease detection: Image processing and modified genetic optimization with supervised k-nearest neighbors

The use of modern technology to improve skin disease diagnosis includes things like artificial intelligence (AL), deep learning (DL) and computer vision. Medical images of skin lesions, dermatoscopic images, or thermal imaging can be analyzed by automated algorithms and image processing techniques to diagnose skin diseases. Diseases can cause physical and emotional suffering, making them silent killers. Extreme instances might cause skin cancer. skin disease diagnosis from clinical photographs is a key medical image processing challenge. Manual skin diagnosis takes time and it is subjective for doctors. Computerized skin disease prediction can simplify patient and dermatologist treatment planning. To tackle these issues, we proposed a genetic optimization and supervised K-Nearest Neighbor (MGO-SKNN) technique for detecting advanced skin diseases. The research gathers clinical data for detecting advanced skin diseases. Three methods were used to preprocess our data: noise reduction, hair removal and image resizing. The Grey Level Co-Occurrence Matrix (GLCM) approach was used in this work to extract features. The integration of image processing into MGO-SKNN represents a significant step forward in the quest for accurate, efficient and cutting-edge skin disease diagnosis. The metrics like accuracy (97.53%), precision (95.20%), recall (96.74%) and f1-score (98.06%) of our suggested technique, MGO-SKNN, exceed the traditional approaches for skin detection.

Primary tumor heterogeneity on pre-treatment [68Ga]Ga-PSMA PET/CT for the prediction of biochemical recurrence in prostate cancer

PurposeThe aim of this study is to research the value of the texture analysis of primary tumors in pre-treatment [68Ga]Ga-PSMA PET in the prediction of the development of biochemical recurrence (BCR) in prostate cancer patients who underwent definitive therapies. Methods51 patients with prostate adenocarcinoma who had a pre-treatment [68Ga]Ga-PSMA-11 PET/CT and underwent definitive radiotherapy (RT) or radical prostatectomy (RP) were included in the study. Demographics, clinicopathologic features, the presence of BCR, and the last follow-up date of patients were recorded. Textural and conventional PET parameters (maximum standardized uptake value (SUVmax), total lesion-PSMA (TL-PSMA), and PSMA-tumor volume (PSMA-TV)) were obtained from PET/CT images using LifeX program. Parameters were grouped using the Youden index in ROC analysis. Factors predicting the BCR were determined using Cox regression analyses. Results29 (56.9%) patients have received primary curative RT, while the remaining 22 (43.1%) patients have undergone RP. 5 (22.7%) patients with RP and 3 (10.3%) patients with curative RT have developed BCR during the follow-up. INTENSITY-BASED-minimum grey level (P=.050), GLCM-sum variance (P=.019), and GLCM-cluster prominence (P=.050) were associated with BCR in univariate analysis. INTENSITY-BASED-minimum grey level (P=.009) and GLCM-sum variance (P=.004) were found as independent predictors of BCR in the multivariate analysis. ConclusionTumor heterogeneity on pre-treatment [68Ga]Ga-PSMA PET is associated with a high risk of BCR in PCa patients who underwent definitive therapies.

Case Series of Changes in Greyscale and Power Doppler Signals on Salivary Gland Ultrasonography after Treatment in Patients with Sjögren's Syndrome.

The usefulness of greyscale (GS) in salivary gland ultrasonography for Sjögren's syndrome (SS) has been established; however, limited information is currently available on power Doppler signals (PDs), and changes after treatment remain unknown. PDs are considered to represent glandular inflammation, which indicates the worsening of GS in later years. We examined the changes in PDs in three immunosuppressant-treated SS patients. PDs decreased, along with GS and markers of disease activity, after treatment. PDs have the potential to provide insights into glandular inflammation in real time; however, large-scale studies on their clinical usefulness are needed.

Android malware detection using GIST based machine learning and deep learning techniques

In today’s digital world, Android phones play a vital part in a variety of facets of both professionals and individuals’ personal and professional lives. Android phones are great for getting things done faster and more organized. The proportionate increase in the number of malicious applications has also been seen to be expanding. Since the play store offers millions of apps, detection of malware apps is challenging task. In this paper, a methodology is introduced for detecting malware in Android applications through the utilization of global image shape transform (GIST) features extracted from grayscale images of the applications. The dataset comprises samples of both malware and benign apps collected from the virus share website. After converting the apps into grayscale images, GIST features are extracted to capture their global spatial layout. Various machine learning (ML) algorithms, such as logistic regression (LR), k-nearest neighbour (KNN), AdaBoost, decision tree (DT), Naïve Bayes (NB), random forest (RF), support vector machine (SVM), extra tree classifier (ETC), and gradient boosting (GB), are employed to classify the applications according to their GIST features. Furthermore, a feed forward neural network (FFNN) is utilized as a deep learning (DL) technique to further improve the accuracy of classification. The performance of each algorithm is evaluated using metrics such as accuracy, precision and recall. The results demonstrated that the FFNN achieves superior accuracy compared to traditional ML classifiers, indicating its effectiveness in detecting malware in Android apps.

A fuzzy control algorithm based on artificial intelligence for the fusion of traditional Chinese painting and AI painting

Recently, artificial intelligence (AI)-generated resources have gained popularity because of their high effectiveness and reliability in terms of output and capacity to be customized and broadened, especially in image generation. Traditional Chinese paintings (TCPs) are incomplete because their color contrast is insufficient, and object reality is minimal. However, combining AI painting (AIP) with TCP remains inadequate and uncertain because of image features such as patterns, styles, and color. Hence, an algorithm named variational fusion-based fuzzy accelerated painting (VF2AP) has been proposed to resolve this challenge. Initially, the collected TCP data source is applied for preprocessing to convert it into a grayscale image. Then, the feature extraction process is performed via fuzzy-based local binary pattern (FLBP) and brushstroke patterns to enhance the fusion of intelligent fuzzy logic to optimize the local patterns of textures in a noisy image. Second, the extracted features are used as inputs to the variational autoencoder (VAE), which is used to avoid latent space irregularities in the image and the reconstructed image by maintaining minimum reconstruction loss. Third, fuzzy inference rules are applied to avoid variation in the fusion process of the reconstructed and original images. Fourth, the feedback mechanism is designed with evaluation metrics such as area under the curve-receiver operating characteristic (AUC-ROC) analysis, mean square error (MSE), structural similarity index (SSIM), and Kullback‒Leibler (KL) divergence to enhance the viewer's understanding of fused painting images.

Timbral brightness perception investigated through multimodal interference

Brightness is among the most studied aspects of timbre perception. Psychoacoustically, sounds described as “bright” versus “dark” typically exhibit a high versus low frequency emphasis in the spectrum. However, relatively little is known about the neurocognitive mechanisms that facilitate these metaphors we listen with. Do they originate in universal magnitude representations common to more than one sensory modality? Triangulating three different interaction paradigms, we investigated using speeded classification whether intramodal, crossmodal, and amodal interference occurs when timbral brightness, as modeled by the centroid of the spectral envelope, and pitch height/visual brightness/numerical value processing are semantically congruent and incongruent. In four online experiments varying in priming strategy, onset timing, and response deadline, 189 total participants were presented with a baseline stimulus (a pitch, gray square, or numeral) then asked to quickly identify a target stimulus that is higher/lower, brighter/darker, or greater/less than the baseline after being primed with a bright or dark synthetic harmonic tone. Results suggest that timbral brightness modulates the perception of pitch and possibly visual brightness, but not numerical value. Semantically incongruent pitch height-timbral brightness shifts produced significantly slower reaction time (RT) and higher error compared to congruent pairs. In the visual task, incongruent pairings of gray squares and tones elicited slower RTs than congruent pairings (in two experiments). No interference was observed in the number comparison task. These findings shed light on the embodied and multimodal nature of experiencing timbre.

Research on the Application of Deep Learning in Human Spinal Image Segmentation

Abstract Traditional segmentation methods can only segment grayscale images, which limits their application; The segmentation process often depends on the doctor’s experience, which can lead to subjective factors affecting the results; Therefore, the accuracy and efficiency of segmentation are difficult to achieve practical application results. The deep learning model is a structural model that mimics the neural connections within the human brain. The deep learning model can accurately extract multi-level features of key information in images from low-level to high-level, and provide feedback on data interpretation, thereby achieving accurate and efficient image segmentation results. Introducing deep learning algorithms into medical image segmentation can accurately express the key information at a deeper level in spinal images, achieving better image segmentation results.

A novel colorimetric sensor through peroxymonosulfate activated by CoFe@CN nanozymes for on-site resorcinol detection

The presence of resorcinol (RS) in water contaminated with pollutants may engender environmental and ecosystem risks. In this study, we developed rapid and efficient colorimetric methods for the on-site quantitative determination of RS. Specifically, a synthetic nanozyme composed of nitrogen-doped carbon-coated cobalt-iron (CoFe@CN) was utilized to initiate the oxidation reaction of 3,3′,5,5′-tetramethylbenzidine (TMB) with the activator of peroxymonosulfate (PMS). Compared to the activator of hydrogen peroxide (H2O2), PMS demonstrated a sixfold enhancement in catalytic efficacy, markedly elevating the detection performance. Density Functional Theory calculations indicate that RS can serve as a reducing agent for oxTMB, confirming the theoretical feasibility of this process. A linear relationship between inhibition rate (IR %) of RS concentration and absorbance was established and the colorimetric analysis revealed a linear relationship between RS concentration and gray scale values over the range of 0–45 μM, with a detection limit of 3.39 μM. Furthermore, smartphone-based analysis of color images allows for on-site quantitative analysis of RS concentration, leveraging portability and real-time detection capabilities. This study provides pioneering analytical support for on-site water quality monitoring within the ecological environment.

Classification of Lung Disease in X-Ray Images Using Gray Level Co-Occurrence Matrix Method and Convolutional Neural Network

The lungs are a very important part of the human body, as they serve as a place for oxygen exchange. They have a very complex task and are susceptible to damage from the polluted air we breathe every day, which can lead to various diseases. Lung disease is a very common health problem that can be found in everyone, but there are still many people who do not pay attention to their lung health, making them vulnerable to lung disease. One of the methods used to detect lung disorders is by examining images obtained from X-rays. Image processing is one of the techniques that can also be used for lung disease identification and is most commonly used in medical images. Therefore, the purpose of this research is to implement image processing to determine the accuracy of lung disease identification using deep learning algorithms and the application of feature extraction. In this research, there are two experiments conducted consisting of the application of the classification method, namely Convolutional Neural Network and Gray Level Co-Occurrence Matrix feature extraction with CNN. The results show that the CNN model gets a precision of 0.92, recall of 0.92, f1-score of 0.92, and average accuracy of 0.92. The combination of the GLCM method with CNN produces a precision of 0.87, recall of 0.87, f1-score of 0.87, and average accuracy of 0.87. The results of this study indicate that the use of CNN in the lung disease classification model based on X-ray images is superior to the GLCM-CNN method.

Study on lung CT image segmentation algorithm based on threshold-gradient combination and improved convex hull method

Lung images often have the characteristics of strong noise, uneven grayscale distribution, and complex pathological structures, which makes lung image segmentation a challenging task. To solve this problems, this paper proposes an initial lung mask extraction algorithm that combines threshold and gradient. The gradient used in the algorithm is obtained by the time series feature extraction method based on differential memory (TFDM), which is obtained by the grayscale threshold and image grayscale features. At the same time, we also proposed a lung contour repair algorithm based on the improved convex hull method to solve the contour loss caused by solid nodules and other lesions. Experimental results show that on the COVID-19 CT segmentation dataset, the advanced lung segmentation algorithm proposed in this article achieves better segmentation results and greatly improves the consistency and accuracy of lung segmentation. Our method can obtain more lung information, resulting in ideal segmentation effects with improved accuracy and robustness.

Image Enhancement Thanks to Negative Grey Levels in the Logarithmic Image Processing Framework.

The present study deals with image enhancement, which is a very common problem in image processing. This issue has been addressed in multiple works with different methods, most with the sole purpose of improving the perceived quality. Our goal is to propose an approach with a strong physical justification that can model the human visual system. This is why the Logarithmic Image Processing (LIP) framework was chosen. Within this model, initially dedicated to images acquired in transmission, it is possible to introduce the novel concept of negative grey levels, interpreted as light intensifiers. Such an approach permits the extension of the dynamic range of a low-light image to the full grey scale in "real-time", which means at camera speed. In addition, this method is easily generalizable to colour images and is reversible, i.e., bijective in the mathematical sense, and can be applied to images acquired in reflection thanks to the consistency of the LIP framework with human vision. Various application examples are presented, as well as prospects for extending this work.

A Novel Chaos-Based Encryption Technique with Parallel Processing Using CUDA for Mobile Powerful GPU Control Center

Chaotic systems possess unique properties that can be leveraged for cybersecurity due to their complex and unpredictable nature, making it challenging for systems to interpret them. When combined with CUDA, chaotic systems benefit from high-efficiency parallel processing capabilities, allowing for the rapid and secure handling of large data sets. In this study, a CUDA-supported chaos-based parallel processing encryption mechanism for mobile control centers is developed. The powerful GPU of the control center enables fast encryption and decryption of image data from multiple connected devices. The Logistic Map is used for random number generation, and XOR operations are applied to encrypt the R, G, B, and Gray Scale channels of images. Initially, an analysis of the numbers generated from the Logistic Map is conducted, followed by a detailed explanation of the encryption technique. The technique is then applied to image data, and image analyses are performed. Finally, the performance of the encryption technique is compared with other studies, and encryption speeds are examined. Results indicate that the new encryption technique provides significantly fast encryption and security levels comparable to other studies. The key discovery is that the mechanism is well-suited for parallel processing, allowing for rapid image encryption using the proposed method. For encrypting large IoT files, random number generation is performed first, followed by statistical tests, and then encryption using the developed algorithm. Security analyses are conducted, and the performance of the mechanism is compared with other studies. Results from image analysis and encryption performance demonstrate that the developed mechanism can be effectively used with high security for IoT applications.