Average Intensity Values Research Articles

Application of Image Encryption Algorithm for Wireless Sensor Network in the Security Analysis of Public Big Data

To improve the security of the Wireless Sensor Network (WSN), the image encryption algorithm is studied. Firstly, the shortcomings of current image encryption algorithms are compared. Secondly, based on the chaotic system, the color image encryption algorithm of double chaotic cross-diffusion is built, and the encryption platform is constructed in combination with the current development of intelligent equipment. Finally, the performance of the constructed algorithm and platform is evaluated, and the correlation of adjacent pixel pairs in different color channels and different directions of image encryption is verified. The results indicate that in the designed image encryption platform, the channels of different color are encrypted in different directions, and the encryption effect is good. Compared with the evaluation platform, the practicability of the encryption algorithm is good. After the image is encrypted, the correlation between adjacent pixels is significantly reduced. The image is highly correlated with adjacent pixels in three directions in the first three channels of encryption. After the studied algorithm is encrypted, the correlation coefficient value of adjacent pixel pairs is greatly reduced, and the maximum reduction is 0.97. The information entropy of the red, green, and blue channels before encryption is lower than the information entropy of the three-color channels after encryption. The information entropy of the algorithm is closer to the optimal value of the information entropy. For the index values of Unified Average Changing Intensity (UACI) and Number of Pixels Change Rate (NPCR), the average UACI of the algorithm is 99.62%, while the average of the UACI of the literature algorithm is 98.33%. The NPCR value of the algorithm is 33.52%, while the NPCR value of the literature algorithm is 31.23%. The encryption and decryption time of the algorithm is shorter than that of the literature algorithm. The constructed algorithm has high efficiency of encryption and decryption. It provides a reference for the enhancement of the security of image data in the WSN.

CarbArrayART: a new software tool for carbohydrate microarray data storage, processing, presentation, and reporting.

Glycan microarrays are essential tools in glycobiology and are being widely used for assignment of glycan ligands in diverse glycan recognition systems. We have developed a new software, called Carbohydrate microArray Analysis and Reporting Tool (CarbArrayART), to address the need for a distributable application for glycan microarray data management. The main features of CarbArrayART include: (i) Storage of quantified array data from different array layouts with scan data and array-specific metadata, such as lists of arrayed glycans, array geometry, information on glycan-binding samples, and experimental protocols. (ii) Presentation of microarray data as charts, tables, and heatmaps derived from the average fluorescence intensity values that are calculated based on the imaging scan data and array geometry, as well as filtering and sorting functions according to monosaccharide content and glycan sequences. (iii) Data export for reporting in Word, PDF, and Excel formats, together with metadata that are compliant with the guidelines of MIRAGE (Minimum Information Required for A Glycomics Experiment). CarbArrayART is designed for routine use in recording, storage, and management of any slide-based glycan microarray experiment. In conjunction with the MIRAGE guidelines, CarbArrayART addresses issues that are critical for glycobiology, namely, clarity of data for evaluation of reproducibility and validity.

Acoustic characteristics associated with liquid swallowing sounds of different bolus consistencies in young healthy adults.

Cervical auscultation has been used clinically as an augmentative procedure for swallow examination. Despite its frequent use for screening and preliminary assessment of swallowing, the usefulness of cervical auscultation is controversial due to a lack of sufficient research, particularly in quantifying swallowing sounds. To document the acoustic characteristics of normal swallowing sound associated with swallowing bolus of different consistencies among younger healthy adults. A total of 30 healthy young adult participants swallowed 5 ml thickened liquids of four different consistencies (slightly thick, mildly thick, moderately thick and extremely thick) prepared using a starch-based commercial thickener, and the corresponding swallowing sounds were recorded using a wireless accelerometric stethoscope. An array of acoustic measures including duration of swallowing sound (DSS), duration to peak intensity (DPI), frequency at peak intensity (FPI), peak intensity (PI), average intensity (AI), and difference between peak and average intensity (DPAI) values associated with the swallowing signals were obtained. In general, increased durational measures of the swallowing sounds were associated with an increase in bolus consistency. Intensity measures including PI, AI and DPAI were found to be stable across different consistencies. The change in FPI did not appear to be particularly meaningful due to its high variability. In addition, no significant differences were observed between men and women. Swallow sounds associated with different bolus consistencies could be quantified and used to differentiate consistencies. The present findings could serve as a reference for future swallowing research of normal and dysphagic population. What is already known on the subject Cervical auscultation using traditional stethoscope has been used as part of an informal clinical swallow examination by practitioners. Validity of cervical auscultation is controversial, possibly due to the lack of normative data on swallow sounds. What this paper adds to existing knowledge The present study explored the possibility of using wireless accelerometric stethoscopy for cervical auscultation for dysphagia screening. Acoustic profiles of swallow sounds associated with boluses of different consistencies in healthy individuals were examined. What are the potential or actual clinical implications of this work? Findings contribute to our knowledge about the acoustic characteristics of swallow sounds of boluses of different consistencies in healthy young individuals. The study provides normative clinical data on cervical auscultation using wireless accelerometric stethoscope for normal swallow.

An efficient image encryption scheme for healthcare applications.

In recent years, there has been an enormous demand for the security of image multimedia in healthcare organizations. Many schemes have been developed for the security preservation of data in e-health systems however the schemes are not adaptive and cannot resist chosen and known-plaintext attacks. In this contribution, we present an adaptive framework aimed at preserving the security and confidentiality of images transmitted through an e-healthcare system. Our scheme utilizes the 3D-chaotic system to generate a keystream which is used to perform 8-bit and 2-bit permutations of the image. We perform pixel diffusion by a key-image generated using the Piecewise Linear Chaotic Map (PWLCM). We calculate an image parameter using the pixels of the image and perform criss-cross diffusion to enhance security. We evaluate the scheme’s performance in terms of histogram analysis, information entropy analysis, statistical analysis, and differential analysis. Using the scheme, we obtain the average Number of Pixels Change Rate (NPCR) and Unified Average Changing Intensity (UACI) values for an image of size 256 × 256 equal to 99.5996 and 33.499 respectively. Furthermore, the average entropy is 7.9971 and the average Peak Signal to Noise Ratio (PSNR) is 7.4756. We further test the scheme on 50 chest X-Ray images of patients having COVID-19 and viral pneumonia and found the average values of variance, PSNR, entropy, and Structural Similarity Index (SSIM) to be 257.6268, 7.7389, 7.9971, and 0.0089 respectively. Furthermore, the scheme generates completely uniform histograms for medical images which reveals that the scheme can resist statistical attacks and can be applied as a security framework in AI-based healthcare.

Difference Long Irradiation on The Growth Rate of Kappaphycus Alvarezii

The study aimed to find out the long-standing difference in irradiation to the growth rate of Kappaphycus alvarezii in controlled containers. The method used is experimental. This study used a Complete Randomized Design (RAL) using four (4) treatments with 3 repeats, namely the treatment of P1 control (without irradiation), P2 (8 hours of irradiation), P3 (10-hour irradiation), P4 (12-hour irradiation). The data were analyzed using analysis of variance (ANOVA) at a rate of 5% with a confidence interval of 95% and continued with the Duncan test. In this study, the average results of absolute weight and the highest specific daily growth rate at P4 treatment of 25.67±1.53 gr, 3.30±0.06%, followed by P3 treatment 21±2 gr, 3.11±0.09%, P2 treatment 16±1 gr, 2.87±0.06%, and lowest P1 treatment 12.33±2.08 gr, 2.63±0.15%. The average value of light intensity in each treatment is P1 1053±41.00 lux, P2 1256±27.71 lux, P3 1479±0.58 lux, and P4 1668±34.64 lux. Water quality parameters during maintenance are in the normal limit range for seaweed maintenance. D treatment produces the highest growth value this is because the light is an energy source in the process of photosynthesis, in chlorophyll plants, photosynthesis is the main process determining the rate of growth. The quality and amount of light that enters affects the growth of seaweed. This research concluded that the maintenance of Kappaphycus alvarezii in controlled containers with different irradiation had a significant effect on growth. 12 hours irradiation (P4) gave the best growth with an absolute weight of 16.00 g, and a specific growth rate of 4.06% per day and gave the highest chlorophyll-a value of 2.72 mg/l. Meanwhile, the highest phycoerythrin value was found in (P1) without irradiation, which was 4.58 mg/l.

Gas-liquid vertical pipe flow patterns convolutional neural network classification using experimental advanced wire mesh sensor images

Flow pattern identification for simultaneous gas-liquid flow in pipes is a central problem in the petroleum industry. However, there are many conventional methods used for flow patterns identification that come with some form of restrictions limited to specific operating conditions. Although previous studies made efforts to make flow patterns identification free from biases (subjectivity), objective predictions are yet to be fully explored. For the first time, this study employs a 4-layer convolutional neural network (CNN) architecture and threshold segmentation algorithm to classify industrial working fluids of air/silicone oil flow patterns in a vertical pipe. Also, an advanced wire mesh sensor (WMS) instrumentation was used to obtain the cross-sectional frame images from a series of 52 experimental runs. This study made an attempt to use the sequential cross-sectional frames original data obtained from the experimental WMS because it is the crossing wires of the WMS that interact with the flow field and also capture the main flow pattern transitional zones. The obtained experimental testing results indicate that the supervised CNN model has an accuracy of 99.90% better than the seven-benchmarked supervised machine learning classification models used in recognizing bubbly, slug and churn flow patterns. The CNN model experimental test accuracy also outwits other related flow patterns identified in the literature when compared, thereby affirming the model's robustness. Since the WMS provide high spatial and temporal resolutions data, the average pixel intensity values from the segmented images were used as the flow indicator in identifying the transition zones within the main flow patterns. Furthermore, the Locally Interpretable Model-agnostic Explanation (LIME) algorithm for the first time was used to explain and interpret features in the WMS flow images that were contributing to the overall CNN classification scores. Finally, the CNN trained model was able to classify the main flow patterns and segmented transitional zones with certainty and confidence without subjectivity which is inherent in the direct conventional methods.

Новый подход к применению метода измерения напряженности электрического поля по среднему значению

The paper considers a new possibility of using the method of measuring the electric field strength by an average value in order to reduce the error from the inhomogeneity of the field and expand the spatial range of measurements. The studies carried out in the work allowed us to establish that when using the measurement method by the average value, there are always three values at each measurement step, two of which correspond to the two previous values of the EP intensity determined with errors opposite in sign, and the third corresponds to the average value of them. Therefore, in the work, it is proposed to measure the electric field strength at one point in space by two sensors with errors opposite in sign. At the same time, alternately determining at each step of averaging the average value of the EP intensity from the previously obtained and subsequent average values, bringing the measurement result closer to the true value. Based on the results of theoretical research, a new method is proposed — the "Measurement method with step-by-step averaging", which, without complicating the measurement process, makes it possible to reduce by two to three times the error in measuring the intensity of inhomogeneous electric fields in a wide spatial range compared with the measurement method by the average value.

A Novel Practical Decisive Row-Class Entropy-Based Technique for Multilevel Threshold Selection Using Opposition Flow Directional Algorithm

One of today’s inspiring issues is the 2D histogram-based multilevel threshold selection which is used for segmenting images into several regions. The image analysis warrants exploration of multiclass thresholding techniques using various entropy-based objective functions. In this context, the Shannon type of entropic function without inherent decision making capacity has been widely used for threshold selection in the last decade. Furthermore, a 2D histogram was constructed using local average intensity values resulting in loss of some edge information. To address these problems, this study proposes a new methodology using a novel practical decisive row-class entropy (PDRCE) based fitness function for multilevel thresholding. The PDRCE values are computed using the newly constructed 2D histogram-based on normal local variance. Further, an opposition flow directional algorithm (OFDA) is proposed to maximize the fitness function. The performance of the proposed technique is compared with five state-of-the-art 2D histogram-based entropic fitness functions. Moreover, the performance of OFDA is investigated through comparison with other global optimizers namely the genetic algorithm, particle swarm optimization and artificial bee colony. An image segmentation evaluation dataset (BSDS500) is used in this experiment. It is witnessed that the proposal is more efficient than state-of-the-art methods. Our fitness function would be useful for registration, segmentation, fusion, etc.

Evaluating the role of endometrial colour Doppler dynamic tissue perfusion measurements in in vitro fertilisation success

The role of uterine receptivity and endometrial perfusion in in vitro fertilisation (IVF) remains unclear. In our prospective pilot study, we used a new dynamic tissue perfusion measurement method to evaluate this relationship in humans. A standard ovulation induction and embryo transfer method were applied to all the volunteers. On the day of embryo transfer, dynamic images of the uterus were recorded using colour Doppler ultrasound, and these images were uploaded onto PixelFlux Chameleon Software (GmbH, Münster, Germany). After determining the region of interest (ROI), the average velocity, intensity, and area values for all coloured pixels in the ROI, the tissue resistance index (RI) and the tissue pulsatility index were calculated. Endometrial thickness, morphology and dynamic endometrial perfusion parameters were compared between the clinically pregnant and non-pregnant groups. Endometrial thickness, morphology values and endometrial dynamic tissue perfusion measurements were similar between the groups. This study compared perfusion parameters between clinically pregnant and non-pregnant patients by accurately calculating endometrial tissue perfusion using standard software to establish its relationship with implantation success in IVF treatment. IMPACT STATEMENT What is already known about this subject? The relationship between IVF success, endometrial receptivity and perfusion is known. Clear valuations of endometrial receptivity require an endometrial biopsy which may cause endometrial damage to the actual IVF cycle. This problem has led researchers to conduct non-interventional studies. Studies have revealed the value of endometrial thickness, pattern and Doppler examination of endometrial uterine arteries in predicting the success of IVF treatment. What do the results of this study add? This prospective pilot study is the first one to use this programme in humans to evaluate uterine receptivity in IVF. Successful results can be obtained by using computer programmes in tissues where perfusion parameters cannot be measured using traditional colour Doppler ultrasonography. Revealing the relationship between tissue perfusion and IVF success will be more effective and accurate with the development of software technologies. What are the implications of these findings for clinical practice and/or further research? To increase the success of IVF treatment, current and new technological developments, as well as imaging methods should continue to be tested.

Laser induced breakdown spectroscopy online monitoring of laser cleaning quality on carbon fiber reinforced plastic

Carbon fiber reinforced polymer (CFRP) has attracted extensive attention in the industrial field due to its advantages of light weight and high hardness. The use of laser to clean the epoxy resin film on the surface of the carbon fiber is beneficial to improve the bonding performance. Combined with laser-induced breakdown spectroscopy (LIBS) technology, an on-line monitoring system for laser cleaning is designed to monitor the quality of laser cleaning in real time. The laser used in the experiment is a fiber laser widely used in laser cleaning, which has high cleaning efficiency, flexible use, and can be processed in a multi-dimensional space. The system is coupled with the scanning galvanometer which can be cleaned dynamically in all directions. The function of fast, real-time and high-resolution on-line monitoring of cleaning quality is realized. In this paper, prior information such as element composition and LIBS spectrum of epoxy resin and substrate carbon fiber in CFRP materials were obtained, and the differences were compared and analyzed. The results shown that the LIBS technology was feasible to monitor the cleaning quality. Subsequently, the experiment tested the influence of the laser process parameters on the LIBS characteristic spectrum, and it was confirmed that the average spectral intensity value at the wavelength (588.819 nm) was positively correlated with the average laser power. After that, according to the measured LIBS characteristic strength value, three evaluation grades of cleaning quality were effectively delineated. The piecewise linear fitting method was used to solve the value range of LIBS mapped by different grades, and the key data such as the optimal cleaning parameter threshold were calculated. Finally, scanning electron microscope (SEM) was used to observe the morphological characteristics of samples after laser cleaning under different grades, confirming that the LIBS characteristic line could effectively characterize the quality of laser cleaning.

PENGARUH ENDORPHINE MASSAGE TERHADAP SKALA INTENSITAS NYERI PADA IBU BERSALIN NORMAL PRIMIPARA INPARTU KALA I DI PMB KOTA BENGKULU TAHUN 2020

Pain in labor is a manifestation of uterine contractions in the first stage of labor that can cause fatigue so that it causes a decrease in uterine contractions. Many methods are offered to reduce pain during labor, non pharmacologically including endorphine massage has been proven to increase the ability of mothers to tolerate pain during childbirth. The purpose of this study was to determine the effect of endorphine massage on pain intensity scale in first-time normal mothers in PMB Bengkulu City in 2020. This study used quasi-experimental designs with the pretest-posttest Non Equivalent Control Group. The intervention group the endorphine massage was given to first stage mothers, while the control group was given deep breath relaxation techniques. Total sample are 30 people, divided into 2 groups, each each of which amounted to 15 people. The statistical analysis used was the Wilcoxon test and the Mann-Whitney Test. The results of thetest Wilcoxon showed that the average value of pain intensity in the first stage before and after the intervention was 7.53 and it became 6.00 with a p-value = 0.000 (<0.05). whereas in the control group the average value was 7.20 and after being treated it became 6.00 with a p-value = 0.001 (<0.05) which means that there was an influence of endorphine massage on the intensity scale of pain in first-time normal primipara delivery mothers in PMB Bengkulu City. It is expected that midwives can pay more attention to maternal needs for comfort by applying endorphine massage in the control of labor pain.
 Keywords:Labor, Labor Pain, Endorphine Massage

Analysis of Speech Sounds In the Praat Program

The article identifies the possibility of using the Praat program for comprehensive and complete analysis of speech sounds. It also indicates the range of functions of the software package and the perspective of using the program for comparing sounds. Praat is a program for speech analysis and synthesis written by Paul Boersma and David Veninko at the Department of Phonetics, University of Amsterdam. Praat is a very flexible tool for speech analysis. It offers a wide range of standard and non-standard procedures. The functions of the program that were used in the work: spectrograms, pitch analysis, formant analysis, intensity analysis, jitter, shimmer, voice breaks. There are at least three ways to get sound in the program: sound recording, read sound from the disc, create sound from formulas. In this article, hhe first method was used and the vowel sounds "a" and "e", as well as consonants "n" and "p" were considered. With the help of automated algorithms, a number of important data were quickly obtained, such as the average value of the formant on the selected interval, the maximum, minimum and average value of pitch, maximum, minimum and average intensity, the overall signal report(jitter, shimmer, voice breaks). Also, this programm can create high quality images that can be exported and inserted into scientific articles or personal research. Due to this fact, the images of not only spectrograms and waveforms, but also the images of formants and intensity was presented in the work. Based on the obtained results, the selected sounds were compared. As a result, a conclusion was made about the feasibility and effectiveness of using the software package Praat in the analysis of speech sounds. It was also noted the ease of use of this software package, as well as the importance of using the program in research in various fields of human activity.

Double color image encryption based on fractional order discrete improved Henon map and Rubik’s cube transform

A double color image encryption method based on DNA (deoxyribonucleic acid) computation and chaos is proposed. Differently from the conventional algorithms, double color images are encrypted at the same time so that we can save information of each other, which makes the encryption more safe and reliable. In addition, a new chaotic fractional order (FO) discrete improved Henon map (FODIHM) is proposed as a pseudo-random number generator. To ensure the plain-image sensitivity of the encryption algorithm, the initial value of FODIHM is calculated from the hash value of the color image (SHA-256) and from the three additional keys entered by the user. Furthermore, a Rubik’s cube transform scrambles the pixels in each color component of the two images. Then, each pixel in each color component of the two images is diffused by means of different DNA coding rules. Finally, the CAT transform, based on FO discrete Logistic map and the classic XOR, is used to further improve the security performance. The key space size of the proposed algorithm is of order 10135, which is about 30 orders of magnitude higher than those available in the literature. The information entropies are 7.9974 and 7.9973, which are very close to the ideal entropy value of 8. The values of the unified average changing intensity (NPCI) are 99.630 and 99.623, while the number of pixels change rate (UACR) are 33.473 and 33.553, which are also close to the ideal NPCR and UACR value of 99.6094 and 33.4635, respectively. The numerical results and security analysis prove that the algorithm has good resistance to several classic attacks.

Key Factors for Tuning Au Self-Assembling SERS Films: from Properties to Structure-=SUP=-*-=/SUP=-

We studied self-assembled plasmonic films (SPF) based on gold nanoparticles (Au-NPs). We recorded the intensity distribution maps (IDM) with an area of 2025 μm2 (9x 9 points). We have observed uniform enhancement across the entire surface of SPF. Therefore, we used the parameters average intensity of IDM and IDM gate when we optimized the synthesis of SPF. Average intensity is the average intensity value over 81 spectra of the IDM. The gate is the span excluding 10% of the outliers. We have discovered that the maximum average intensity was observed at a concentration of 1.00· 10-5 M. A decrease in the concentration of Au-NPs leads to a decrease in the average intensity. A simultaneous decrease in TOABr and Au-NPs concentration also leads to a decrease in the average intensity. The film obtained by the optimal synthesis method was characterized using SEM and AFM. Keywords: SERS, Au-NPs, self-assembling.

Auriculotherapy used to manage orthodontic pain: a randomized controlled pilot study.

ABSTRACTIntroduction: Several methods are commonly used to decrease orthodontic pain, but versatile tools and standardized protocols are still lacking. Objective: In response to the need for alternatives to conventional analgesic methods, this study evaluates the analgesic effects of auriculotherapy (AT) during the first three months of fixed orthodontic treatment. Methods: A sample of 36 subjects was selected, with patients randomly allocated into two homogeneous groups, Study Group (SG) and Control Group (CG), depending on the application/non-application of AT. Patients rated their pain scores monthly from 0 to 10, on visual analogue scales (VAS) at the time of bonding (T0) and again at two appliance adjustments (T1 and T2). At each of these treatment phases, VAS was applied in six different time moments (TM): immediately before, immediately after, after 4 hours, after 8 hours, after 24 hours, and after 72h hours. Descriptive statistical analysis, a Student’s t-test, and a Chi-square test were applied to the collected data (statistical significance for p< 0.05).Results: SG patients reported lower pain levels than CG patients, both at T0, T1 and T2. Moreover, average pain intensity values were lower in the SG for all TM analyzed, with the t-test significant (p< 0.05) for most TMs.Conclusion: AT was effective in the pain treatment of patients with fixed orthodontic appliances. Further studies are needed with a sham control group to confirm the validity of these results.

Robust processing of airborne laser scans to plant area density profiles

Abstract. We present a new algorithm for the estimation of the plant area density (PAD) profiles and plant area index (PAI) for forested areas based on data from airborne lidar. The new element in the algorithm is to scale and average returned lidar intensities for each lidar pulse, whereas other methods do not use the intensity information at all, use only average intensity values, or do not scale the intensity information, which can cause problems for heterogeneous vegetation. We compare the performance of the new algorithm to three previously published algorithms over two contrasting types of forest: a boreal coniferous forest with a relatively open structure and a dense beech forest. For the beech forest site, both summer (full-leaf) and winter (bare-tree) scans are analyzed, thereby testing the algorithm over a wide spectrum of PAIs. Whereas all tested algorithms give qualitatively similar results, absolute differences are large (up to 400 % for the average PAI at one site). A comparison with ground-based estimates shows that the new algorithm performs well for the tested sites. Specific weak points regarding the estimation of the PAD from airborne lidar data are addressed including the influence of ground reflections and the effect of small-scale heterogeneity, and we show how the effect of these points is reduced in the new algorithm, by combining benefits of earlier algorithms. We further show that low-resolution gridding of the PAD will lead to a negative bias in the resulting estimate according to Jensen's inequality for convex functions and that the severity of this bias is method dependent. As a result, the PAI magnitude as well as heterogeneity scales should be carefully considered when setting the resolution for the PAD gridding of airborne lidar scans.

Content-adaptive image encryption with partial unwinding decomposition

This study designs a novel image encryption cryptosystem through the two-dimensional partial unwinding decomposition (2D-PUD). It consists of three stages. Firstly, a stream sequence (first part of the security key) is generated by pseudo-random number. Secondly, the plain image is decomposed into three parts by 2D-PUD: one 2D decomposition component, two 1D decomposition components, and the average intensity value of the image. Finally, the 2D decomposition component is shuffled by a generalized Arnold transform where the average intensity value is selected as second part of the security key. The diffusion scheme is subsequently applied to the scrambled image via exclusive OR operations with the randomized 1D decomposition components (third part of the security key) along rows and columns to obtain the cipher image. Due to the adaptive attribute of 2D-PUD, the generated 1D decomposition components are completely distinct for different images. In addition, we can also make them significantly different by tuning the decomposition times for the same image. Thus, the proposed algorithm is an image-content-adaptive encryption scenario that can effectively resist cryptographic attacks. Simulation results demonstrate that our proposed method has excellent encryption performance and can resist against various typical attacks, including brute force, statistical, entropy, and differential attacks.

Shadow detection of the satellite images of earth using ratio image pixels

Shadows, in aerial and satellite high-resolution images of earth, are a common phenomenon. Shadow causes false-color image, loss of information in the image, and false image segmentation. This leads to incorrect outputs of many image processing applications. In this paper, we address the problem of shadow detection in aerial high-resolution images of earth. The paper presents a proposed method that can be valuable in comparing it with other existing methods. The proposed work exploits the impact of ratio image pixel values on the process of shadow detection. The ratio image is the mathematical division of hue over the intensity component in the invariant color model. We propose a method, design, and develop an algorithm. In the designed algorithm, the input RGB aerial image of the earth is transformed into the invariant color model hue, saturation, and value (HSV). It acquires the average intensity value of pixels of the input RGB image components. Then, the ratio image of Hue (H) over Value (V) is calculated. Afterward, a power function is applied to this ratio to modify it by increasing the difference between pixel values very effectively. Finally, a threshold is applied to the modified ratio image to classify pixels into shadow and nonshadow. The proposed power function helps the threshold to better classify pixels into shadow and nonshadow. It has been implemented and experimented extensively. A comparative study has also been made with existing methods in the literature. In comparing the proposed algorithm and some existing algorithms, the experimental results show that the proposed has the ability to detect shadows with satisfying accuracy.

Robust spatial information based tumour detection for brain MR images

Multimodal brain MR image analysis is still a challenging research area due to its complex intensity distribution and sensitivity towards the noise. Tumourous cells have different characteristics than normal human cells, which makes them more salient. In this Letter, the authors propose a novel unsupervised spatial information based saliency boosting tumour detection method which will help to identify tumourous cells by making it more clearly visible. Initially, a pseudo-coloured MR image is formed using the CIELab colour space. Saliency map has been established by calculating distance among scales varying elliptical windows in both spatial and colour space. Elliptical windows endeavour to cover-up curved outliers of the brain images. The average intensity value is kept constant by fixing the axis ratio for each window. The proposed algorithm has been evaluated on both real and simulated brain images of different patients from MICCAI-BRATS database. The performance analysis of the new algorithm exhibits higher accuracy with a low computational complexity as compared to other state of the art. The efficacy is due to the immobility of the window across rows and columns to move over the image. The novelty of the proposed technique is that neither it downscales the input images nor require any training bases.

Measurement of material surface defect intensity by distributed cumulative histogram and clustering

The object of research is a distributed cumulative histogram of a digital image and its advantages for auto-mated determination of the location and intensity of defects of different nature on the surfaces of materials: metal, paper, etc. The technique considered in the study is aimed at minimization of human interference in the process of material surface control from the moment of its photographing to the moment of making a decision about the surface quality. Three-dimensional distributed cumulative histogram (DCH) is presented as a two-dimensional image in which the pixel intensity corresponds to the third dimension – the number of pixels of a certain intensity in the original surface image. Informative distributed cumulative histogram (IDCH) is used to recognize black, dark and light defects, and to measure their intensity and location by the clustering algorithm. The average value of the pixel intensity in the columns and rows of the pixel matrix of the cumulative histogram image is calculated to estimate the intensity of the defects. Measurement of the intensity of defects is carried out in two ways: directly on the image of the surface sample and by comparing the image of the sample and the reference image of the sample without defects. To solve the problem, an algorithm of hierarchical clustering of data to rectangular segments of the surface image is used. In the image, each cluster is marked with a corresponding color of gray. The image for analysis is transformed using segmentation and inversion algorithms. This allows to get more accurate estimates of the intensity of light and dark defects. The clustering algorithm groups the image segments of the surface samples, as well as the images of the distributed cumulative histogram to group the level of surface damage. Distributed cumulative histogram was used to detect defects on the surface of materials as a method of linking the number and intensity of pixels to image coordinates. Cluster analysis helps to find their coordinates and intensity. In comparison with known approaches, the method has a linear algorithmic complexity to the number of pixels in the input image, which allows to do a significant number of experiments to identify the types of surfaces of materials for use and the features of algorithms.