Compression of remote sensing data using second-generation wavelets: a review

Wavelet-based methods have been widely used for compression of remotely sensed images and data. Recently, second generation of wavelets which is based on a method called lifting has proven to be more effective than traditional wavelets as it provides lossless compression, lowers the memory usage, and is computationally faster. This study explores the literature related to applying second-generation wavelets for the compression of remote sensing data. Nevertheless, in order to compare the results of two wavelet types, some applications of traditional wavelets are also presented

A stable and accurate wavelet-based method for noise reduction from hyperspectral vegetation spectrum

Hyperspectral vegetation spectrum is normally contaminated with noise and the presence of noise affects the results of vegetation studies, such as species discrimination and classification, disease detection, stress assessment and the estimation of vegetation’s biophysical and biochemical characteristics. Additionally, hyperspectral signals are usually studied using the derivative analysis method that is very sensitive to noise in the data. This study investigates denoising of the hyperspectral vegetation spectrum using different wavelet-based methods. A test signal and several real-world vegetation spectra are denoised using four wavelet methods: traditional discrete wavelet transform (DWT); stationary wavelet transform (SWT); lifting wavelet transform (LWT); and a combination of SWT and LWT, which in this paper is called stationary lifting wavelet transform (SLWT). SLWT incorporates the advantages of both SWT and LWT methods, including a translation invariance property and a fast simple algorithm. Experimental results show that SLWT highly outperforms other wavelet-based methods in terms of accuracy and visual quality. Furthermore, this research reveals the following novel results: SLWT 1) for different levels of decomposition of the wavelet transform gives similar results and its denoising results is independent to the selection of decomposition level; 2) generates stable statistical results; 3) can make use of mother wavelets with small filter size (i.e., low-order mother wavelets) that are suitable for preserving subtle features in vegetation spectrum; and 4) its denoising results do not depend on the selection of the mother wavelet when applying low-order mother wavelets

Application of Imaging Technologies in Breast Cancer Detection: A Review Article

One of the techniques utilised in the management of cancer in all stages is multiple biomedical imaging. Imaging as an important part of cancer clinical protocols can provide a variety of information about morphology, structure, metabolism and functions. Application of imaging technics together with other investigative apparatus including in fluids analysis and vitro tissue would help clinical decision-making. Mixed imaging techniques can provide supplementary information used to improve staging and therapy planning. Imaging aimed to find minimally invasive therapy to make better results and reduce side effects. Probably, the most important factor in reducing mortality of certain cancers is an early diagnosis of cancer via screening based on imaging. The most common cancer in women is breast cancer. It is considered as the second major cause of cancer deaths in females, and therefore it remained as an important medical and socio-economic issue. Medical imaging has always formed part of breast cancer care and has used in all phases of cancer management from detection and staging to therapy monitoring and post-therapeutic follow-up. An essential action to be performed in the preoperative staging of breast cancer based on breast imaging. The general term of breast imaging refers to breast sonography, mammography, and magnetic resonance tomography (MRT) of the breast (magnetic resonance mammography, MRM). Further development in technology will lead to increase imaging speed to meet physiological processes requirements. One of the issues in the diagnosis of breast cancer is sensitivity limitation. To overcome this limitation, complementary imaging examinations are utilised that traditionally includes screening ultrasound, and combined mammography and ultrasound. Development in targeted imaging and therapeutic agents calls for close cooperation among academic environment and industries such as biotechnological, IT and pharmaceutical industries

Investigation into Breast Cancer and Partial Breast Reconstruction: A Review

Growing increasingly in South America, Africa and Asia, breast cancer is known as the dominant type of cancer in women. Different treatments are available for breast cancer, among which surgery is the most widely used, but researchers are trying to develop new strategies. One of the most prominent surgical methods is referred to as oncoplastic surgery, that helps to remove segments of malignant breast tissue. This type of surgery aims to obtain vast surgical margins, while the remaining tissue is rearranged so that the better cosmetic outcome is obtained. This review will investigate the breast cancer and then discuss partial breast reconstruction. Before outlining the procedures, the different types of partial breast reconstruction will be discussed. Finally, advantages and disadvantages will be outlined. MEDLINE database was used to conduct the search. The main terms used were ‘Conservation Breast Surgery Reconstruction’ AND ‘Oncoplastic Surgery’, ‘Partial Mastectomy Reconstruction’ AND ‘Conservative Breast Surgery Reconstruction’, ‘oncoplastic’ [All Fields], ‘breast’ AND ‘surgery’ OR ‘surgery’ operative’, ‘oncoplastic’ (‘breast’)’. The bibliographies of relevant papers were manually searched up to October 2018, but more recent voices are also included

A review of applying second-generation wavelets for noise removal from remote sensing data.

The processing of remotely sensed data includes compression, noise reduction, classification, feature extraction, change detection and any improvement associated with the problems at hand. In the literature, wavelet methods have been widely used for analysing remote sensing images and signals. The second-generation of wavelets, which is designed based on a method called the lifting scheme, is almost a new version of wavelets, and its application in the remote sensing field is fresh. Although first-generation wavelets have been proven to offer effective techniques for processing remotely sensed data, second-generation wavelets are more efficient in some respects, as will be discussed later. The aim of this review paper is to examine all existing studies in the literature related to applying second-generation wavelets for denoising remote sensing data. However, to make a better understanding of the application of wavelet-based denoising methods for remote sensing data, some studies that apply first-generation wavelets are also presented. In the part of hyperspectral data, there is a focus on noise removal from vegetation spectrum

Improved method for noise removal from hyperspectral vegetation spectrum using second generation wavelets

Many of vegetation studies make use of the vegetation reflectance spectra acquired by hyperspectral remote sensing technique. However, the hyperspectral vegetation spectra are highly noisy and the presence of noise affects the results of the spectral discrimination between vegetation species. Moreover, in hyperspectral studies it is common to perform analysis based on derivatives of the spectrum. This method is very sensitive to noise; therefore, noise removal is essential before performing derivative analysis. Also, to relate the cover reflectance to image reflectance in hyperspectral remote sensing imagery, noise-free field spectra are essential. Compared to traditional smoothing methods, wavelet transform shows promising results in denoising area. This thesis applied different types of wavelet transforms including discrete wavelet transform (DWT), lifting wavelet transform (LWT) which is the basis of the second generation wavelets, stationary wavelet transform (SWT),and the proposed method that is based on a combination of LWT and SWT methods that is called stationary lifting wavelet transform (SLWT). The objective of this research is to propose an accurate and stable method based on SLWT for noise removal from hyperspectral vegetation spectrum. The proposed method takes into account the characteristics of the vegetation reflectance spectrum and its results are compared with other three wavelet methods that are DWT, LWT, and SWT. These wavelet techniques were examined on a synthetic vegetation spectrum which is created by PROSPECT leaf model (a model of leaf optical properties spectra) and on several real-world vegetation spectra. To assess the effects of denoising several indicators including root mean square error (RMSE), signal-to-noise ratio (SNR), correlation coefficient and visual evaluation methods were employed. The experimental results showed that compared to other wavelet methods, the proposed method produced highly accurate statistical results. The best denoising results were acquired by applying Haar mother wavelet by making 13% improvement for SNR and by giving an RMSE as low as 0.0002 and correlation coefficient value of almost one. The visual evaluation showed that the proposed method preserves the absorption features and inflection points, as well as the wavelength positioning of local minima and maxima. Furthermore, the following novel results are concluded from this thesis: the proposed method is level-independent and narrow downs the choice of mother wavelet to a few low-order mother wavelets; as a result, it highly lowers the complexity of the denoising process. Unlike other wavelet-based methods, the proposed method gives reliable and predictable statistical results therefore is a stable method for noise removal from hyperspectral vegetation spectrum

The anti-planktonic and anti-biofilm formation activity of Iranian pomegranate peel hydro-extract against Staphylococcus aureus

Staphylococcal infections and contaminations have elicited a growing and perennial concern in the medical and food industries. Meanwhile, the manifestation of antibiotic-resistant strains such as methicillin-resistant Staphylococcus aureus (MRSA) beside the production of disinfectant-resistant biofilms makes the confrontation with the bacteria more cumbersome and challenging. Pomegranate peel as a waste product of juicing factories is a natural antibacterial agent. The pomegranate peel hydro-extract (PPHE), as a bio-friendly material, was prepared from an Iranian pomegranate cultivar, Rabab, and its phenolic compounds and antioxidant (via DPPH and FRAP assays) and anti-staphylococcal (anti-planktonic and anti-biofilm) properties were assessed. The Rabab PPHE inhibited planktonic cells and biofilm formation of three S. aureus. The Rabab PPHE produced large and obvious staphylococcal inhibition zones in which their diameters were significantly dose-dependent for the milk isolated S. aureus (p < 0.05). Despite the resistance of MRSA (ATCC 33591) to beta-lactam antibiotics, the minimum inhibitory concentration (MIC) of PPHE against its planktonic cells was only 3.75mg mL-1. Furthermore, Rabab PPHE inhibited bacterial biofilms formation in a dose-dependent manner. The MIC of Rabab PPHE against planktonic milk-isolated S. aureus, S. aureus (ATCC 29737), and MRSA prevented 47, 36, and 26% of their biofilm formation, respectively. This addresses the differences between the anti-planktonic and anti-biofilm activity of Rabab PPHE. The anti-planktonic and to a lesser extent the anti-biofilm forming activity of this water-based extract supports the notion of its effectiveness and salubrious application in food and pharmaceutical industries

Teacher-student communication in medical sciences: a qualitative study in Iran

Introduction: The existence of an effective relationship between teachers and students plays a pivotal role in the improvement of the education process, learning, and students&rsquo; growth. Objectives: The present study aimed to determine the experiences of students and professors of medical sciences from the effective factors in the student-teacher relationship. Methods: This qualitative study was conducted on 10 professors and 10 students. The study data were collected using Individual in-depth interviews, focus group interviews, and observation. The study data were analyzed using conventional content analysis proposed by Granheim and Landman. Results: The results revealed 16 subcategories and 4 main categories. The main categories included &ldquo;adherence to moral values&rdquo;, &ldquo;professor&rsquo;s professional competence&rdquo;, &ldquo;sociocultural factors&rdquo;, and&rdquo; clinical communication&rdquo;. Conclusions: The present findings helped to identify a wide range of the dimensions and various factors affecting the student-teacher relationship in medical universities. Educational Managers and decision-makers can use the results of the study to determine the problems of the relationship process between the Professor and the student and to decide on the appropriate action to develop this important proces

Design and Psychometrics of Measurement Tool of Health Needs in Patients with Chronic Back Ache

he necessity of valid, reliable and objective toolsis one of the subjects that have always beenemphasized in studies related to the health ofindividuals. However, it is believed that existing tools generallydo not have the necessary credibility and validityand cannot correctly assess the health needs of patientswith chronic back ache. This study aimed to design a validand reliable tool for assessing the health needs of patientswith chronic back ache.This is an exploratory sequential mixed (qualitative-quantitative)method research conducted in medical sciencesclinics of the Ministry of Health and Medical Education inShiraz in 2017. In the qualitative content analysis phasethrough interview with patients, their family carers, thetreatment team in relation to these patients, the healthneeds of patients with chronic back ache were definedand then, based on the findings and with extensive reviewof the texts, the tool&rsquo;s dimensions and items weredesigned. Then, in the quantitative phase of the psychometricsof tool, the content (quantitative and qualitative)validity and structural validity (factor analysis) were doneby using different methods of formal (quantitative andqualitative) validity. Reliability was also calculated throughinternal consistency and stability.29 people are the participants included patients withchronic back ache, family carers and specialists associatedwith the disease. The health needs of patients withchronic back ache were explained in four dimensions: educationand information needs, spiritual / religious needs,socio-economic needs, and physical-psychological needs.Based on these four dimensions, 109 original items weredesigned. 91 items in the Item Impact section acquiredthe scores 1/5 and higher. In terms of content validity, 49items received 0.49 and higher. The content validity indexof the individual items was equal to one, and the contentvalidity index of the entire tool, both as S-CVI / Universaland as S-CVI / Average, was 1 at this stage. The kappacoefficient of all the terms was equal to one.In this structure, using exploratory factor analysis, fourfactors were also explained; they were named based onthe items of each group. The internal consistency of thetool by calculating the total Cronbach&rsquo;s alpha coefficientwas 0.75, which was 0.73 in education and 0.72 in social/economic needs, 0.74 in physical needs and 0.72 inpsychological needs. The intra-cluster correlation wasequal to &hellip;The designed tool for the needs of chronic back ache patientshas different dimensions and has a good validity andreliability. This tool can provide an appropriate assessmentof the health needs of patients with chronic back acheand improve the quality of services provided to patients

Monitoring the impacts of crop residue cover on agricultural productivity and soil chemical and physical characteristics

Abstract To the best of our knowledge, the impacts of crop residue cover (CRC) on agricultural productivity and soil fertility have not been studied by previous researchers. In this regard, this study aims to apply an integrated approach of remote sensing and geospatial analysis to detect CRC and monitor the effects of CRC on agricultural productivity, as well as soil chemical and physical characteristics. To achieve this, a series of Landsat images and 275 ground control points (GCPs) collected from the study areas for the years 2013, 2015, and 2021 were used. A convolutional neural network (CNN), a class of artificial neural network has commonly applied to analyze visual imagery, was employed in this study for CRC detection in two classes (Not-CRC and CRC) for the years 2013, 2015, and 2021. To assess the effects of CRC, the Normalized Difference Vegetation Index (NDVI) was applied to Landsat image series for the years 2015 (22 images), 2019 (20 images), and 2022 (23 images). Furthermore, this study evaluates the impacts of CRC on soil fertility based on collected field observation data. The results show a high performance (Accuracy of > 0.95) of the CNN for CRC detection and mapping. The findings also reveal positive effects of CRC on agricultural productivity, indicating an increase in vegetation density by about 0.1909 and 0.1377 for study areas 1 and 2, respectively, from 2015 to 2022. The results also indicate an increase in soil chemical and physical characteristics, including EC, PH, Na, Mg, HCO3, K, silt, sand, and clay from 2015 to 2022, based on physical examination. In general, the findings underscore that the value of an integrated approach of remote sensing and geospatial analysis for detecting CRC and monitoring its impacts on agricultural productivity and soil fertility. This research can offer valuable insight to researchers and decision-makers in the field of soil science, land management and agriculture