Kuryati Kipli

Towards automated detection of depression from brain structural magnetic resonance images.

IntroductionDepression is a major issue worldwide and is seen as a significant health problem. Stigma and patient denial, clinical experience, time limitations, and reliability of psychometrics are barriers to the clinical diagnoses of depression. Thus, the establishment of an automated system that could detect such abnormalities would assist medical experts in their decision-making process. This paper reviews existing methods for the automated detection of depression from brain structural magnetic resonance images (sMRI).MethodsRelevant sources were identified from various databases and online sites using a combination of keywords and terms including depression, major depressive disorder, detection, classification, and MRI databases. Reference lists of chosen articles were further reviewed for associated publications.ResultsThe paper introduces a generic structure for representing and describing the methods developed for the detection of depression from sMRI of the brain. It consists of a number of components including acquisition and preprocessing, feature extraction, feature selection, and classification.ConclusionAutomated sMRI-based detection methods have the potential to provide an objective measure of depression, hence improving the confidence level in the diagnosis and prognosis of depression.

Biological Effect of 900MHz and 1800MHz Mobile Phones in SAR Weight

In recent years, the public concern on human biological effect of mobile phone is increasing due to the widely used of mobile phones by public including the children. As a consequences, a safety guidelines have been established by the various public organization such as World Health Organization (WHO) and International Commission on Non-Ionization Radiation Protection (ICNIRP). The main objective of this paper is to investigate and study on the human biological effect that may cause by the used of 900MHz and 1800MHz mobile phones. For the purpose of the assessment, Finite-Difference Time Domain (FDTD) is used to simulate the modeling of mobile phone attached to human head in order to obtain the Specific Absorption Rate (SAR) value of mobile phones that absorbed into human head. The minimum SAR value stated by International Commission on Non-Ionization Radiation Protection (ICNIRP) is 2W/kg. When the limit is exceeds, it may produces adverse human health effects that contibute to reverse cell membrane polarity, alter brain waves and damage DNA. This leads to cancer and memory loss. However, mobile phones are designed with low power and operate at high frequency, resulted in lower SAR value comparing to 2W/kg that stated by ICNIRP. Other temporary biological or potential human health effects that may produce by mobile phones are heating, headache, fuzziness, fatigue and nausea. By the way, further studies or researches are needed in order to draw more complete picture of health risks that may cause by the used of mobile phones.

Investigating machine learning techniques for detection of depression using structural MRI volumetric features

Structural MRI offers anatomical details and high sensitivity to pathological changes. It can demonstrate certain patterns of brain changes present at a structural level. Research to date has shown that volumetric analysis of brain regions has importance in depression detection. However, such analysis has had very minimal use in depression detection studies at individual level. Optimally combining various brain volumetric features/attributes, and summarizing the data into a distinctive set of variables remain difficult. This study investigates machine learning algorithms that automatically identify relevant data attributes for depression detection. Different machine learning techniques are studied for depression classification based on attributes extracted from structural MRI (sMRI) data. The attributes include volume calculated from whole brain, white matter, grey matter and hippocampus. Attributes subset selection is performed aiming to remove redundant attributes using three filtering methods and one hybrid method, in combination with ranker search algorithms. The highest average classification accuracy, obtained by using a combination of both SVM-EM and IG-Random Tree algorithms, is 85.23%. The classification approach implemented in this study can achieve higher accuracy than most reported studies using sMRI data, specifically for detection of depression.

Application of Particle Swarm Optimization in Histogram Equalization for image enhancement

The basic and most common technique for contrast adjustment in the image is using Histogram Equalization (HE). It is based on equalizing the histogram of the image and subsequently enhancing its contrast, and results in overall contrast improvement. This paper introduces a combination of normal HE technique and Particle Swarm Optimization (PSO) algorithm for enhancing distorted image naturally. The process is as follows. The image is separated into red, green and blue (RGB) channels and PSO algorithm is applied into each channel in order to get its best fitness value. The fitness value that is obtained then will be applied into HEs normalization, after that the processed color image will be merged back to RGB image. Experimental results have shown the effectiveness in improving the contrast of the original images without introducing disturbing artifacts caused by normal HE.

Human health implication of 900MHz and 1800MHz mobile phones

In recent years, there has been increasing public concern about the health implication of Electromagnetic (EM) wave exposures due to the mobile phone. For this reason, various public organizations in the world have been established safety guidelines. This paper focused on the investigation or study of human health effect that cause by the used of 900MHz and 1800MHz mobile phones. For the purpose of the assessment, Finite-Difference Time Domain (FDTD) is used to find the Specific Absorption Rate (SAR) value of mobile phones that absorbed into human head. The minimum SAR value stated by International Commission on Non-Ionization Radiation Protection (ICNIRP) is 4W/kg. When the limit is exceeds, it produces human health effects where it can caused reverse cell membrane polarity, alter brain waves and brain chemistry and damage DNA. This leads to cancer and memory loss. However, mobile phones are designed with low power and operate at high frequency where the value of SAR is lower than the limit that stated by ICNIRP. Other temporary biological or potential human health effects produce by mobile phones are heating, headache, fuzziness, fatigue and nausea. By the way, further studies or researches are needed in order to draw more complete picture of health risks that cause by the use of mobile phones.

Computer-aided detection of depression from magnetic resonance images

Magnetic resonance imaging (MRI) of the brain is used to detect depression disorder. However, a large number of MRI scans needs to be analyzed for such detection. Manual segmentation of the biomarkers in MRI scans by clinical experts can become time consuming and sometimes erroneous. This paper presents a study on computer-aided detection of depression from MRI scans. These systems have not yet been identified, categorized and compared in the literature. The paper covers fully automated to semi-automated detection systems. It also presents performance comparison for the considered systems.

Evaluation of feature selection algorithms for detection of depression from brain sMRI scans

Detection of depression from structural MRI (sMRI) scans is relatively new in the mental health diagnosis. Such detection requires processes including image acquisition and pre-processing, feature extraction and selection, and classification. Identification of a suitable feature selection (FS) algorithm will facilitate the enhancement of the detection accuracy by selection of important features. In the field of depression study, there are very limited works that evaluate feature selection algorithms for sMRI data. This paper investigates the performance of four algorithms for FS of volumetric attributes in sMRI scans. The algorithms are One Rule (OneR), Support Vector Machine (SVM), Information Gain (IG) and ReliefF. The performances of the algorithms are determined through a set of experiments on sMRI brain scans. An experimental procedure is developed to measure the performance of the tested algorithms. The result of the evaluation of the FS algorithms is discussed by using a number of analyses.

Electromagnetic radiation towards adult human head from 900MHz handheld mobile phone

Electromagnetic radiation produce by mobile phone and the relationship with the humans health is not a new issue nowadays. Since the used of mobile phone had increased rapidly over the past few years, people are becoming more concern with their health when dealing with the so-called electromagnetic radiation. This type of radiation would leads to heating of body tissue at specific rate called the thermal radiation. Thermal radiation depends on the frequency of the energy, the power density of the radio frequency field that strikes the body and the polarization of wave. This paper will discuss on the result collected from the thermal radiation generated by handheld mobile phone with frequency of 900 MHz towards adult human head. The analysis is conducted in a laboratory with average of 45 minutes talking hour with two different types of mobile phone, internal and external antenna. The results show an increased of heat especially at the place near the ear skull after 45 minutes of operation. When comparing both different types of mobile phone, mobile phone with external antenna produce more heat compared to mobile phone with internal antenna.

Degree of contribution (DoC) feature selection algorithm for structural brain MRI volumetric features in depression detection

PurposeAccurate detection of depression at an individual level using structural magnetic resonance imaging (sMRI) remains a challenge. Brain volumetric changes at a structural level appear to have importance in depression biomarkers studies. An automated algorithm is developed to select brain sMRI volumetric features for the detection of depression.MethodsA feature selection (FS) algorithm called degree of contribution (DoC) is developed for selection of sMRI volumetric features. This algorithm uses an ensemble approach to determine the degree of contribution in detection of major depressive disorder. The DoC is the score of feature importance used for feature ranking. The algorithm involves four stages: feature ranking, subset generation, subset evaluation, and DoC analysis. The performance of DoC is evaluated on the Duke University Multi-site Imaging Research in the Analysis of Depression sMRI dataset. The dataset consists of 115 brain sMRI scans of 88 healthy controls and 27 depressed subjects. Forty-four sMRI volumetric features are used in the evaluation.ResultsThe DoC score of forty-four features was determined as the accuracy threshold (Acc_Thresh) was varied. The DoC performance was compared with that of four existing FS algorithms. At all defined Acc_Threshs, DoC outperformed the four examined FS algorithms for the average classification score and the maximum classification score.ConclusionDoC has a good ability to generate reduced-size subsets of important features that could yield high classification accuracy. Based on the DoC score, the most discriminant volumetric features are those from the left-brain region.

An Empirical Comparison of Classification Algorithms for Diagnosis of Depression from Brain SMRI Scans

To be diagnostically effective, structural magnetic resonance imaging (sMRI) must reliably distinguish a depressed person from a healthy one at individual scan level. One of the tasks in the automated diagnosis of depression from brain sMRI is the classification of samples (i.e., depressed/not depressed, remitted/not-remitted depression) based on the values of their features. Thus far, very limited works have been reported on identifying a suitable classification algorithm for depression detection. In this paper, ten different types of classification algorithms are applied to depression diagnosis and their performance is compared, through a set of experiments on sMRI brain scans. In the experiments, a procedure is developed to measure the performance of these algorithms and an evaluation method is employed to evaluate and compare the performance of the classifiers.