An image processing technique for mental health assessment from Electrophotonic

Almost every single existing Medical Imaging techniques available nowadays is dealing with captured non-invasive radiations spectrum (NIR) in digital image form to aid the visualization probes for disease diagnosis and treatment process. Electrophotonic Imaging (EPI) is one among them. Through exten...

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Main Author:	Janifal Alipal
Format:	Thesis
Language:	English
Published:	2019
Subjects:	TA Engineering (General) Civil engineering (General)
Online Access:	https://eprints.ums.edu.my/id/eprint/25250/1/An%20image%20processing%20technique%20for%20mental%20health%20assessment%20from%20Electrophotonic.pdf
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spelling	my-ums-ep.252502020-03-16T06:10:25Z An image processing technique for mental health assessment from Electrophotonic 2019 Janifal Alipal TA Engineering (General). Civil engineering (General) Almost every single existing Medical Imaging techniques available nowadays is dealing with captured non-invasive radiations spectrum (NIR) in digital image form to aid the visualization probes for disease diagnosis and treatment process. Electrophotonic Imaging (EPI) is one among them. Through extensive studied literatures, EPI is a current technique used in Integrative Medicine. In correlation to EPI development, this thesis is presenting an engineering approach on how the captured Kirlian effect in an image form indicates the energy level of human biofield. The study is introducing an Enhanced Region-specific algorithm, ERS to extract the captured Kirlian's 'digital signatures' as human radiated energy inside an EPI (Electrophotonic Imaging) image. The prevalence range of radiated energy on the EPI image is calculated based on the extracted significant (morphed-absolute) region of Kirlian effect on image and its most-significant region (the peak signals on the image). By utilizing image morphology transform, ERS is improving the procedure of blob extraction process using absolute arithmetic between the gray-level and binary slice of the image. In addition, ERS analysis deduces energy parameters as the image significant 'digital signature' for energy in Joules. In brief, through these digital parameters, the energy level of different mental health status in 160 images of healthy and mentally ill subjects is quantified. As a result, by using ERS algorithm, the image quality is improved and the extracted region derives the maximum and minimum significant region in the image, subsequently improve the existing extraction process. Through this capability, this study found that the energy of healthy subject based on its EPI images is cumulatively from 1.5 up to 2.5 x 10-27 Joules. Meanwhile above this range are mostly Anxiety, and below this range are confirmed for Acute Psychosis, Hypertension and Retarded. Henceforth, the finding subsequently offers new diagnostic information about captured Kirlian effects through the skin of human's fingertips. At the same time, this study also provides reasoning evidence that the recorded human biofield energy levels inside an EPI image can potentially be used as an alternative approach to aid early stage detection of mental illness and psychological state in future clinical practice. 2019 Thesis https://eprints.ums.edu.my/id/eprint/25250/ https://eprints.ums.edu.my/id/eprint/25250/1/An%20image%20processing%20technique%20for%20mental%20health%20assessment%20from%20Electrophotonic.pdf text en public mphil masters Universiti Malaysia Sabah Faculty of Engineering
institution	Universiti Malaysia Sabah
collection	UMS Institutional Repository
language	English
topic	TA Engineering (General) Civil engineering (General)
spellingShingle	TA Engineering (General) Civil engineering (General) Janifal Alipal An image processing technique for mental health assessment from Electrophotonic
description	Almost every single existing Medical Imaging techniques available nowadays is dealing with captured non-invasive radiations spectrum (NIR) in digital image form to aid the visualization probes for disease diagnosis and treatment process. Electrophotonic Imaging (EPI) is one among them. Through extensive studied literatures, EPI is a current technique used in Integrative Medicine. In correlation to EPI development, this thesis is presenting an engineering approach on how the captured Kirlian effect in an image form indicates the energy level of human biofield. The study is introducing an Enhanced Region-specific algorithm, ERS to extract the captured Kirlian's 'digital signatures' as human radiated energy inside an EPI (Electrophotonic Imaging) image. The prevalence range of radiated energy on the EPI image is calculated based on the extracted significant (morphed-absolute) region of Kirlian effect on image and its most-significant region (the peak signals on the image). By utilizing image morphology transform, ERS is improving the procedure of blob extraction process using absolute arithmetic between the gray-level and binary slice of the image. In addition, ERS analysis deduces energy parameters as the image significant 'digital signature' for energy in Joules. In brief, through these digital parameters, the energy level of different mental health status in 160 images of healthy and mentally ill subjects is quantified. As a result, by using ERS algorithm, the image quality is improved and the extracted region derives the maximum and minimum significant region in the image, subsequently improve the existing extraction process. Through this capability, this study found that the energy of healthy subject based on its EPI images is cumulatively from 1.5 up to 2.5 x 10-27 Joules. Meanwhile above this range are mostly Anxiety, and below this range are confirmed for Acute Psychosis, Hypertension and Retarded. Henceforth, the finding subsequently offers new diagnostic information about captured Kirlian effects through the skin of human's fingertips. At the same time, this study also provides reasoning evidence that the recorded human biofield energy levels inside an EPI image can potentially be used as an alternative approach to aid early stage detection of mental illness and psychological state in future clinical practice.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Janifal Alipal
author_facet	Janifal Alipal
author_sort	Janifal Alipal
title	An image processing technique for mental health assessment from Electrophotonic
title_short	An image processing technique for mental health assessment from Electrophotonic
title_full	An image processing technique for mental health assessment from Electrophotonic
title_fullStr	An image processing technique for mental health assessment from Electrophotonic
title_full_unstemmed	An image processing technique for mental health assessment from Electrophotonic
title_sort	image processing technique for mental health assessment from electrophotonic
granting_institution	Universiti Malaysia Sabah
granting_department	Faculty of Engineering
publishDate	2019
url	https://eprints.ums.edu.my/id/eprint/25250/1/An%20image%20processing%20technique%20for%20mental%20health%20assessment%20from%20Electrophotonic.pdf
_version_	1747836554027466752

An image processing technique for mental health assessment from Electrophotonic

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