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: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2019
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| Subjects: | |
| 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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| Summary: | 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. |
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