Mahdad Esmaeili

Tabriz University of Medical Sciences

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Seyed Hossein Rasta

Tabriz University of Medical Sciences

Hossein Rabbani

Isfahan University of Medical Sciences

Ahmad Keshtkar

Iran University of Medical Sciences

Behrouz Khodadad

Tabriz University of Medical Sciences

Alireza Dehghani

Isfahan University of Medical Sciences

Naser Aslanabadi

Tabriz University of Medical Sciences

AlirezaMehri Dehnavi

Isfahan University of Medical Sciences

Ali Kazemi

Tehran University of Medical Sciences

Saeid Rashidi

Islamic Azad University, Tehran

Fariborz Rahimi

University of Bonab

Cooperative Institutions

Tabriz University of Medical Sciences

Isfahan University of Medical Sciences

University of Tabriz

Tehran University of Medical Sciences

University of Tehran

University of Isfahan

Shahid Beheshti University of Medical Sciences

Imam Reza Hospital

Iran University of Medical Sciences

Faculty (United Kingdom)

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Phage-displayed antibody fragments in microfluidic paper-based devices: a novel approach for sensitive detection of glycine-extended gastrin 17 biomarker using gold nanoparticles

Microfluidics and Nanofluidics (2025)

Shokouh Jahedi Mohammad Reza Tohidkia Mahdad Esmaeili Farhad Bani Saeed Kaboli

10.1007/s10404-025-02791-x

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Citations (0)

Automatic detection of exudates and optic disk in retinal images using curvelet transform

IET Image Processing (2012)

Mahdad Esmaeili Hossein Rabbani AlirezaMehri Dehnavi Alireza Dehghani

This work presents a curvelet-based algorithm for detection of optic disk (OD) and exudates on low contrast images. This algorithm which is composed of three main stages does not require user initialisation and is robust to the changes in the appearance of retinal fundus images. At first, bright candidate lesions in the image are extracted by employing DCUT and modification of curvelet coefficients of enhanced retinal image. For this purpose, the authors apply a new bright lesions enhancement on green plane of retinal image to obtain adequate illumination normalisation in the regions near the OD, and to increase brightness of lesions in dark areas such as fovea. Following this step, the authors introduce a new OD detection and boundary extraction method based on DCUT and level set method. Finally, bright lesions map (BLM) image is generated and to distinguish between exudates and OD (i.e. a false detection for the final exudates detection), the extracted candidate pixels in BLM that are not in OD regions (detected in previous step) are considered as actual bright lesions. The sensitivity and specificity of the authors exudates detection method are 98.4 and 90.1%, respectively, and the average accuracy of their OD boundary extraction method is 94.51%.

Curvelet

Fundus (uterus)

10.1049/iet-ipr.2011.0333

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Citations (57)

An Update on Choroidal Layer Segmentation Methods in Optical Coherence Tomography Images: a Review

Journal of Biomedical Physics and Engineering (2022)

Reza Alizadeh Eghtedar Mahdad Esmaeili Alireza Peyman‎Mohammadreza Akhlaghi Seyed Hossein Rasta

Choroid is one of the structural layers, playing a significant role in physiology of the eye and lying between the sclera and the retina. The segmentation of this layer could guide ophthalmologists in diagnosing most of the eye pathologies such as choroidal tumors and polypoidal choroidal vasculopathy. High signal-to-noise ratio and high speed imaging in Spectral-Domain Optical Coherence Tomography (SD-OCT) make choroidal imaging feasible. Several variables such as pre-operative axial length (AXL), time of day and age affect thickness of the choroidal vascularization and should be considered for segmentation of this layer. These days most of the eye specialists manually segment the choroidal layer which is time-consuming, tiresome and dependent on human errors. To overcome these difficulties, some studies have introduced different automatic choroidal segmentation methods. In this paper, we have conducted a comprehensive review on existing recently published methods for automatic choroidal segmentation algorithms.

Choroid

Sclera

Human eye

10.31661/jbpe.v0i0.1234

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Citations (9)

Phase Angle determinants in patients with cardiovascular disease using machine learning methods

Health and Technology (2021)

Seyed Amir Tabatabaei Hosseini Fariborz Rahimi Mahdad Esmaeili Mohammad Khalili

Body water

Phase angle (astronomy)

Stepwise regression

Bioelectrical Impedance Analysis

10.1007/s12553-021-00622-x

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Citations (4)

Automatic classification of fatty acid amide hydrolase polymorphism genotype based on EEG signal

Soft Computing (2024)

Reza Javanshir Mohammadreza Sedghi Mahdad Esmaeili Saeid Charsouei Leila Hosseinzadeh Anvar

10.1007/s00500-024-10306-z

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BACTERIAL INFECTIOUS KERATITIS AFTER LASIK: A CASE REPORT

Ali Foroutan Mahdad Esmaeili

LASIK

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Electroencephalographic Activity in Patients with Claustrophobia

Journal of Medical Signals & Sensors (2021)

Dunya Moradi Reza Eyvazpour Fariborz Rahimi Ali Jahan Seyed Hossein Rasta

Exposure to small confined spaces evokes physiological responses such as increased heart rate in claustrophobic patients. However, little is known about electrocortical activity while these people are functionally exposed to such phobic situations. The aim of this study was to examine possible changes in electrocortical activity in this population.Two highly affected patients with claustrophobia and two healthy controls participated in this in vivo study during which electroencephalographic (EEG) activity was continuously recorded. Relative power spectral density (rPSD) was compared between two situations of being relaxed in a well-lit open area, and sitting in a relaxed chair in a small (90 cm × 180 cm × 155 cm) chamber with a dim light. This comparison of rPSDs in five frequency bands of EEG was intended to investigate possible patterns of change in electrical activity during fear-related situation. This possible change was also compared between claustrophobic patients and healthy controls in all cortical areas.Statistical models showed that there is a significant interaction between groups of participants and experimental situations in all frequency bands (P < 0.01). In other words, claustrophobic patients showed significantly different changes in electrical activity while going from rest to the test situation. Clear differences were observed in alpha and theta bands. In the theta band, while healthy controls showed an increase in rPSD, claustrophobic patients showed an opposite decrease in the power of electrical activity when entering the confined chamber. In alpha band, both groups showed an increase in rPSD, though this increase was significantly higher for claustrophobic patients.The effect of in vivo exposure to confined environments on EEG activity is different in claustrophobic patients than in healthy controls. Most of this contrast is observed in central and parietal areas of the cortex, and in the alpha and theta bands.

Sitting

10.4103/jmss.jmss_62_20

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Citations (2)

Automatic optic disk detection by the use of curvelet transform

Mahdad Esmaeili Hossein Rabbani Alireza Mehri Dehnavi Alireza Dehghani

Efficient optic disk (OD) localization and segmentation are important tasks in automated retinal screening. In this paper, we take digital curvelet transform (DCUT) of the enhanced retinal image and modify its coefficients based on the sparsity of curvelet coefficients to get probable location of OD. If there are not yellowish objects in retinal images or their size are negligible, we can then directly detect OD location by performing Canny edge detector to reconstructed image with modified coefficients. Otherwise, if the size of these objects is eminent, we can see circular regions in edge map as candidate regions for OD. In this case, we use some morphological operations to fill these circular regions and erode them to get final locations for candidate regions and remove undesired pixels in edge map. Finally, we choose the candidate region that has maximum summation of pixels in strongest edge map that obtained by performing threshold to curvelet-based enhanced image, as final location of OD. This method has been tested on different retinal image datasets and quantitative results are presented.

Curvelet

Canny edge detector

10.1109/itab.2009.5394299

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Citations (9)

Supervised Contrastive Learning for Short Text Classification in Natural Language Processing

Mahdad Esmaeili Hamed Vahdat‐Nejad

10.1109/iccke65377.2024.10874505

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Automatic classification of schizophrenia patients using resting-state EEG signals

Physical and Engineering Sciences in Medicine (2021)