Isfahan University of Medical Sciences

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Faculty Members have conducted research related to the Optimal Deep Learning Architecture for Automated Segmentation of Cysts in Oct Images Using X-Let Transforms
Maryam Monemian
Maryam Monemian

Assistant Professor of Electrical Engineering

Medical Image and Signal Processing Research Center

Isfahan University of Medical Sciences

Related Documents
1. Analysis of a Novel Segmentation Algorithm for Optical Coherence Tomography Images Based on Pixels Intensity Correlations, IEEE Transactions on Instrumentation and Measurement (2021)
2. Cyst Identification in Retinal Optical Coherence Tomography Images Using Hidden Markov Model, Scientific Reports (2023)
3. Directional Analysis of Intensity Changes for Determining the Existence of Cyst in Optical Coherence Tomography Images, Scientific Reports (2022)
4. Mathematical Analysis of Texture Indicators for the Segmentation of Optical Coherence Tomography Images, Optik (2020)
5. A New Texture-Based Segmentation Method for Optical Coherence Tomography Images, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS (2019)
6. A Computationally Efficient Red-Lesion Extraction Method for Retinal Fundus Images, IEEE Transactions on Instrumentation and Measurement (2023)
7. A New Texture-Based Labeling Framework for Hyper-Reflective Foci Identification in Retinal Optical Coherence Tomography Images, Scientific Reports (2024)
8. Detecting Red-Lesions From Retinal Fundus Images Using Unique Morphological Features, Scientific Reports (2023)
9. A Review on Texture-Based Methods for Anomaly Detection in Retinal Optical Coherence Tomography Images, Optik (2023)
10. Red-Lesion Extraction in Retinal Fundus Images by Directional Intensity Changes’ Analysis, Scientific Reports (2021)
11. Isfahan Artificial Intelligence Event 2023: Macular Pathology Detection Competition, Journal of Medical Signals and Sensors (2025)
12. Texture Modeling in Optical Coherence Tomography Images, Handbook of Texture Analysis: Generalized Texture for AI-Based Industrial Applications (2024)
All Documents
1. Isfahan Artificial Intelligence Event 2023: Lesion Segmentation and Localization in Magnetic Resonance Images of Patients With Multiple Sclerosis, Journal of Medical Signals and Sensors (2025)
2. Isfahan Artificial Intelligence Event 2023: Macular Pathology Detection Competition, Journal of Medical Signals and Sensors (2025)
3. A New Method for the Localization of Hard Exudates Based on Analyzing Intensity Incremental-Decremental Trends, IEEE Transactions on Instrumentation and Measurement (2024)
4. A New Texture-Based Labeling Framework for Hyper-Reflective Foci Identification in Retinal Optical Coherence Tomography Images, Scientific Reports (2024)
5. Texture Modeling in Optical Coherence Tomography Images, Handbook of Texture Analysis: Generalized Texture for AI-Based Industrial Applications (2024)
6. A Computationally Efficient Red-Lesion Extraction Method for Retinal Fundus Images, IEEE Transactions on Instrumentation and Measurement (2023)
7. A Review on Texture-Based Methods for Anomaly Detection in Retinal Optical Coherence Tomography Images, Optik (2023)
8. Cyst Identification in Retinal Optical Coherence Tomography Images Using Hidden Markov Model, Scientific Reports (2023)
9. Detecting Red-Lesions From Retinal Fundus Images Using Unique Morphological Features, Scientific Reports (2023)
10. Exudate Identification in Retinal Fundus Images Using Precise Textural Verifications, Scientific Reports (2023)
11. Directional Analysis of Intensity Changes for Determining the Existence of Cyst in Optical Coherence Tomography Images, Scientific Reports (2022)
12. Analysis of a Novel Segmentation Algorithm for Optical Coherence Tomography Images Based on Pixels Intensity Correlations, IEEE Transactions on Instrumentation and Measurement (2021)
13. Analysis of a Novel Segmentation Algorithm for Optical Coherence Tomography Images Based on Pixels Intensity Correlations, IEEE Transactions on Instrumentation and Measurement (2021)
14. Red-Lesion Extraction in Retinal Fundus Images by Directional Intensity Changes’ Analysis, Scientific Reports (2021)
15. Mathematical Analysis of Texture Indicators for the Segmentation of Optical Coherence Tomography Images, Optik (2020)
16. Mathematical Analysis of Texture Indicators for the Segmentation of Optical Coherence Tomography Images, Optik (2020)
17. A New Texture-Based Segmentation Method for Optical Coherence Tomography Images, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society# EMBS (2019)
18. A New Texture-Based Segmentation Method for Optical Coherence Tomography Images, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS (2019)