Dr. Sokratis Makrogiannis Profile

Dr. Sokratis Makrogiannis

Associate Professor

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Author

Publications (17)

Proceedings Article | 2 April 2024 Poster + Paper

Multi-method and multi-atlas segmentation fusion for delineation of thigh muscle groups in 3D water-fat separated MRI

Nagasoujanya Annasamudram, Azubuike Okorie, Richard Spencer, Rita Kalyani, Qi Yang, Bennett Landman, Luigi Ferrucci, Sokratis Makrogiannis

Proceedings Volume 12926, 129262X (2024) https://doi.org/10.1117/12.3006894

KEYWORDS: Image segmentation, Muscles, Deformation, Tissues, Magnetic resonance imaging, 3D image processing, Image registration, Deep learning

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SPIE Journal Paper | 12 July 2023

Single slice thigh CT muscle group segmentation with domain adaptation and self-training

Qi Yang, Xin Yu, Ho Hin Lee, Leon Cai, Kaiwen Xu, Shunxing Bao, Yuankai Huo, Ann Zenobia Moore, Sokratis Makrogiannis, Luigi Ferrucci, Bennett Landman

JMI, Vol. 10, Issue 04, 044001, (July 2023) https://doi.org/10.1117/12.10.1117/1.JMI.10.4.044001

KEYWORDS: Muscles, Image segmentation, Computed tomography, Magnetic resonance imaging, Education and training, Anatomy, Bone, Gallium nitride, Data modeling, Adversarial training

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Proceedings Article | 7 April 2023 Poster + Paper

Breast mass characterization using sparse approximations of patch-sampled deep features

Chelsea Harris, Sokratis Makrogiannis

Proceedings Volume 12465, 124652A (2023) https://doi.org/10.1117/12.2653926

KEYWORDS: Feature extraction, Mammography, Breast, Machine learning, Neural networks, Image classification, Computer aided detection, Breast imaging

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Proceedings Article | 3 April 2023 Poster + Paper

Semantic segmentation of individual muscle groups in 3D thigh MRI

Azubuike Okorie, Sokratis Makrogiannis, Richard Spencer, Rita Kalyani, Luigi Ferrucci

Proceedings Volume 12464, 124643D (2023) https://doi.org/10.1117/12.2654507

KEYWORDS: Muscles, Image segmentation, Magnetic resonance imaging, Deformation, Silver, Image registration, Tissues, 3D image processing, 3D magnetic resonance imaging, Matrices

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SPIE Journal Paper | 19 May 2022 Open Access

Label efficient segmentation of single slice thigh CT with two-stage pseudo labels

Qi Yang, Xin Yu, Ho Hin Lee, Yucheng Tang, Shunxing Bao, Kristofer Gravenstein, Ann Zenobia Moore, Sokratis Makrogiannis, Luigi Ferrucci, Bennett A. Landman

JMI, Vol. 9, Issue 05, 052405, (May 2022) https://doi.org/10.1117/12.10.1117/1.JMI.9.5.052405

KEYWORDS: Image segmentation, Bone, Computed tomography, Tissues, Data modeling, Neural networks, Medical imaging, 3D modeling, Performance modeling, Surgery

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Proceedings Article | 4 April 2022 Poster + Paper

Simulation of mid-thigh anatomy for virtual clinical studies

Azubuike Okorie, Predrag Bakic, Sokratis Makrogiannis

Proceedings Volume 12031, 120313Q (2022) https://doi.org/10.1117/12.2613261

KEYWORDS: Tissues, Computer simulations, Bone, Virtual reality, Systems modeling, Imaging systems, Breast, Statistical analysis, Breast imaging, Statistical modeling

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Proceedings Article | 4 April 2022 Poster + Paper

TIDAQUNET: tissue identification and quantification network for mid-thigh CT segmentation

Sokratis Makrogiannis, Nagasoujanya Annasamudram, Taposh Biswas

Proceedings Volume 12032, 1203234 (2022) https://doi.org/10.1117/12.2613304

KEYWORDS: Tissues, Image segmentation, Bone, Computed tomography, Computer programming, Network architectures, Neural networks, Medical research, Medical imaging

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Proceedings Article | 4 April 2022 Presentation + Paper

Quantification of muscle, bones, and fat on single slice thigh CT

Qi Yang, Xin Yu, Ho Hin Lee, Yucheng Tang, Shunxing Bao, Kristofer Gravenstein, Ann Zenobia Moore, Sokratis Makrogiannis, Luigi Ferrucci, Bennett Landman

Proceedings Volume 12032, 120321K (2022) https://doi.org/10.1117/12.2611664

KEYWORDS: Image segmentation, Bone, Tissues, Data modeling, Computed tomography, Medical imaging, Computer science, Neural networks

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Proceedings Article | 15 February 2021 Presentation + Paper

Sparse analysis of deep features for characterization of breast masses

Sokratis Makrogiannis, Keni Zheng, Chelsea Harris

Proceedings Volume 11597, 115970E (2021) https://doi.org/10.1117/12.2582321

KEYWORDS: Breast, Breast cancer, Skin cancer, Associative arrays, Receivers, Mammography, Diagnostics, Convolutional neural networks, Cancer

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Proceedings Article | 10 March 2020 Paper

Multi-atlas-based tissue identification in the lower leg using pQCT

Sokratis Makrogiannis, Azubuike Okorie, Taposh Biswas, Luigi Ferrucci

Proceedings Volume 11313, 113133D (2020) https://doi.org/10.1117/12.2550010

KEYWORDS: Tissues, Image segmentation, Bone, Image quality, Motion models, Expectation maximization algorithms, Signal to noise ratio, Clinical trials, Medical imaging, Fusion energy

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Proceedings Article | 13 March 2019 Paper

Ensembles of sparse classifiers for osteoporosis characterization in digital radiographs

Keni Zheng, Rachid Jennane, Sokratis Makrogiannis

Proceedings Volume 10950, 1095024 (2019) https://doi.org/10.1117/12.2511179

KEYWORDS: Image classification, Bone, Radiography, Classification systems, Algorithm development

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SPIE Journal Paper | 6 September 2016 Open Access

Imaging and tracking HIV viruses in human cervical mucus

Fatima Boukari, Sokratis Makrogiannis, Ralph Nossal, Hacene Boukari

JBO, Vol. 21, Issue 09, 096001, (September 2016) https://doi.org/10.1117/12.10.1117/1.JBO.21.9.096001

KEYWORDS: Viruses, Confocal microscopy, Diffusion, Particles, Statistical analysis, Motion analysis, Luminescence, Biological research, Confocal fluorescent microscopy, Image analysis

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Proceedings Article | 5 June 2014 Paper

Software system for computing material and structural properties of bone and muscle in the lower extremity from pQCT

Sokratis Makrogiannis, Luigi Ferrucci

Proceedings Volume 9112, 911216 (2014) https://doi.org/10.1117/12.2050790

KEYWORDS: Bone, Tissues, Image segmentation, Imaging systems, Minerals, Computing systems, Java, Computed tomography, Imaging technologies, Human-machine interfaces

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Proceedings Article | 9 March 2010 Paper

Automated liver lesion characterization using fast kVp switching dual energy computed tomography imaging

Alberto Santamaria-Pang, Sandeep Dutta, Sokratis Makrogiannis, Amy Hara, William Pavlicek, Alvin Silva, Brian Thomsen, Scott Robertson, Darin Okerlund, David Langan, Rahul Bhotika

Proceedings Volume 7624, 76240V (2010) https://doi.org/10.1117/12.844059

KEYWORDS: Computed tomography, Dual energy imaging, Liver, Image classification, Switching, Signal attenuation, Tumors, Imaging spectroscopy, Medicine, X-ray computed tomography

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Hypodense metastases are not always completely distinguishable from benign cysts in the liver using conventional Computed Tomography (CT) imaging, since the two lesion types present with overlapping intensity distributions due to similar composition as well as other factors including beam hardening and patient motion. This problem is extremely challenging for small lesions with diameter less than 1 cm. To accurately characterize such lesions, multiple follow-up CT scans or additional Positron Emission Tomography or Magnetic Resonance Imaging exam are often conducted, and in some cases a biopsy may be required after the initial CT finding. Gemstone Spectral Imaging (GSI) with fast kVp switching enables projection-based material decomposition, offering the opportunity to discriminate tissue types based on their energy-sensitive material attenuation and density. GSI can be used to obtain monochromatic images where beam hardening is reduced or eliminated and the images come inherently pre-registered due to the fast kVp switching acquisition. We present a supervised learning method for discriminating between cysts and hypodense liver metastases using these monochromatic images. Intensity-based statistical features extracted from voxels inside the lesion are used to train optimal linear and nonlinear classifiers. Our algorithm only requires a region of interest within the lesion in order to compute relevant features and perform classification, thus eliminating the need for an accurate segmentation of the lesion. We report classifier performance using M-fold cross-validation on a large lesion database with radiologist-provided lesion location and labels as the reference standard. Our results demonstrate that (a) classification using a single projection-based spectral CT image, i.e., a monochromatic image at a specified keV, outperforms classification using an image-based dual energy CT pair, i.e., low and high kVp images derived from the same fast kVp acquisition and (b) classification using monochromatic images can achieve very high accuracy in separating benign liver cysts and metastases, especially for small lesions.

SPIE Journal Paper | 1 January 2010

Efficient registration of multitemporal and multisensor aerial images based on alignment of nonparametric edge features

Sokratis Makrogiannis, Nikolaos Bourbakis

JEI, Vol. 19, Issue 01, 013002, (January 2010) https://doi.org/10.1117/12.10.1117/1.3293436

KEYWORDS: Image registration, Image processing, Sensors, Genetic algorithms, Optimization (mathematics), Statistical analysis, Visualization, Stochastic processes, Distance measurement, Edge detection

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Proceedings Article | 16 September 2005 Paper

Object detection and segmentation in camouflaged environments

S. Makrogiannis, M. Trujillo San-Martin

Proceedings Volume 5909, 59091K (2005) https://doi.org/10.1117/12.618357

KEYWORDS: Image segmentation, Fuzzy logic, Image classification, Feature extraction, Feature selection, Image processing algorithms and systems, Environmental sensing, Statistical analysis, Image analysis, Video

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SPIE Journal Paper | 1 July 2005

Watershed-based multiscale segmentation method for color images using automated scale selection

Sokratis Makrogiannis, I. Vanhamel, Spiros Fotopoulos, Hichem Sahli, Jan Cornelis

JEI, Vol. 14, Issue 03, 033007, (July 2005) https://doi.org/10.1117/12.10.1117/1.1995711

KEYWORDS: Image segmentation, Fuzzy logic, Image processing, Color image processing, Diffusion, Statistical analysis, Image analysis, Edge detection, Anisotropic diffusion, RGB color model

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Showing 5 of 17 publications

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