Dr. Sokratis Makrogiannis Profile

Dr. Sokratis Makrogiannis

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Publications (8)

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

Proc. SPIE. 11313, Medical Imaging 2020: Image Processing

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

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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

Proc. SPIE. 10950, Medical Imaging 2019: Computer-Aided Diagnosis

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

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SPIE Journal Paper | 6 September 2016

Imaging and tracking HIV viruses in human cervical mucus

Fatima Boukari, Sokratis Makrogiannis, Ralph Nossal, Hacene Boukari

JBO Vol. 21 Issue 09

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

Proc. SPIE. 9112, Sensing Technologies for Global Health, Military Medicine, and Environmental Monitoring IV

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

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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

Proc. SPIE. 7624, Medical Imaging 2010: Computer-Aided Diagnosis

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

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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

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

Proc. SPIE. 5909, Applications of Digital Image Processing XXVIII

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

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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

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 8 publications

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