Geodesic
Echo State Network for Features Extraction and Segmentation of Tomography Images
Computer Science and Information Systems, Tome 21 (2024) no. 1.

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The paper proposes a novel approach for gray scale images segmentation. It is based on multiple features extraction from a single feature per image pixel, namely its intensity value, via a recurrent neural network from the reservoir computing family - Echo state network. The preliminary tests on the benchmark gray scale image Lena demonstrated that the newly extracted features - reservoir equilibrium states - reveal hidden image characteristics. In present work the developed approach was applied to a real life task for segmentation of a 3D tomography image of a of bone whose aim was to explore the object’s internal structure. The achieved results demonstrated the novel approach allows for clearer revealing the details of the bone internal structure thus supporting further tomography image analyses.
Keywords: reservoir computing, Echo state network, intrinsic plasticity, gray image, segmentation, tomography 
@article{CSIS_2024_21_1_a21,
     author = {Petia Koprinkova-Hristova and Ivan Georgiev and Miryana Raykovska},
     title = {Echo {State} {Network} for {Features} {Extraction} and {Segmentation} of {Tomography} {Images}},
     journal = {Computer Science and Information Systems},
     publisher = {mathdoc},
     volume = {21},
     number = {1},
     year = {2024},
     url = {http://geodesic.mathdoc.fr/item/CSIS_2024_21_1_a21/}
}
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Petia Koprinkova-Hristova; Ivan Georgiev; Miryana Raykovska. Echo State Network for Features Extraction and Segmentation of Tomography Images. Computer Science and Information Systems, Tome 21 (2024) no. 1. http://geodesic.mathdoc.fr/item/CSIS_2024_21_1_a21/