Computer Science and Information Systems 2024 Volume 21, Issue 1, Pages: 379-393
https://doi.org/10.2298/CSIS230128045K
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Echo state network for features extraction and segmentation of tomography images
Koprinkova-Hristova Petia (Institute of Information and Communication technologies, Bulgarian Academy of Sciences Acad., Sofia, Bulgaria), petia.koprinkova@iict.bas.bg
Georgiev Ivan (Institute of Information and Communication technologies, Bulgarian Academy of Sciences Acad., Sofia, Bulgaria + Institute of Mathematics and Informatics, Bulgarian Academy of Sciences Acad. Sofia, Bulgaria), ivan.georgiev@parallel.bas.bg
Raykovska Miryana (Institute of Information and Communication technologies, Bulgarian Academy of Sciences Acad., Sofia, Bulgaria), miriana.raykovska@iict.bas.bg
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
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