Open AccessDetection and segmentation of lymphomas in 3D PET images via clustering with entropy-based optimization strategy
Author(s) -
Haigen Hu,
Pierre Decazes,
Pierre Véra,
Hua Li,
Su Ruan
Publication year - 2019
Publication title -
international journal of computer assisted radiology and surgery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.701
H-Index - 49
eISSN - 1861-6429
pISSN - 1861-6410
DOI - 10.1007/s11548-019-02049-2
Subject(s) - computer science , cluster analysis , dbscan , artificial intelligence , segmentation , entropy (arrow of time) , pattern recognition (psychology) , voxel , image segmentation , data mining , fuzzy clustering , cure data clustering algorithm , physics , quantum mechanics
Lymphoma detection and segmentation from PET images are critical tasks for cancer staging and treatment monitoring. However, it is still a challenge owing to the complexities of lymphoma PET data themselves, and the huge computational burdens and memory requirements for 3D volume data. In this work, an entropy-based optimization strategy for clustering is proposed to detect and segment lymphomas in 3D PET images.
Accelerating Research
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom
Address
John Eccles HouseRobert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom