@inproceedings{215b7692f24f499d9ccb1ac310bbaa18,
title = "Thickness NETwork (ThickNet) features for the detection of prodromal AD",
abstract = "Regional analysis of cortical thickness has been studied extensively in building imaging biomarkers for early detection of Alzheimer's disease (AD), but not its inter-regional covariation. We present novel features based on the inter-regional co-variation of cortical thickness. Initially the cortical labels of each patient is partitioned into small patches (graph nodes) by spatial k-means clustering. A graph is then constructed by establishing a link between two nodes if the difference in thickness between the nodes is below a certain threshold. From this binary graph, thickness network (ThickNet) features are computed using nodal degree, betweenness and clustering coefficient measures. Fusing them with multiple kernel learning, we demonstrate their potential for the detection of prodromal AD.",
keywords = "alzheimer, fusion, mild cognitive impairment, network, thickness",
author = "Raamana, {Pradeep Reddy} and Lei Wang and Beg, {Mirza Faisal}",
year = "2013",
doi = "10.1007/978-3-319-02267-3_15",
language = "English (US)",
isbn = "9783319022666",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "114--122",
booktitle = "Machine Learning in Medical Imaging - 4th International Workshop, MLMI 2013, Held in Conjunction with MICCAI 2013, Proceedings",
note = "4th International Workshop on Machine Learning in Medical Imaging, MLMI 2013, Held in Conjunction with 16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013 ; Conference date: 22-09-2013 Through 22-09-2013",
}