TY - GEN
T1 - Combining outlier detection with random walker for automatic brain tumor segmentation
AU - Kanas, Vasileios G.
AU - Zacharaki, Evangelia I.
AU - Dermatas, Evangelos
AU - Bezerianos, Anastasios
AU - Sgarbas, Kyriakos
AU - Davatzikos, Christos
PY - 2012
Y1 - 2012
N2 - The diagnosis of brain neoplasms has been facilitated by the emerging of high-quality imaging techniques, such as Magnetic Resonance Imaging (MRI), while the combination of several sequences from conventional and advanced protocols has increased the diagnostic information. Treatment planning and therapy follow-up require the detection of neoplastic and edematous tissue boundaries, a very time consuming task when manually performed by medical experts based on the 3D MRI data. Automating the detection process is challenging, due to the high diversity in appearance of neoplastic tissue among different patients and, in many cases, similarity between neoplastic and normal tissue. In this paper, we propose an automatic brain tumor segmentation method based on a multilabel multiparametric random walks approach initialized by an outlier detection scheme. Segmentation assessment is performed by measuring spatial overlap between automatic segmentation and manual segmentation performed by medical experts. Good agreement is observed in most of the 26 cases for both neoplastic and edematous tissue. The highest achieved overlapping values were 0.74 and 0.79 for neoplastic and edematous tissue, respectively.
AB - The diagnosis of brain neoplasms has been facilitated by the emerging of high-quality imaging techniques, such as Magnetic Resonance Imaging (MRI), while the combination of several sequences from conventional and advanced protocols has increased the diagnostic information. Treatment planning and therapy follow-up require the detection of neoplastic and edematous tissue boundaries, a very time consuming task when manually performed by medical experts based on the 3D MRI data. Automating the detection process is challenging, due to the high diversity in appearance of neoplastic tissue among different patients and, in many cases, similarity between neoplastic and normal tissue. In this paper, we propose an automatic brain tumor segmentation method based on a multilabel multiparametric random walks approach initialized by an outlier detection scheme. Segmentation assessment is performed by measuring spatial overlap between automatic segmentation and manual segmentation performed by medical experts. Good agreement is observed in most of the 26 cases for both neoplastic and edematous tissue. The highest achieved overlapping values were 0.74 and 0.79 for neoplastic and edematous tissue, respectively.
KW - brain neoplasms
KW - k- means
KW - magnetic resonance imaging
KW - outlier detection
KW - random walks
KW - segmentation
UR - http://www.scopus.com/inward/record.url?scp=84870904188&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84870904188&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-33412-2_3
DO - 10.1007/978-3-642-33412-2_3
M3 - Conference contribution
AN - SCOPUS:84870904188
SN - 9783642334115
T3 - IFIP Advances in Information and Communication Technology
SP - 26
EP - 35
BT - Artificial Intelligence Applications and Innovations - AIAI 2012 International Workshops
PB - Springer New York LLC
T2 - 8th International Workshop on Artificial Intelligence Applications and Innovations, AIAI 2012: AIAB, AIeIA, CISE, COPA, IIVC, ISQL, MHDW, and WADTMB
Y2 - 27 September 2012 through 30 September 2012
ER -