Combining outlier detection with random walker for automatic brain tumor segmentation

Vasileios G. Kanas; Evangelia I. Zacharaki; Evangelos Dermatas; Anastasios Bezerianos; Kyriakos Sgarbas; Christos Davatzikos

doi:10.1007/978-3-642-33412-2_3

Combining outlier detection with random walker for automatic brain tumor segmentation

Vasileios G. Kanas, Evangelia I. Zacharaki, Evangelos Dermatas, Anastasios Bezerianos, Kyriakos Sgarbas, Christos Davatzikos

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

7 Scopus citations

Abstract

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.

Original language	English (US)
Title of host publication	Artificial Intelligence Applications and Innovations - AIAI 2012 International Workshops
Subtitle of host publication	AIAB, AIeIA, CISE, COPA, IIVC, ISQL, MHDW, and WADTMB, Proceedings
Publisher	Springer New York LLC
Pages	26-35
Number of pages	10
Edition	PART 2
ISBN (Print)	9783642334115
DOIs	https://doi.org/10.1007/978-3-642-33412-2_3
State	Published - 2012
Externally published	Yes
Event	8th International Workshop on Artificial Intelligence Applications and Innovations, AIAI 2012: AIAB, AIeIA, CISE, COPA, IIVC, ISQL, MHDW, and WADTMB - Halkidiki, Greece Duration: Sep 27 2012 → Sep 30 2012

Publication series

Name	IFIP Advances in Information and Communication Technology
Number	PART 2
Volume	382 AICT
ISSN (Print)	1868-4238

Other

Other	8th International Workshop on Artificial Intelligence Applications and Innovations, AIAI 2012: AIAB, AIeIA, CISE, COPA, IIVC, ISQL, MHDW, and WADTMB
Country/Territory	Greece
City	Halkidiki
Period	9/27/12 → 9/30/12

Keywords

brain neoplasms
k- means
magnetic resonance imaging
outlier detection
random walks
segmentation

ASJC Scopus subject areas

Information Systems and Management

Access to Document

10.1007/978-3-642-33412-2_3

Cite this

Kanas, V. G., Zacharaki, E. I., Dermatas, E., Bezerianos, A., Sgarbas, K., & Davatzikos, C. (2012). Combining outlier detection with random walker for automatic brain tumor segmentation. In Artificial Intelligence Applications and Innovations - AIAI 2012 International Workshops: AIAB, AIeIA, CISE, COPA, IIVC, ISQL, MHDW, and WADTMB, Proceedings (PART 2 ed., pp. 26-35). (IFIP Advances in Information and Communication Technology; Vol. 382 AICT, No. PART 2). Springer New York LLC. https://doi.org/10.1007/978-3-642-33412-2_3

Combining outlier detection with random walker for automatic brain tumor segmentation. / Kanas, Vasileios G.; Zacharaki, Evangelia I.; Dermatas, Evangelos et al.
Artificial Intelligence Applications and Innovations - AIAI 2012 International Workshops: AIAB, AIeIA, CISE, COPA, IIVC, ISQL, MHDW, and WADTMB, Proceedings. PART 2. ed. Springer New York LLC, 2012. p. 26-35 (IFIP Advances in Information and Communication Technology; Vol. 382 AICT, No. PART 2).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Kanas, VG, Zacharaki, EI, Dermatas, E, Bezerianos, A, Sgarbas, K & Davatzikos, C 2012, Combining outlier detection with random walker for automatic brain tumor segmentation. in Artificial Intelligence Applications and Innovations - AIAI 2012 International Workshops: AIAB, AIeIA, CISE, COPA, IIVC, ISQL, MHDW, and WADTMB, Proceedings. PART 2 edn, IFIP Advances in Information and Communication Technology, no. PART 2, vol. 382 AICT, Springer New York LLC, pp. 26-35, 8th International Workshop on Artificial Intelligence Applications and Innovations, AIAI 2012: AIAB, AIeIA, CISE, COPA, IIVC, ISQL, MHDW, and WADTMB, Halkidiki, Greece, 9/27/12. https://doi.org/10.1007/978-3-642-33412-2_3

Kanas VG, Zacharaki EI, Dermatas E, Bezerianos A, Sgarbas K, Davatzikos C. Combining outlier detection with random walker for automatic brain tumor segmentation. In Artificial Intelligence Applications and Innovations - AIAI 2012 International Workshops: AIAB, AIeIA, CISE, COPA, IIVC, ISQL, MHDW, and WADTMB, Proceedings. PART 2 ed. Springer New York LLC. 2012. p. 26-35. (IFIP Advances in Information and Communication Technology; PART 2). doi: 10.1007/978-3-642-33412-2_3

Kanas, Vasileios G. ; Zacharaki, Evangelia I. ; Dermatas, Evangelos et al. / Combining outlier detection with random walker for automatic brain tumor segmentation. Artificial Intelligence Applications and Innovations - AIAI 2012 International Workshops: AIAB, AIeIA, CISE, COPA, IIVC, ISQL, MHDW, and WADTMB, Proceedings. PART 2. ed. Springer New York LLC, 2012. pp. 26-35 (IFIP Advances in Information and Communication Technology; PART 2).

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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.

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Combining outlier detection with random walker for automatic brain tumor segmentation

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