NDLI: A hierarchical decision tree classification scheme for brain tumour astrocytoma grading using support vector machines

Content Provider	IEEE Xplore Digital Library
Author	Glotsos, D. Spyridonos, P. Petalas, P. Cavouras, D. Zolota, V. Dadioti, P. Lekka, I. Nikiforidis, G.
Copyright Year	2003
Description	Author affiliation: Comput. Lab., Patras Univ., Greece (Glotsos, D.; Spyridonos, P.; Petalas, P.)
Abstract	The use of the concepts of support vector machines (SVMs) and decision tree (DT) classification as a possible methodology for the characterization of the degree of malignancy of brain tumours astrocytomas (ASTs) is proposed in this paper. A two-level hierarchical DT model was constructed for the discrimination of 87 ASTs in accordance to the WHO grading system. The first level concerned the detection of low versus high-grade tumours and the second level the detection of less aggressive as opposed to highly aggressive tumours. The decision rule at each level was based on a SVM classification methodology comprising 3 steps: i) From each biopsy, images were digitized and segmented to isolate nuclei from surrounding tissue. ii) Descriptive quantitative variables related to chromatin distribution and DNA content were generated to encode the degree of tumour malignancy. iii) Exhaustive search was performed to determine best feature combination that led to the smallest classification error. SVM classifier training was based on the leave-one-out method. Finally, SVMs were comparatively evaluated with the Bayesian classifier and the probabilistic neural network. The SVM classifier discriminated low from high-grade tumours with an accuracy of 90.8% and less from highly aggressive tumours with 85.6%. The proposed decision tree classification scheme based on SVMs and the analysis of quantitative nuclear features provide means to reduce subjectivity in grading brain tumors.
Sponsorship	EURASIP - European Association for Signal Processing IEEE Signal Processing Soc
Starting Page	1034
Ending Page	1038
File Size	319180
Page Count	5
File Format	PDF
ISBN	953184061X
DOI	10.1109/ISPA.2003.1296442
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-09-18
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Decision trees Classification tree analysis Tumors Support vector machines Support vector machine classification Biopsy Image segmentation DNA Bayesian methods Biological neural networks
Content Type	Text
Resource Type	Article

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