| Investigating machine learning techniques for MRI-based classification of brain neoplasms. | |
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MedLine Citation:
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PMID: 21516321 Owner: NLM Status: Publisher |
Abstract/OtherAbstract:
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PURPOSE: Diagnosis and characterization of brain neoplasms appears of utmost importance for therapeutic management. The emerging of imaging techniques, such as Magnetic Resonance (MR) imaging, gives insight into pathology, while the combination of several sequences from conventional and advanced protocols (such as perfusion imaging) increases the diagnostic information. To optimally combine the multiple sources and summarize the information into a distinctive set of variables however remains difficult. The purpose of this study is to investigate machine learning algorithms that automatically identify the relevant attributes and are optimal for brain tumor differentiation. METHODS: Different machine learning techniques are studied for brain tumor classification based on attributes extracted from conventional and perfusion MRI. The attributes, calculated from neoplastic, necrotic, and edematous regions of interest, include shape and intensity characteristics. Attributes subset selection is performed aiming to remove redundant attributes using two filtering methods and a wrapper approach, in combination with three different search algorithms (Best First, Greedy Stepwise and Scatter). The classification frameworks are implemented using the WEKA software. RESULTS: The highest average classification accuracy assessed by leave-one-out (LOO) cross-validation on 101 brain neoplasms was achieved using the wrapper evaluator in combination with the Best First search algorithm and the KNN classifier and reached 96.9% when discriminating metastases from gliomas and 94.5% when discriminating high-grade from low-grade neoplasms. CONCLUSIONS: A computer-assisted classification framework is developed and used for differential diagnosis of brain neoplasms based on MRI. The framework can achieve higher accuracy than most reported studies using MRI. |
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Authors:
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Evangelia I Zacharaki; Vasileios G Kanas; Christos Davatzikos |
Publication Detail:
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Type: JOURNAL ARTICLE Date: 2011-4-23 |
Journal Detail:
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Title: International journal of computer assisted radiology and surgery Volume: - ISSN: 1861-6429 ISO Abbreviation: - Publication Date: 2011 Apr |
Date Detail:
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Created Date: 2011-4-25 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 101499225 Medline TA: Int J Comput Assist Radiol Surg Country: - |
Other Details:
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Languages: ENG Pagination: - Citation Subset: - |
Affiliation:
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Department of Computer Engineering & Informatics, University of Patras, Patras, Greece, ezachar@upatras.gr. |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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