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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/7794
Title: Segmentation, feature extraction, and multiclass brain tumor classification
Authors: Sachdeva J.
Kumar V.
Gupta, Indra
Khandelwal N.
Ahuja C.K.
Published in: Journal of Digital Imaging
Abstract: Multiclass brain tumor classification is performed by using a diversified dataset of 428 post-contrast T1-weighted MR images from 55 patients. These images are of primary brain tumors namely astrocytoma (AS), glioblastoma multiforme (GBM), childhood tumor-medulloblastoma (MED), meningioma (MEN), secondary tumor-metastatic (MET), and normal regions (NR). Eight hundred fifty-six regions of interest (SROIs) are extracted by a content-based active contour model. Two hundred eighteen intensity and texture features are extracted from these SROIs. In this study, principal component analysis (PCA) is used for reduction of dimensionality of the feature space. These six classes are then classified by artificial neural network (ANN). Hence, this approach is named as PCA-ANN approach. Three sets of experiments have been performed. In the first experiment, classification accuracy by ANN approach is performed. In the second experiment, PCA-ANN approach with random sub-sampling has been used in which the SROIs from the same patient may get repeated during testing. It is observed that the classification accuracy has increased from 77 to 91 %. PCA-ANN has delivered high accuracy for each class: AS - 90.74 %, GBM - 88.46 %, MED - 85 %, MEN - 90.70 %, MET - 96.67 %, and NR - 93.78 %. In the third experiment, to remove bias and to test the robustness of the proposed system, data is partitioned in a manner such that the SROIs from the same patient are not common for training and testing sets. In this case also, the proposed system has performed well by delivering an overall accuracy of 85.23 %. The individual class accuracy for each class is: AS - 86.15 %, GBM - 65.1 %, MED - 63.36 %, MEN - 91.5 %, MET - 65.21 %, and NR - 93.3 %. A computer-aided diagnostic system comprising of developed methods for segmentation, feature extraction, and classification of brain tumors can be beneficial to radiologists for precise localization, diagnosis, and interpretation of brain tumors on MR images. © 2013 Society for Imaging Informatics in Medicine.
Citation: Journal of Digital Imaging (2013), 26(6): 1141-1150
URI: https://doi.org/10.1007/s10278-013-9600-0
http://repository.iitr.ac.in/handle/123456789/7794
Issue Date: 2013
Keywords: Content-based active contour (CBAC)
Feature extraction
Multiclass brain tumor classification
Principal component analysis (PCA)
Segmented regions of interest (SROIs)
ISSN: 8971889
Author Scopus IDs: 54999212000
25646515800
56211916300
7006950739
23048455900
Author Affiliations: Sachdeva, J., Biomedical Engineering Lab, Department of Electrical Engineering, Indian Institute of Technology Roorkee, 247667 Roorkee, Uttrakhand, India
Kumar, V., Biomedical Engineering Lab, Department of Electrical Engineering, Indian Institute of Technology Roorkee, 247667 Roorkee, Uttrakhand, India
Gupta, I., Biomedical Engineering Lab, Department of Electrical Engineering, Indian Institute of Technology Roorkee, 247667 Roorkee, Uttrakhand, India
Khandelwal, N., Department of Radiodiagnosis, Post Graduate Institute of Medical Education and Research, Chandigarh, India
Ahuja, C.K., Department of Radiodiagnosis, Post Graduate Institute of Medical Education and Research, Chandigarh, India
Corresponding Author: Sachdeva, J.; Biomedical Engineering Lab, Department of Electrical Engineering, Indian Institute of Technology Roorkee, 247667 Roorkee, Uttrakhand, India; email: jainysachdeva@gmail.com
Appears in Collections:Journal Publications [EE]

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