ARCHIVES
Research Article
Machine Learning Approaches for Brain Disease Diagnosis: Principles and Recent Advances
Dr.A.S.Shanthi1
R.Kokila2
D. Ananthi3
G. Indhumathi4
G. Shobika Sree Malya5
S R.Vahini6
123456 Department of Computer Science And Engineering, Tamilnadu College of Engineering, Tamilnadu, India.
Published Online: September-October 2022
Pages: 57-60
Cite this article
No DOIReferences
1. Liu J, Udupa JK, Odhner D, Hackney D, Moonis G (2005) A system for brain tumor volume estimation via MR imaging and fuzzy
connectedness. Comput Med Imaging Graphics 29(1):21–34
2. Sled JG, Zijdenbos AP, Evans AC (1998) A nonparametric method for automatic correction of intensity nonuniformity in MRI data.
IEEE Trans Med Imaging 17(1):87–97
3. Belaroussi B, Milles J, Carme S, Zhu YM, Benoit-Cattin H (2006) Intensity non-uniformity correction in MRI: existing methods and
their validation. Med Image Anal 10(2):234
4. Madabhushi A, Udupa JK (2005) Interplay between intensity standardization and inhomogeneity correction in MR image processing.
IEEE Trans Med Imaging 24(5):561– 57
5. Prastawa M, Bullitt E, Ho S, Gerig G (2004) A brain tumor segmentation framework based on outlier detection. Med Image Anal
8(3):275–283
6. PhillipsW, Velthuizen R, Phuphanich S, Hall L, Clarke L, SilbigerM(1995) Application of fuzzy c-means segmentation technique for
tissue differentiation inMR images of a hemorrhagic glioblastoma multiforme. Magn Reson Imaging 13(2):277–290
7. Clark MC, Hall LO, Goldgof DB, Velthuizen R, Murtagh FR, Silbiger MS (1998) Automatic tumor segmentation using knowledge
based techniques. IEEE Trans Med Imaging 17(2):187–201
8. Fletcher-Heath LM, Hall LO, Goldg of DB, Murtagh FR (2001) Automatic segmentation of non-enhancing brain tumors in magnetic
resonance images. ArtifIntell Med 21(1–3):43– 63
9. Warfield SK, Kaus M, Jolesz FA, Kikinis R (2000) Adaptive, template moderated, spatially varying statistical classification. Med
Image Anal 4(1):43–55
10. KausMR,Warfield SK, Nabavi A, Black PM, Jolesz FA, Kikinis R (2001) Automated segmentation of MR images of Brain Tumors1.
Radiology 218(2):586–591
11. Guillemaud R, Brady M (1997) Estimating the bias field of MR images. IEEE Trans Med Imaging 16(3):238–251
12. Corso JJ, Sharon E, Dube S, El-Saden S, Sinha U, Yuille A (2008) Efficient multilevel brain tumor segmentation with integrated
bayesian model classification. IEEE Trans Med Imaging 27(5):629–640
13. . Zhou J, Chan K, Chong V, Krishnan S (2006) Extraction of brain tumor from MR images using one-class support vector machine.
In: 27th annual international conference of the engineering in medicine and biology society, 2005. IEEE-EMBS 2005. pp 6411–6414
14. Corso J, Yuille A, Sicotte N, Toga A (2007) Detection and segmentation of pathological structures by the extended graph-shifts
algorithm. In: Medical Image Computing and ComputerAssisted Intervention—MICCAI. pp 985–993
15. Schapire RE, Freund Y, Bartlett P, Lee WS (1998) Boosting the margin: a new explanation for the effectiveness of voting methods.
Ann Stat 26(5):1651–1686
connectedness. Comput Med Imaging Graphics 29(1):21–34
2. Sled JG, Zijdenbos AP, Evans AC (1998) A nonparametric method for automatic correction of intensity nonuniformity in MRI data.
IEEE Trans Med Imaging 17(1):87–97
3. Belaroussi B, Milles J, Carme S, Zhu YM, Benoit-Cattin H (2006) Intensity non-uniformity correction in MRI: existing methods and
their validation. Med Image Anal 10(2):234
4. Madabhushi A, Udupa JK (2005) Interplay between intensity standardization and inhomogeneity correction in MR image processing.
IEEE Trans Med Imaging 24(5):561– 57
5. Prastawa M, Bullitt E, Ho S, Gerig G (2004) A brain tumor segmentation framework based on outlier detection. Med Image Anal
8(3):275–283
6. PhillipsW, Velthuizen R, Phuphanich S, Hall L, Clarke L, SilbigerM(1995) Application of fuzzy c-means segmentation technique for
tissue differentiation inMR images of a hemorrhagic glioblastoma multiforme. Magn Reson Imaging 13(2):277–290
7. Clark MC, Hall LO, Goldgof DB, Velthuizen R, Murtagh FR, Silbiger MS (1998) Automatic tumor segmentation using knowledge
based techniques. IEEE Trans Med Imaging 17(2):187–201
8. Fletcher-Heath LM, Hall LO, Goldg of DB, Murtagh FR (2001) Automatic segmentation of non-enhancing brain tumors in magnetic
resonance images. ArtifIntell Med 21(1–3):43– 63
9. Warfield SK, Kaus M, Jolesz FA, Kikinis R (2000) Adaptive, template moderated, spatially varying statistical classification. Med
Image Anal 4(1):43–55
10. KausMR,Warfield SK, Nabavi A, Black PM, Jolesz FA, Kikinis R (2001) Automated segmentation of MR images of Brain Tumors1.
Radiology 218(2):586–591
11. Guillemaud R, Brady M (1997) Estimating the bias field of MR images. IEEE Trans Med Imaging 16(3):238–251
12. Corso JJ, Sharon E, Dube S, El-Saden S, Sinha U, Yuille A (2008) Efficient multilevel brain tumor segmentation with integrated
bayesian model classification. IEEE Trans Med Imaging 27(5):629–640
13. . Zhou J, Chan K, Chong V, Krishnan S (2006) Extraction of brain tumor from MR images using one-class support vector machine.
In: 27th annual international conference of the engineering in medicine and biology society, 2005. IEEE-EMBS 2005. pp 6411–6414
14. Corso J, Yuille A, Sicotte N, Toga A (2007) Detection and segmentation of pathological structures by the extended graph-shifts
algorithm. In: Medical Image Computing and ComputerAssisted Intervention—MICCAI. pp 985–993
15. Schapire RE, Freund Y, Bartlett P, Lee WS (1998) Boosting the margin: a new explanation for the effectiveness of voting methods.
Ann Stat 26(5):1651–1686
Related Articles
2022
A Review on Bamboo Reinforced Concrete Beam
2022
FARMERS AGRICULTURAL PORTAL
2022
Sentiment Analysis of Religious Tweets
2022
Enhancement of beam strength by using bamboo as reinforcement in place of steel bars
2022
A Review on Anomaly Detection using PYOD Package
2022
