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Analysis of dual tree M-band wavelet transform based features for brain image classification

Corresponding Author

Ratna Raju Ayalapogu

[email protected]

Mahatma Gandhi Institute of Technology, Hyderabad, Telangana, India

Correspondence Ratna Raju Ayalapogu, Mahatma Gandhi Institute of Technology, Hyderabad-500075, India. Email: [email protected]Search for more papers by this author

Suresh Pabboju,

Suresh Pabboju

Department of Information Technology, Chaitanya Bharathi Institute of Technology, Hyderabad, Telangana, India

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Rajeswara Rao Ramisetty,

Rajeswara Rao Ramisetty

Jawaharal Nehru Technological University, University College of Engineering Vizianagaram, Andhra Pradesh, India

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Ratna Raju Ayalapogu,

Corresponding Author

Ratna Raju Ayalapogu

[email protected]

Mahatma Gandhi Institute of Technology, Hyderabad, Telangana, India

Correspondence Ratna Raju Ayalapogu, Mahatma Gandhi Institute of Technology, Hyderabad-500075, India. Email: [email protected]Search for more papers by this author

Suresh Pabboju,

Suresh Pabboju

Department of Information Technology, Chaitanya Bharathi Institute of Technology, Hyderabad, Telangana, India

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Rajeswara Rao Ramisetty,

Rajeswara Rao Ramisetty

Jawaharal Nehru Technological University, University College of Engineering Vizianagaram, Andhra Pradesh, India

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First published: 29 April 2018

https://doi.org/10.1002/mrm.27210

Citations: 15

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Abstract

Purpose

The most complex organ in the human body is the brain. The unrestrained growth of cells in the brain is called a brain tumor. The cause of a brain tumor is still unknown and the survival rate is lower than other types of cancers. Hence, early detection is very important for proper treatment.

Methods

In this study, an efficient computer-aided diagnosis (CAD) system is presented for brain image classification by analyzing MRI of the brain. At first, the MRI brain images of normal and abnormal categories are modeled by using the statistical features of dual tree m-band wavelet transform (DTMBWT). A maximum margin classifier, support vector machine (SVM) is then used for the classification and validated with k-fold approach.

Results

Results show that the system provides promising results on a repository of molecular brain neoplasia data (REMBRANDT) with 97.5% accuracy using 4^th level statistical features of DTMBWT.

Conclusion

Viewing the experimental results, we conclude that the system gives a satisfactory performance for the brain image classification.

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Volume80, Issue6

December 2018

Pages 2393-2401

Analysis of dual tree M-band wavelet transform based features for brain image classification

Abstract

Purpose

Methods

Results

Conclusion

REFERENCES

Citing Literature

References

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Analysis of dual tree M-band wavelet transform based features for brain image classification

Abstract

Purpose

Methods

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Conclusion

REFERENCES

Citing Literature

References

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