Brain tissue segmentation using q-entropy in multiple sclerosis magnetic resonance images
The loss of brain volume has been used as a marker of tissue destruction and can be used as an index of the progression of neurodegenerative diseases, such as multiple sclerosis. In the present study, we tested a new method for tissue segmentation based on pixel intensity threshold using generalized...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Associação Brasileira de Divulgação Científica
2010-01-01
|
| Series: | Brazilian Journal of Medical and Biological Research |
| Subjects: | |
| Online Access: | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-879X2010000100011 |
| _version_ | 1818979682283945984 |
|---|---|
| author | P.R.B. Diniz L.O. Murta-Junior D.G. Brum D.B. de Araújo A.C. Santos |
| author_facet | P.R.B. Diniz L.O. Murta-Junior D.G. Brum D.B. de Araújo A.C. Santos |
| author_sort | P.R.B. Diniz |
| collection | DOAJ |
| description | The loss of brain volume has been used as a marker of tissue destruction and can be used as an index of the progression of neurodegenerative diseases, such as multiple sclerosis. In the present study, we tested a new method for tissue segmentation based on pixel intensity threshold using generalized Tsallis entropy to determine a statistical segmentation parameter for each single class of brain tissue. We compared the performance of this method using a range of different q parameters and found a different optimal q parameter for white matter, gray matter, and cerebrospinal fluid. Our results support the conclusion that the differences in structural correlations and scale invariant similarities present in each tissue class can be accessed by generalized Tsallis entropy, obtaining the intensity limits for these tissue class separations. In order to test this method, we used it for analysis of brain magnetic resonance images of 43 patients and 10 healthy controls matched for gender and age. The values found for the entropic q index were 0.2 for cerebrospinal fluid, 0.1 for white matter and 1.5 for gray matter. With this algorithm, we could detect an annual loss of 0.98% for the patients, in agreement with literature data. Thus, we can conclude that the entropy of Tsallis adds advantages to the process of automatic target segmentation of tissue classes, which had not been demonstrated previously. |
| first_indexed | 2024-12-20T17:03:25Z |
| format | Article |
| id | doaj.art-015459f4596e4acb858fb7e1fb777ffe |
| institution | Directory Open Access Journal |
| issn | 0100-879X 1414-431X |
| language | English |
| last_indexed | 2024-12-20T17:03:25Z |
| publishDate | 2010-01-01 |
| publisher | Associação Brasileira de Divulgação Científica |
| record_format | Article |
| series | Brazilian Journal of Medical and Biological Research |
| spelling | doaj.art-015459f4596e4acb858fb7e1fb777ffe2022-12-21T19:32:25ZengAssociação Brasileira de Divulgação CientíficaBrazilian Journal of Medical and Biological Research0100-879X1414-431X2010-01-014317784Brain tissue segmentation using q-entropy in multiple sclerosis magnetic resonance imagesP.R.B. DinizL.O. Murta-JuniorD.G. BrumD.B. de AraújoA.C. SantosThe loss of brain volume has been used as a marker of tissue destruction and can be used as an index of the progression of neurodegenerative diseases, such as multiple sclerosis. In the present study, we tested a new method for tissue segmentation based on pixel intensity threshold using generalized Tsallis entropy to determine a statistical segmentation parameter for each single class of brain tissue. We compared the performance of this method using a range of different q parameters and found a different optimal q parameter for white matter, gray matter, and cerebrospinal fluid. Our results support the conclusion that the differences in structural correlations and scale invariant similarities present in each tissue class can be accessed by generalized Tsallis entropy, obtaining the intensity limits for these tissue class separations. In order to test this method, we used it for analysis of brain magnetic resonance images of 43 patients and 10 healthy controls matched for gender and age. The values found for the entropic q index were 0.2 for cerebrospinal fluid, 0.1 for white matter and 1.5 for gray matter. With this algorithm, we could detect an annual loss of 0.98% for the patients, in agreement with literature data. Thus, we can conclude that the entropy of Tsallis adds advantages to the process of automatic target segmentation of tissue classes, which had not been demonstrated previously.http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-879X2010000100011Multiple sclerosisAutomatic segmentationMagnetic resonance imageVolumetryTsallis entropy |
| spellingShingle | P.R.B. Diniz L.O. Murta-Junior D.G. Brum D.B. de Araújo A.C. Santos Brain tissue segmentation using q-entropy in multiple sclerosis magnetic resonance images Brazilian Journal of Medical and Biological Research Multiple sclerosis Automatic segmentation Magnetic resonance image Volumetry Tsallis entropy |
| title | Brain tissue segmentation using q-entropy in multiple sclerosis magnetic resonance images |
| title_full | Brain tissue segmentation using q-entropy in multiple sclerosis magnetic resonance images |
| title_fullStr | Brain tissue segmentation using q-entropy in multiple sclerosis magnetic resonance images |
| title_full_unstemmed | Brain tissue segmentation using q-entropy in multiple sclerosis magnetic resonance images |
| title_short | Brain tissue segmentation using q-entropy in multiple sclerosis magnetic resonance images |
| title_sort | brain tissue segmentation using q entropy in multiple sclerosis magnetic resonance images |
| topic | Multiple sclerosis Automatic segmentation Magnetic resonance image Volumetry Tsallis entropy |
| url | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-879X2010000100011 |
| work_keys_str_mv | AT prbdiniz braintissuesegmentationusingqentropyinmultiplesclerosismagneticresonanceimages AT lomurtajunior braintissuesegmentationusingqentropyinmultiplesclerosismagneticresonanceimages AT dgbrum braintissuesegmentationusingqentropyinmultiplesclerosismagneticresonanceimages AT dbdearaujo braintissuesegmentationusingqentropyinmultiplesclerosismagneticresonanceimages AT acsantos braintissuesegmentationusingqentropyinmultiplesclerosismagneticresonanceimages |