Publication
Automatic Classification and Segmentation of Low-Grade Gliomas in Magnetic Resonance Imaging
| Summary: | In this article a new methodology is proposed to tackle the problem of automatic segmentation of low-grade gliomas. The possibility of knowing the limits of this type of tumor is crucial for effectively characterizing the neoplasm, enabling, in certain cases, to obtain useful information about how to treat the patient in a more effective way. Using a database of magnetic resonance images, containing several occurrences of this type of tumors, and through a carefully designed image processing pipeline, the purpose of this work is to accurately locate, isolate and thus facilitate the classification of the pathology. The proposed methodology, described in detail, was able to achieve an accuracy of 87.5% for a binary classification task. The quality of the identified regions had an accuracy of 81.6%. These are promising results that may point the effectiveness of the approach. The low contrast of the images, as a result of the acquisition process, and the detection of very small tumors are still challenges that bring motivation to further pursue additional results. |
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| Main Authors: | Barbosa, Marta |
| Other Authors: | Moreira, Pedro; Ribeiro, Rogério; Coelho, Luis |
| Subject: | Low-grade glioma Image segmentation MRI |
| Year: | 2020 |
| Country: | Portugal |
| Document type: | article |
| Access type: | restricted access |
| Associated institution: | Instituto Politécnico do Porto |
| Language: | English |
| Origin: | Repositório Científico do Instituto Politécnico do Porto |
| Summary: | In this article a new methodology is proposed to tackle the problem of automatic segmentation of low-grade gliomas. The possibility of knowing the limits of this type of tumor is crucial for effectively characterizing the neoplasm, enabling, in certain cases, to obtain useful information about how to treat the patient in a more effective way. Using a database of magnetic resonance images, containing several occurrences of this type of tumors, and through a carefully designed image processing pipeline, the purpose of this work is to accurately locate, isolate and thus facilitate the classification of the pathology. The proposed methodology, described in detail, was able to achieve an accuracy of 87.5% for a binary classification task. The quality of the identified regions had an accuracy of 81.6%. These are promising results that may point the effectiveness of the approach. The low contrast of the images, as a result of the acquisition process, and the detection of very small tumors are still challenges that bring motivation to further pursue additional results. |
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