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Real-time classification of brain tumors in MRI images with a convolutional operator-based hidden Markov model

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Abstract

Classification of brain tumors based on the brain magnetic resonance imaging (MRI) results of patients has become an important problem in medical image processing. A computer program that can efficiently analyze brain MRI images of patients in real time and generate accurate classification results of the tumors in these images can significantly reduce the amount of time needed for diagnosis, which may increase the chances for patients to survive. This paper proposes a new statistical method that can accurately classify three types of brain tumors based on MRI images, the three types of tumors considered include pituitary tumor, glioma, and meningioma. The features for a pixel in an MRI image are obtained by applying a set of convolutional operators to the neighborhood area of the pixel. For training, a hidden Markov model (HMM) is constructed and trained from a training dataset by computing a statistical profile for the feature vectors for pixels in the tumor regions of each type of brain tumors. In addition, a statistical profile is also obtained for pixels that are in the background of a tumor. For classification, the trained HMM is used to assign labels to pixels in an MRI image with a dynamic programming approach and the classification result of the image is obtained from the labels assigned to the tumor region. Both the training and classification processes can be efficiently performed in linear time and does not require the availability of a large amount of computational resources. Experimental results on a large dataset of MRI images show that the proposed method can provide classification results with high accuracy for all three types of brain tumors. A comparison with state-of-the-art methods for brain tumor classification suggests that the proposed method can achieve improved classification accuracy. In addition, real-time analysis also reveals that the proposed approach can probably be used for real-time classification of brain tumors.

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Acknowledgements

G. Li, J, Sun and Y. Song’s work is fully supported by the fund of Specially Appointed Professors of Jiangsu Province, China with the grant number: 1034901701.

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Authors and Affiliations

  1. School of Electronics and Information Science, Jiangsu University of Science and Technology, Zhenjiang, 212003, China

    Guoliang Li, Jinhong Sun, Yinglei Song & Zhiyu Zhu

  2. Department of Computer Science and Information Technology, Clayton State University, Morrow, GA, 30260, USA

    Junfeng Qu

  3. Computer Engineering Department, Persian Gulf University, Bushehr, Iran

    Mohammad R. Khosravi

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  1. Guoliang Li
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  2. Jinhong Sun
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  3. Yinglei Song
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Correspondence to Yinglei Song.

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Li, G., Sun, J., Song, Y. et al. Real-time classification of brain tumors in MRI images with a convolutional operator-based hidden Markov model. J Real-Time Image Proc 18, 1207–1219 (2021). https://doi.org/10.1007/s11554-021-01072-4

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