ICTACT Journal on Image and Video Processing

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Automated Brain Tumor Segmentation in MR Images Using a Hidden Markov Classifier Framework Trained by Svd-Derived Features


Affiliations
1 Department of Medical Radiation Engineering, Shiraz University, Iran, Islamic Republic of
2 Radiation Research Center, Shiraz University, Shiraz, Iran, Islamic Republic of
     

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Interpreting brain MR images are becoming automated, to such extent that in some cases “all” the diagnostic procedure is done by computers. Therefore, diagnosing the patients is done by a comparably higher accuracy. Computer models that have been trained by a priori knowledge act as the decision makers. They make decisions about each new image, based on the training data fed to them previously. In case of cancerous images, the model picks that image up, and isolates the malignant tissue in the image as neatly as possible. In this paper we have developed an unsupervised learning system for automatic tumor segmentation and detection that can be applied to low contrast images.

Keywords

Image Segmentation, Hidden Markov Model, Singular Value decomposition, Wavelet Analysis.
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  • Automated Brain Tumor Segmentation in MR Images Using a Hidden Markov Classifier Framework Trained by Svd-Derived Features

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Authors

Fazel Mirzaei
Department of Medical Radiation Engineering, Shiraz University, Iran, Islamic Republic of
Mohammad Reza Parishan
Department of Medical Radiation Engineering, Shiraz University, Iran, Islamic Republic of
Mohammadjavad Faridafshin
Department of Medical Radiation Engineering, Shiraz University, Iran, Islamic Republic of
Reza Faghihi
Radiation Research Center, Shiraz University, Shiraz, Iran, Islamic Republic of
Sedigheh Sina
Radiation Research Center, Shiraz University, Shiraz, Iran, Islamic Republic of

Abstract


Interpreting brain MR images are becoming automated, to such extent that in some cases “all” the diagnostic procedure is done by computers. Therefore, diagnosing the patients is done by a comparably higher accuracy. Computer models that have been trained by a priori knowledge act as the decision makers. They make decisions about each new image, based on the training data fed to them previously. In case of cancerous images, the model picks that image up, and isolates the malignant tissue in the image as neatly as possible. In this paper we have developed an unsupervised learning system for automatic tumor segmentation and detection that can be applied to low contrast images.

Keywords


Image Segmentation, Hidden Markov Model, Singular Value decomposition, Wavelet Analysis.

References