A computer aided diagnosis ‘CAD’ for brain glioma exploration | IEEE Conference Publication | IEEE Xplore

A computer aided diagnosis ‘CAD’ for brain glioma exploration


Abstract:

Manual analysis of brain glioma tumor lacks accuracy and is time consuming. Thus, to avoid human error, this paper presents an automatic and accurate computer aided diagn...Show More

Abstract:

Manual analysis of brain glioma tumor lacks accuracy and is time consuming. Thus, to avoid human error, this paper presents an automatic and accurate computer aided diagnosis (CAD) system for brain glioma exploration on magnetic resonance imaging. A preprocessing approach was proposed to eliminate extra-cerebral features. Tumor and even its edema was automatically extracted by a fast distribution matching algorithm. Then, we realize a 3D reconstruction for brain tumor glioma and its edema in order to classify glioma tumor based on their radiologic appearance. As discussed with clinicians, the experimental results showed that the proposed computer aided diagnosis for brain glioma tumor achieves a good agreement. Thus, the proposed tool facilitates and speeds up the analysis of data and supports decision making.
Date of Conference: 17-19 March 2014
Date Added to IEEE Xplore: 16 June 2014
Electronic ISBN:978-1-4799-4888-8
Conference Location: Sousse

I. Introduction

Brain glioma tumor could be considered as among serious pathologies threatening people. An estimated 37,890 Americans were diagnosed with glioma since2011. Refering to the statistics published by the American society of clinical oncology, the five year survival rate for patients with glioma is 2.9% [1]. Hence, early detection and classification of this pathology becomes important. Magnetic resonance imaging is the main modality used to brain glioma diagnosis. We could notice the scarcity of CAD system dedicated to brain glioma tumor classification based on their radiological appearance. Based on their radiologic appearance, we can classify brain glioma tumor into for classes: non-enhanced, full-enhanced without edema, full-enhanced with edema and ring-enhanced tumors. Computer aided diagnosis (CAD) is then highly recommended since it would be able to detect and even identify brain glioma tumor at an earlier stage [2], [3] and classify them so as to decide correctly about adequate action to conduct. Moreover, this clinical tool offer clinicians the opportunity to process a large dataset in a reduced time in a clearer and accurate process. During our research, we focused on the four key steps: preprocessing step, segmentation step, 3D reconstruction and classification.

References

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