Reliability of Segmenting Brain Tumor and Finding Optimal Volume Estimator for MR Images of Patients with Glioma’s
T. Kalaiselvi1, P. Kumarashankar2, P. Sriramakrishnan3

1Kalaiselvi T., Assistant Professor, Department of Computer Science and Applications, Gandhigram Rural Institute – Deemed University, Dindigul (Tamil Nadu), India.
2Kumarashankar P. Project Manager,  IT MNC. He received his Bachelor of Science (Computer Science) Madurai Kamaraj University, Madurai, (Tamil Nadu) India
3Sriramakrishnan P. Department of Computer Science and Applications, Gandhigram Rural Institute – Deemed University, Dindigul,(Tamil Nadu), India.

Manuscript received on 30 June 2019 | Revised Manuscript received on 05 July 2019 | Manuscript published on 30 July 2019 | PP: 1647-1653 | Volume-8 Issue-9, July 2019 | Retrieval Number: I8509078919/19©BEIESP | DOI: 10.35940/ijitee.I8509.078919
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Abstract: Tumor volume estimation is a significant prognostic part of the Glioma tumor detection. Reliable assessment of Glioma tumor segmentation and volume estimation is a common problem in clinical aspects. We aim to propose a tumor segmentation method by suggesting suitable estimator for MR brain tumor volume construction. Run length algorithm is used to automatic initialize the seed point to the region growing algorithm. Region growing algorithm works with a threshold value using 8 × 8 patches. In this experiment includes thirty BraTS2013 high-grade and low-grade Glioma datasets. Proposed method yield 80.12% of Dice similarity with 6.8% of deviation and 84% of accuracy with 10% of deviation. The proposed work uses six state-of-the-art volume detectors to estimate the size of tumor volume. From the results, Cavalieri’s estimator gives more accurate results with less deviation.
Index Terms Volume estimators, Rectangular estimator, Cavalieri’s Estimator, Trapezoidal Estimator, Parabolic Estimator, Truncated Pyramid, Simson’s Estimators

Scope of the Article: Predictive Analysis