Statistical Approach for Brain Cancer Classification Using a Region Growing Threshold

Authors:
Bassam Al-Naami;Adnan Bashir;Hani Amasha;Jamal Al-Nabulsi;Abdul-Majeed Almalty
Affiliations:
Department of Biomedical Engineering, Hashemite University, Zarqa, Jordan 13115;Department of Industrial Engineering, Hashemite University, Zarqa, Jordan 13115;Department of Biomedical Engineering, Hashemite University, Zarqa, Jordan 13115;Department of Biomedical Engineering, Hashemite University, Zarqa, Jordan 13115;Physical Therapy Department, Hashemite University, Zarqa, Jordan 13133
Venue:
Journal of Medical Systems
Year:
2011

Citing 5
Cited 0

Digital Image Processing

Digital Image Processing
Threshold-based 3D Tumor Segmentation using Level Set (TSL)

WACV '07 Proceedings of the Eighth IEEE Workshop on Applications of Computer Vision
A Bayes-Based Region-Growing Algorithm for Medical Image Segmentation

Computing in Science and Engineering
Statistical Structure Analysis in MRI Brain Tumor Segmentation

ICIG '07 Proceedings of the Fourth International Conference on Image and Graphics
Automatic segmentation of non-enhancing brain tumors in magnetic resonance images

Artificial Intelligence in Medicine

Quantified Score

Hi-index	0.00

Visualization

Abstract

In brain cancer, a biopsy as an invasive procedure is needed in order to differentiate between malignant and benign brain tumor. However, in some cases, it is difficult or harmful to perform such a procedure, to the brain. The aim of this study is to investigate a new method in maximizing the probability of brain cancer type detection without actual biopsy procedure. The proposed method combines both image and statistical analysis for tumor type detection. It employed image filtration and segmentation of the target region of interest with MRI to assure an accurate statistical interpretation of the results. Statistical analysis was based on utilizing the mean, range, box plot, and testing of hypothesis techniques to reach acceptable and accurate results in differentiating between those two types. This method was performed, examined and compared on actual patients with brain tumors. The results showed that the proposed method was quite successful in distinguishing between malignant and benign brain tumor with 95% confident that the results are correct based on statistical testing of hypothesis.