title: Statistical approach for brain cancer classification using a region growing threshold.
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.
authors: Department of Biomedical Engineering, Hashemite University, Zarqa, Jordan. b.naami@hu.edu.jo
authors: Bassam
authors: B
authors: Al-Naami
authors: Adnan
authors: A
authors: Bashir
authors: Hani
authors: H
authors: Amasha
authors: Jamal
authors: J
authors: Al-Nabulsi
authors: Abdul-Majeed
authors: AM
authors: Almalty
mesh_terms: D001932:Brain Neoplasms / Q000175:diagnosis* / Q000473:pathology*; D003627:Data Interpretation, Statistical; D006801:Humans; D007089:Image Enhancement; D008279:Magnetic Resonance Imaging; D015233:Models, Statistical; D012680:Sensitivity and Specificity