## 1 Introduction
\begin{table}
\begin{tabular}{l l l l l} \hline
**Authors** & **Technique** & **Type** & **Image sequences** & **\# of tumors** \\ \hline [PERSON] _et al._[16] & Semi-automated fuzzy & GBM & T1w, T1w+c, Flair & 5 \\  & clustering & & & \\ [PERSON] _et al._[4] & Fuzzy clustering & GBM & PD,T2w, T1w+c & 1 \\ [PERSON] _et al._[5] & Fuzzy clustering and & GBM & PD,T2w, T1w+c & 7 \\  & knowledge-based analysis & & & \\ Fletcher-Heath & Fuzzy clustering and & Brain & PD,T2w, T1w with & 4 \\ _et al._[6] & knowledge-based analysis & tumor & no contrast & \\ [PERSON] _et al._[7] & Learn distribution of normal & Brain & T2w, T1w (with or & 3 \\  & tissues/outlier detection as & tumors & without contrast) & \\  & tumors & & & \\ [PERSON] _et al._[8] & Adaptive template -moderate & LGG/M & T1w+c, sagittal & 20 \\  & technique with atlas prior & G & view & \\ [PERSON] _et al._[11] & Conditional random field and & Brain & T1w, T1w+c, T2w & 7 \\  & support vector machine & tumors & & \\ [PERSON] _et al._[9] & 3D level set & GBM & T1w+c, T1w, T2w & 3 \\ [PERSON] _et al._[10] & k-nearest neighbor & MS and & PD, T2w, T1w, & 9 \\  & & MG & magnetization & \\  & & transfer & \\ [PERSON] \& [PERSON] _et al._[12] & Hopfield neural network & Brain & NA & 2 \\  & tumors & & & \\ [PERSON] _et al._[13] & Support vector machine & NC & T1w, T1w+c & 9 \\ [PERSON] _et al._[15] & SWA-segmentation by & GBM & T2w, T1w, T1w+c, & 20 \\  & weighted aggregation. & & Flair & \\ [PERSON] _et al._[17] & SWA with texture features & GBM & T1w+c & NA \\ [PERSON] _et al._[14] & Spatial accuracy-weighted & Gliomas & High:T1w, T1w+c & 15 \\  & hidden Markov field and EM & & Low:T2w, Flair & \\  & to solve the problem of high & & & \\  & and low resolution problem & & & \\ \hline \end{tabular}
\end{table} 
Table 1: Summary of related methods in brain tumor segmentation. The type abbreviations are NC: Nasopharyngeal carcinoma; MNG: Meningiomas; MG - malignant gliomas; MS – multiple sclerosis. change on T1w images is the only imaging biomarker that is accepted by the FDA as a surrogate endpoint of clinical outcome after chemotherapy and radiotherapy for phase III trials [19]. Diffusion MRI has been explored as early detection of human GBM brain tumor treatment response early therapeutic responses before the tumor size changes. Table 2 presents a review of the recent studies that used DWI for GBM early prediction of treatment response. [PERSON] et al reported ADC value increase significantly in effective therapeutic intervention in pre-clinical studies and presented two patients to support this hypothesis in a preliminary clinical study [2, 20]. [PERSON] _et al_. [21] applied both low and high b-value and used mean ADC and diffusion index for treatment response evaluation. [PERSON] et al calculated voxel-by-voxel tumor ADC value changes over time and displayed it as a functional diffusion map for correlation with clinical response [22, 23]. They reported that the number of voxels with increased ADC is related to treatment efficacy. Our previous work [24] showed promising results for using ADC histogram analysis, and we explored a more sophisticated classifier and designed experiments to show the advantages of the two-component histogram modeling. Machine learning and statistical pattern recognition have great contributions to the biomedical community because they can improve the sensitivity and/or specificity of detection and diagnosis of disease, while at the same time increasing objectivity of the decision-making process [26]. The need for machine learning is perhaps greater than ever given the dramatic increase in medical data being collected, new detection, and diagnostic modalities being developed as well as the complexity of the data types and importance of multimodal analysis. In all of these cases, machine learning can provide new tools for interpreting the high-dimensional and complex datasets with which the clinician is confronted [26]. In our study, we explored three different classification methods: AdaBoost, random forest, and support vector machine.