Brain Tumor Detection: MATLAB & Machine Learning Algorithms

Brain tumor segmentation is the process of separating the tumor from normal brain tissues; in clinical routine, it provides useful information for diagnosis and treatment planning. However, it's still a challenging task thanks to the irregular form and confusing boundaries of tumors. Tumor cells thermally represent a heat source; their temperature is high compared to normal brain cells. The most aim of this paper is to demonstrate that thermal information of brain tumors is often wont to reduce false positive and false negative results of segmentation performed in MRI images. The obtained results in all patients showed a significant improvement using the proposed method compared to segmentation by a level set method with an average of 0.8% of the tumor area and 11 2.48% of healthy tissue was differentiated using thermal images only. We conclude that tumor contours delineation based on tumor temperature changes can be exploited to reinforce and enhance segmentation algorithms in MRI diagnostics.Feature extractioncoordinate-based features:the extraction of features based on the registration to a standard coordinate system, potentially including coordinates features, spatial prior probabilities for structures or tissue types in the coordinate system, and local measures of anatomic variability within the coordinate system;registration-based features:the extraction of features based on known properties of the one or more aligned templates, potentially including features based on labeled regions in the template, image-based features at corresponding locations in the template, features derived from the warping field, and features derived from the use of the template's known line of symmetry.ClassificationMachine learning algorithms are used for the classification of MR brain images either as normal or abnormal. The major aim of ml algorithms is to automatically learn and make intelligent decisions the classification is done based on the below features: (a) feature processing: before classification, the extracted feature set can be refined to make it more appropriate for achieving high classification accuracies: (b) classifier training: pixels that are labeled as normal and abnormal are used with the extracted features to automatically learn a classification model that predicts labels based on the features; (c) pixel classification: the learned classification model can then be used to predict the labels for pixels with unassigned labels, based on their extracted features; (d) relaxation: since the learned classification model may be noisy, a relaxation of the classification results which takes into account dependencies in the labels (i.e. Classification) of neighboring pixels can be used to refine the classification predictions and yield a final segmentation