Automated Tumor Segmentation Based on Hidden Markov Classifier using Singular Value Decomposition Feature Extraction in Brain MR images

ntroduction: Diagnosing brain tumor is not always easy for doctors, and existence of an assistant that                                                      facilitates the interpretation process is an asset in the clinic. Computer vision techniques are devised to aid the clinic in detecting tumors based on a database of tumor contained images. These precise techniques open up the way for recognizing a tumor even in the early stages of cancer development depicted as very low contrast object in an image. To this end, at first a set of similarity measures are calculated for each pixel in the image database. Then, this dataset is fed into a classifier that defines the mathematical rules necessary for making decision about a new image. After defining the rules, a model is formed which is ready to make decisions for new images outside the database. This decision is accompanied with an accuracy which is calculated in the end as the classifier performance measures. Materials and Methods: In this study, Singular Value Decomposition – SVD – method is used as a tool to extract similarity measures for each malignant image in the database. These measures form a matrix with which the classifier is trained. The classifier in this study, is a model based on Hidden Markov Random Field – HMRF – idea, that utilizes Bayes’ rule and maximum a posteriori criterion to obtain labels for each pixel and making decisions about he new images respectively. After sorting the new image as either malignant or not, the malignant image goes through a wavelet process for tumor segmentation. In this section, the image is transformed into a multilevel wavelet structure. Image segmentation is done using direct and inverse wavelet transform. Results: The classification performance is done quantitatively and qualitatively by calculating Volume Overlap Ratio (VOR), Recognition Rate (RR), True Negative Rate (TNR), True Positive Rate (TPR), Accuracy (ACC), Sensitivity and Specificity parameters, and 5 output images respectively. Also, a qualitative comparison is made between MRF, HMRF and morphological image segmentation methods. Conclusion: The application of HMRF-Wavelet pattern recognition has been investigated in this work. VOR, RR, TNR, TPR, ACC, Sensitivity and Specificity are calculated, and most values have been reported as over 95% for five test images. Despite the complexity of the HMRF statistical modeling, acceptable performance measure values indicate the sufficiency of this algorithm.