Segmentation and precise volume estimation of abnormalities is one of the main focus in medical image processing field for the purpose of diagnosis and treatment planning. The precise estimation of volume of the abnormality aids better prognosis, treatment planning and dose estimation. The work put forth in this paper has proposed and implemented a semi-automatic technique that yields appropriate segmented regions from MR brain images. The Segmentation technique here utilizes fusion of information beyond human perception from MR images to develop a fused feature map. The information beyond human perception include second order derivatives that are computed from an image which are discussed in detail in relevant section of this paper. This obtained feature map acts as a stopping function for the initialized curve in the framework of an active contour model to obtain a well segmented region of interest. The segmentation is carried out in all the slices of a particular dataset with initialization of the active contour required only on the first slice which makes this method fast. The obtained segmentation results are compared with ground truth segmentation results obtained from experts manually using Jackard’s Co-efficient of Similarity and Overlap index. The boundaries of the segmented regions are utilized in surveyor’s algorithm to compute the volume of the tumors with high accuracy. The efficacy of this volume estimation technique is illustrated with comparison to mostly used ABC/2 method and cavalieri method. The results obtained on various case studies like Craniophryngioma, High grade Glioma and Microadenoma show a good efficacy of the overall method.
['Shoaib Amin Banday', 'Ajaz Hussain Mir']
Statistical textural feature and deformable model based brain tumor segmentation and volume estimation