Watershed algorithm for brain tumor segmentation based on morphological reconstruction and gradient layered modification.

The accurate segmentation of tumor in brain MRI images is usually difficult due to noise, gray inhomogeneity, fuzzy and discontinuous boundaries. For the purpose to get precise segmentation with less contour position bias, this paper presented a novel watershed algorithm based on morphological reconstruction and gradient layered modification. Firstly, it employed morphological hybrid opening and closing by reconstruction operators to smooth and denoise the original image, while retaining the target contour information. Then, it stratified the gradient image by the volume of three-dimension landform, further modified the lower gradient layers with large-sized structuring elements, whereas the smaller-sized to the higher layers. Thus it removed most local minimums caused by irregular details and noises, while region contour positions corresponded to the target area. Finally, it employed morphological watershed algorithm to implement segmentation on the basis of multi-scale modified image. The experimental results show that the suggested method can achieve more accurate segmentation result, owing to its lower over-segmentation and under-segmentation, as well as the higher similarity index compared with the standard segmentation. [ABSTRACT FROM AUTHOR]