Qualitative Analysis of Image Segmentation Using Watershed Transform

Watershed transform has interesting properties that make it useful for much different image segmentation analysis, it has important drawbacks (Over segmentation, sensitivity to noise, poor detection of thin or low signal to noise ratio structures). Recent advances in medical imaging technology using multiple Computed tomographies (CT) provide volumetric datasets with spatial resolution segmentation solution tend to have difficulty in accurately delineating region subtle variation along their boundaries or delineating region which are spatially close. image segmentation algorithm introduces an original modification to the classical watershed transform is used to differentiate between kinds of tissues based on their density.WT uses set probability distribution to model likelihood a given pixels is measured obtained from the provided schematic classes it is simple and intuitive, can be parallelized and various image where tested in bone fracture. Such as negative image, superimposed watershed and watershed-marker controlled.