Many methods for liver tumor Computed Tomography (CT) segmentation have the difficulty to achieve accurate tumor due to inhomogeneous gray and fuzzy edges. To obtain precise segmentation result, a method using multi-scale morphology was proposed to eliminate local minima. Firstly, the morphological area operation was used to remove image's small burrs and irregular edges so as to avoid boundaries migration. Secondly, local minima in gradient image were distinguished by the combined knowledge of statistic characteristics and morphological properties including depth and scale. After partition, the function relationship was established between multi-scale structure elements and local minima. In order to filter noise via large-size structure elements and preserving major object via small-size structure elements, a morphological method called close operation was then employed to adaptively modify the image.Finally, standard watershed transform was utilized to implement segmentation of liver tumor. The experimental results show that this method can reduce over-segmentation effectively and liver tumor can be segmented accurately while boundaries of objects are located precisely.

Tumor in brain Magnetic Resonance Imaging (MRI) images is often difficult to be segmented accurately due to noise, gray inhomogeneity, complex structrue, fuzzy and discontinuous boundaries. For the purpose of getting precise segmentation with less position bias, a new method based on Fuzzy C-Means (FCM) clustering and morphological multi-scale modification was proposed. Firstly, a control parameter was introduced to distinguish noise points, edge points and regional interior points in neighborhood, and the function relationship between pixels and the sizes of structure elements was established by combining with spatial information. Then, different pixels were modified with different-sized structure elements using morphological closing operation. Thus most local minimums caused by irregular details and noises were removed, while region contours positions corresponding to the target area were largely unchanged. Finally, FCM clustering algorithm was employed to implement segmentation on the basis of multi-scale modified image, which avoids the local optimization, misclassification and region contours position bias, while remaining accurate positioning of contour area. Compared with the standard FCM, Kernel FCM (KFCM), Genetic FCM (GFCM), Fuzzy Local Information C-Means (FLICM) and expert hand sketch, 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 higher similarity index compared with the standard segmentation.

As many methods of brain tumor Magnetic Resonance Imaging (MRI) segmentation are usually driven by such conditions as noise, intensity inhomogeneity within tumor, fuzzy and discontinuous boundaries, it is difficult to segment tumor accurately. To improve the segmentation results, morphological multiscale modification of controlled marker was proposed. Firstly, this method was based on morphological gradient images because the adaptive structure elements were utilized on different pixels in different areas. In addition, modifying gradient image was key to avoid a larger misregistration of target boundaries. Finally, marker-controlled watershed was applied to segment brain tumor. The experimental results show that the method of brain tumors has more accurate segmentation results. Key words：brain tumor; morphological multi-scale modification; watershed transform