A Tumor Segmentation using Improved K-mean Clustering with Morphological Algorithm

Tumor detection is an important work in the medical image processing. In the last decade researcher are focused on tumor detection in early age of development. In this research paper proposed a new improved method to deals with the implementation of simple algorithm for detection of range and shape of different types of tumor images. Tumor is an uncontrolled growth of tissues in any part of the body. Many techniques have been developed for the detection of tumor using the abnormal lesion size and shape. However this method of detection resists the accurate determination of stage and size of tumor. To avoid such type of problems, this research uses computer aided method for segmentation (detection) of tumor based on the combination of two algorithms K-means algorithm for with five cluster method and morphological operation for proper area detection. In addition, it also reduces the time for analysis. The detection of the malignant tumor is somewhat difficult to mass tumor. The developing platform for the detection is MATLAB. Because it is easy to develop and execute. At the end, we are providing systems that detect the tumor and its shape and area. The final outcome of proposed method calculate the area of tumor and also the complexity level of tumor image.