A Dualistic Sub-Image Histogram Equalization Based Enhancement and Segmentation Techniques for Medical Images

Image segmentation plays a vital role in many medical imaging applications by automatically locating the regions of interest. Image segmentation is the most crucial functions in image analysis and processing. Also segmentation results affect all the subsequent processes of image analysis. Manual segmentation of medical image by the radiologist is not only a tedious and time consuming process but also not very accurate. Hence it is necessary to develop medical image segmentation algorithms that are accurate and efficient. In this work, we propose a dualistic sub-image histogram equalization based enhancement and segmentation techniques. The proposed method has been tested and evaluated on several medical images. In this work, the medical image is lineated and extracted out so that it can be viewed individually. The results demonstrate that the proposed algorithm is highly efficient over hierarchical grouping technique. This is validated using the performance measures such as completeness and clearness.