Comparison of Hybrid Contrast Algorithm for Medical Images with Edge Enhancement and Segmentation

Abstract

A hybrid image enhancement algorithm based on Seeded Region Growing with existing contrast enhancement technique is proposed. Image adjustment, Histogram equalization and Adaptive Histogram Equalization are applied for the various medical images are prescribed in this paper. Generally in medical images contrast is low, whereas the detail features are poor and enhancing these images by the existing contrast enhancement cannot give the required output image due to various factors. The Seeded Region algorithm is implemented by considering the neighborhood and threshold function, then the grouping is built between the pixels which satisfy the criteria and the detail scales can be enhanced, by the existing contrast techniques. Finally, according to the enhanced image, the image metric parameters are measured and same is implemented on different medical images such as X-ray. CT, MRI images. By Comparing with other image enhancement algorithms, quality parameters show that the proposed method can improve the global image effectively and eliminates the visible artifacts of medical images particularly, Region based Adaptive Histogram equalization reveals better result. Therefore, the Medical image becomes easy to analyze and obtain a perceptual image is acquired for the image feature recognizing and matching in this work. Further, due to various grading of medical imaging, it is difficult to analyze diagnosis for inherited complex problems; To solve the problem, edge enhancing and segmentation of the image is included in this work, which results in edge pattern and segmentation of image is very ease for diagnostic analysis.