Hybrid Algorithm Edge Detected DICOM Image Enhancement and Analysis based on Genetic Algorithm for Evolution and Best Fit Value - S, Chetan; Sheshadri, H S; Lokesha, V (British Journal of Healthcare and Medical Research, 2017)
The segmentation of a DICOM standard medical image is a necessary technique which is essential for feature extraction, object edge detection and classification of the segments of the image. The DICOM image is partitioned based on the Hybrid ACO-CPM algorithm, based on the edges in the image, for analysis. The edges are seen as the boundaries within the image which differentiates different regions in the image. The factors that links to the boundary discontinuities that co-exists between the pixels of DICOM image, like texture, intensity and gradient are rendered redundant and are taken care with the application of the Hybrid ACO-CPM algorithm. DICOM image features correspond to that of meta-heuristic characteristics, which are considered during the application of Hybrid ACO-CPM algorithm. The results obtained from this non-deterministic behavior needs to be optimized over a large space called as the search space, wherein the lists of all possible solutions are provided. Each solution is to be marked as a value fit to be termed problematic and needs to be synthesized for an optimized solution. Among various techniques that provide solutions in obtaining an equitable optimization solution, Genetic Algorithms (GA) corroborates as one of the persuasive techniques in a large search space. In this paper we propose an efficient and effective workflow based on a methodology, that provides an overview of the image enhancement and object classification for a DICOM image using Genetic Algorithm (GA). The edge detected medical standard DICOM image obtained from the Hybrid ACO-CPM algorithm is modified with respect to critical edge data. With the application of GA methodology, the process of enhancing the image ultimately suffices by rendering an image suitable for a specific application with an improved visual quality of the segmented image. A Figure-of-Merit is constructed to differentiate between the image metrics and their best fit values obtained for the images with respect to the Ant Colony Optimization (ACO) algorithm and proposed Hybrid ACO-CPM algorithm, upon enhancing the images using GA