An Optimisation for the Level-Set Method for by Genetic Algorithm using Prostate Gland Images


  • Kiran Ingale, Pratibha Shingare


Now a day good quality medical images are required for an accurate diagnosis. For the diagnosis of a particular disease, the field of medical image processing more optimizes results. Diverse methods are furnished to carry out these attributes. Many methods of segmentation of images were developed out of which segmentation with level set function is often used. It involves the execution of energy functions for segmentation but involves complex mathematical calculations. The genetic algorithm optimizes the segmentation process by using a generated training database. For optimization of the level set function, we used a genetic algorithm.  Here we assess suggested segmentation methods on a set of transrectal ultrasound and magnetic resonance prostate images. This research makes a comparison of three segmentation algorithms and assesses their performance parameters like specificity, sensitivity, accuracy. It is observed that the suggested Genetic algorithm with a level set function gives optimize results in contrast with the remaining two methods.