An Improved Multithreshold Segmentation Algorithm Based On Graph Cuts Applicable For Irregular Image

An Improved Multithreshold Segmentation Algorithm Based on Graph Cuts Applicable for Irregular Image

In order to realize themultithreshold segmentation of images, an improved segmentation algorithm based on graph cut theory using artificial bee colony is proposed. A newweight function based on gray level and the location of pixels is constructed in this paper to calculate the probability that each pixel belongs to the same region. On this basis, a new cost function is reconstructed that can use both square and nonsquare images.Then the optimal threshold of the image is obtained through searching for theminimum value of the cost function using artificial bee colony algorithm. In this paper, public dataset for segmentation and widely used images were measured separately. Experimental results show that the algorithm proposed in this paper can achieve larger Information Entropy (IE), higher Peak Signal to Noise Ratio (PSNR), higher Structural Similarity Index (SSIM), smaller Root Mean Squared Error (RMSE), and shorter time than other image segmentation algorithms.

Advanced Prognostic Predictive Modelling in Healthcare Data Analytics

This book discusses major technical advancements and research findings in the field of prognostic modelling in healthcare image and data analysis. The use of prognostic modelling as predictive models to solve complex problems of data mining and analysis in health care is the feature of this book. The book examines the recent technologies and studies that reached the practical level and becoming available in preclinical and clinical practices in computational intelligence. The main areas of interest covered in this book are highest quality, original work that contributes to the basic science of processing, analysing and utilizing all aspects of advanced computational prognostic modelling in healthcare image and data analysis.

An Improved Image Segmentation by Graph Cuts