Abstract
Image segmentation is a method of delineating prominent structures in an input image for post processing such as texture based analysis, feature extraction, selection, and classification. Various medical applications rely on efficient image segmentation algorithms for disease diagnosis and management. In this work, autonomous segmentation of brain regions of Magnetic Resonance Images (MRI) is attempted for neuroimaging applications. It is carried out by combining heuristic based image segmentation with stochastic modeling. Initially, Particle Swarm Optimization (PSO) algorithm is used to identify ideal intensity thresholds. Labeling priors from PSO are derived for the Markov random field. Markov Random Field (MRF), a probabilistic approach is used to improve the partitioning of the initial segmented image obtained by the multilevel threshold technique. The efficacy of the segmentation procedure is improved by refining the obtained apriori information by Maximum a posteriori (MAP) estimation of the MRFMAP model. By this approach, the spatial information is incorporated into the segmentation process using MRF. A new metaheuristic MRF image segmentation technique is proposed here to take advantage of both the methods. Performance assessment of the proposed method is carried out using a numerical metric that evaluates the silhouette index of the estimated clusters. Experiments conducted on different MRI datasets show the proposed methodology produces an average improvement in cluster classification of 4.42% in terms of silhouette index for clinical datasets.
Keywords: Image segmentation, markov process, markov random field, particle swarm optimization, otsu threshold.
Graphical Abstract
Call for Papers in Thematic Issues
Intelligent Information Retrieval for Multispectral and Hyperspectral Imaging
Multispectral and hyperspectral imaging is an interdisciplinary research area, widely focuses on spectral, spatial, and temporal data. It offers a plethora of opportunities to efficiently analyze the vast area of the earth's surface. However, there are numerous factors such as dimensionality and size of the hyperspectral data, lesser training samples, ...read more
Machine and Deep Learning in Medical Image Diagnosis
Medical Image Processing regards a set of methodologies that have been developed over recent years with the purpose of improving medical image quality, improving medical data visualization, understanding, and assisting medical diagnosis, and so on. The application of machine and deep learning methods in sensing and imaging can potentially have ...read more
Quantum Machine Learning for Medical Data and Imaging
Quantum computing has promised a significant speedup in certain computationally intensive tasks that are intractable on classical computers. Researchers in quantum machine learning, such as those working in computer vision, image processing, biomedical analysis, and related topics, may play an important role in comprehending and working on complicated medical data, ...read more
46
6
1
1