Genetic Algorithms and Simulated Annealing
Genetic Algorithms and Simulated Annealing
Journal of Global Optimization
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art
Evolutionary Computation
Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters
IEEE Transactions on Computers
A unified tensor level set for image segmentation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on game theory
Segmenting images by combining selected atlases on manifold
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part III
Parallelized Evolutionary Learning for Detection of Biclusters in Gene Expression Data
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A Relay Level Set Method for Automatic Image Segmentation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
On the origin of the bilateral filter and ways to improve it
IEEE Transactions on Image Processing
Mean shift based gradient vector flow for image segmentation
Computer Vision and Image Understanding
Global structure constrained local shape prior estimation for medical image segmentation
Computer Vision and Image Understanding
| Hi-index | 0.01 |
Segmentation of medical images is an inevitable image processing step for computer-aided diagnosis. Due to complex acoustic inferences and artifacts, accurate extraction of breast lesions in ultrasound images remains a challenge. Although there have been many segmentation techniques proposed, the performance often varies with different image data, leading to poor adaptability in real applications. Intelligent computing techniques for adaptively learning the boundaries of image objects are preferred. This paper focuses on optimization of a previously documented method called robust graph-based (RGB) segmentation algorithm to extract breast tumors in ultrasound images more adaptively and accurately. A novel technique named as parameter-automatically optimized robust graph-based (PAORGB) image segmentation method is accordingly proposed and performed on breast ultrasound images. A particle swarm optimization algorithm is incorporated with the RGB method to achieve optimal or approximately optimal parameters. Experimental results have shown that the proposed technique can more accurately segment lesions from ultrasound images compared to the RGB and two conventional region-based methods.