1. A topo-graph model for indistinct target boundary definition from anatomical images
- Author
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Guanzhong Gong, Yong Yin, Hui Cui, Michael J. Fulham, Stefan Eberl, Jianlong Zhou, Xiuying Wang, Lisheng Wang, and Dagan Feng
- Subjects
Lung Neoplasms ,Geodesic ,Computer science ,TOPOGRAPHIC REGIONS ,Contrast Media ,Health Informatics ,02 engineering and technology ,Sensitivity and Specificity ,Graph model ,030218 nuclear medicine & medical imaging ,03 medical and health sciences ,0302 clinical medicine ,Carcinoma, Non-Small-Cell Lung ,Abdomen ,Image Processing, Computer-Assisted ,0202 electrical engineering, electronic engineering, information engineering ,Humans ,Cluster Analysis ,Segmentation ,Breast ,Ultrasonography ,Models, Statistical ,Pixel ,business.industry ,Reproducibility of Results ,Pattern recognition ,Graph ,Computer Science Applications ,Hausdorff distance ,Liver ,Graph (abstract data type) ,Female ,020201 artificial intelligence & image processing ,Artificial intelligence ,Tomography, X-Ray Computed ,business ,Medical Informatics ,Algorithms ,Software - Abstract
© 2018 Elsevier B.V. Background and Objective: It can be challenging to delineate the target object in anatomical imaging when the object boundaries are difficult to discern due to the low contrast or overlapping intensity distributions from adjacent tissues. Methods: We propose a topo-graph model to address this issue. The first step is to extract a topographic representation that reflects multiple levels of topographic information in an input image. We then define two types of node connections - nesting branches (NBs) and geodesic edges (GEs). NBs connect nodes corresponding to initial topographic regions and GEs link the nodes at a detailed level. The weights for NBs are defined to measure the similarity of regional appearance, and weights for GEs are defined with geodesic and local constraints. NBs contribute to the separation of topographic regions and the GEs assist the delineation of uncertain boundaries. Final segmentation is achieved by calculating the relevance of the unlabeled nodes to the labels by the optimization of a graph-based energy function. We test our model on 47 low contrast CT studies of patients with non-small cell lung cancer (NSCLC), 10 contrast-enhanced CT liver cases and 50 breast and abdominal ultrasound images. The validation criteria are the Dice's similarity coefficient and the Hausdorff distance. Results: Student's t-test show that our model outperformed the graph models with pixel-only, pixel and regional, neighboring and radial connections (p-values
- Published
- 2018
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