Structured patch model for a unified automatic and interactive segmentation framework
- Authors
- Park, Sang Hyun; Lee, Soochahn; Yun, Il Dong; Lee, Sang Uk
- Issue Date
- Aug-2015
- Publisher
- Elsevier BV
- Keywords
- Structured patch model; Interactive segmentation; Adaptive prior; Markov random field; Incremental learning
- Citation
- Medical Image Analysis, v.24, no.1, pp 297 - 312
- Pages
- 16
- Journal Title
- Medical Image Analysis
- Volume
- 24
- Number
- 1
- Start Page
- 297
- End Page
- 312
- URI
- https://scholarworks.bwise.kr/sch/handle/2021.sw.sch/10445
- DOI
- 10.1016/j.media.2015.01.003
- ISSN
- 1361-8415
1361-8423
- Abstract
- We present a novel interactive segmentation framework incorporating a priori knowledge learned from training data. The knowledge is learned as a structured patch model (StPM) comprising sets of corresponding local patch priors and their pairwise spatial distribution statistics which represent the local shape and appearance along its boundary and the global shape structure, respectively. When successive user annotations are given, the StPM is appropriately adjusted in the target image and used together with the annotations to guide the segmentation. The StPM reduces the dependency on the placement and quantity of user annotations with little increase in complexity since the time-consuming StPM construction is performed offline. Furthermore, a seamless learning system can be established by directly adding the patch priors and the pairwise statistics of segmentation results to the StPM. The proposed method was evaluated on three datasets, respectively, of 20 chest CT, 3D knee MR, and 3D brain MR. The experimental results demonstrate that within an equal amount of time, the proposed interactive segmentation framework outperforms recent state-of-the-art methods in terms of accuracy, while it requires significantly less computing and editing time to obtain results with comparable accuracy. (C) 2015 Elsevier B.V. All rights reserved.
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Collections - College of Engineering > Department of Electronic Engineering > 1. Journal Articles
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