Multi-class regularization parameter learning for graph cut image segmentation
Sema Candemir,Kannappan Palaniappan,Yusuf Sinan Akgul +2 more
- 07 Apr 2013
- pp 1473-1476
TL;DR: This paper proposes a λ estimation system which is modeled as a multi-class classification scheme and claims that λ can be learned by local features which hold the regional characteristics of the image.
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Abstract: One of the first steps of computer-aided systems is robustly detect the anatomical boundaries. Literature has several successful energy minimization based algorithms which are applied to medical images. However, these algorithms depend on parameters which need to be tuned for a meaningful solution. One of the important parameters is the regularization parameter (λ) which is generally estimated in an ad-hoc manner and is used for the whole data set. In this paper we claim that λ can be learned by local features which hold the regional characteristics of the image. We propose a λ estimation system which is modeled as a multi-class classification scheme. We demonstrate the performance of the approach within graph cut segmentation framework via qualitative results on chest X-rays. Experimental results indicate that predicted parameters produce better segmentation results.
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Citations
Lung Segmentation in Chest Radiographs Using Anatomical Atlases With Nonrigid Registration
Sema Candemir,Stefan Jaeger,Kannappan Palaniappan,Jonathan P. Musco,R. Singh,Zhiyun Xue,Alexandros Karargyris,Sameer Antani,George R. Thoma,Clement J. McDonald +9 more
TL;DR: A nonrigid registration-driven robust lung segmentation method using image retrieval-based patient specific adaptive lung models that detects lung boundaries, surpassing state-of-the-art performance is presented.
Automatic Tuberculosis Screening Using Chest Radiographs
Stefan Jaeger,Alexandros Karargyris,Sema Candemir,Les R. Folio,Jenifer Siegelman,Fiona M. Callaghan,Zhiyun Xue,Kannappan Palaniappan,R. Singh,Sameer Antani,George R. Thoma,Yi-Xiang J. Wang,Pu-Xuan Lu,Clement J. McDonald +13 more
TL;DR: The proposed computer-aided diagnostic system for TB screening, which is ready for field deployment, achieves a performance that approaches the performance of human experts.
•Posted Content
Fully Convolutional Architectures for Multi-Class Segmentation in Chest Radiographs
TL;DR: It is demonstrated that the architecture combining delayed subsampling, exponential linear units, highly restrictive regularization, and a large number of high-resolution low-level abstract features outperforms state-of-the-art methods on all considered organs, as well as the human observer on lungs and heart.
143
Segmentation and classification on chest radiography: a systematic survey
Taruna Agrawal,Prakash Choudhary +1 more
TL;DR: In this article , the authors focused on the research conducted using chest X-rays for the lung segmentation and detection/classification of pulmonary disorders on publicly available datasets and included the studies performed using the Generative Adversarial Network (GAN) models.
Automatic tuberculosis screening using canny Edge detection method
R Ramya,P Srinivasa Babu +1 more
- 01 Feb 2015
TL;DR: John has found that, the requirements for the application of edge detection on diverse vision systems are relatively the same, Thus, a development of edge Detection solution to address these requirements can be implemented in a wide range of situations.
20
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