Probabilistic branching node detection using AdaBoost and hybrid local features

Nuzhnaya, Tatyana; Barnathan, Michael; Ling, Haibin; Megalooikonomou, Vasileios; Bakić, Predrag R.; Maidment, Andrew D. A.

doi:10.1109/isbi.2010.5490375

Cited by 2 publications

(2 citation statements)

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“…In order to obtain the minimum cut, one should solve the energy function minimization problem. Finally, the segmentation of the images is computed using (2).…”

Section: Problem Formulationmentioning

confidence: 99%

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Segmentation of anatomical branching structures based on texture features and graph cut

Nuzhnaya

Cheng

Ling

et al. 2011

2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro

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Segmentation of tree-like structure within medical imaging modalities, such as x-ray, MRI, ultrasound, etc., is an important step for analyzing branching patterns involved in many anatomic structures. However, images acquired using these different acquisition techniques frequently have features of poor contrast, blurring and noise, and therefore the segmentation result of traditional image segmentation methods may not be satisfactory. In this paper, we propose a framework for accurate segmentation of the ductal network in x-ray galactograms. Our approach is based on the graph cut algorithm and texture analysis to extract features of skewness, coarseness, contrast, energy and fractal dimension. The features are chosen to capture not only architectural variability of the enhanced ductal tree, but also spatial variations among pixels. The proposed approach was applied to a dataset of 20 galactographic images. We performed receiver operating characteristic (ROC) curve analysis to assess the accuracy. The area under the ROC curve observed was 0.76, indicating that our approach may potentially assist clinicians in the interpretation of breast images and facilitate the investigation of relationships among structure and texture of the branching patterns.

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“…In order to obtain the minimum cut, one should solve the energy function minimization problem. Finally, the segmentation of the images is computed using (2).…”

Section: Problem Formulationmentioning

confidence: 99%

“…In [10], Adaboost learning has been applied on features for classification of lung bronchovascular anatomy. In [2] the authors have used support vector machines (SVM) as a probabilistic inference framework for branching node detection, which is an important step towards complete branching pattern segmentation.…”

Section: Introductionmentioning

confidence: 99%