Segmentation of anatomical branching structures based on texture features and conditional random field

Tatyana Nuzhnaya; Predrag Bakic; Despina Kontos; Vasileios Megalooikonomou; Haibin Ling

doi:10.1117/12.911112

14 February 2012 Segmentation of anatomical branching structures based on texture features and conditional random field

Tatyana Nuzhnaya, Predrag Bakic, Despina Kontos, Vasileios Megalooikonomou, Haibin Ling

Proceedings Volume 8314, Medical Imaging 2012: Image Processing; 83141J (2012) https://doi.org/10.1117/12.911112
Event: SPIE Medical Imaging, 2012, San Diego, California, United States

Abstract

This work is a part of our ongoing study aimed at understanding a relation between the topology of anatomical branching structures with the underlying image texture. Morphological variability of the breast ductal network is associated with subsequent development of abnormalities in patients with nipple discharge such as papilloma, breast cancer and atypia. In this work, we investigate complex dependence among ductal components to perform segmentation, the first step for analyzing topology of ductal lobes. Our automated framework is based on incorporating a conditional random field with texture descriptors of skewness, coarseness, contrast, energy and fractal dimension. These features are selected to capture the architectural variability of the enhanced ducts by encoding spatial variations between pixel patches in galactographic image. The segmentation algorithm was applied to a dataset of 20 x-ray galactograms obtained at the Hospital of the University of Pennsylvania. We compared the performance of the proposed approach with fully and semi automated segmentation algorithms based on neural network classification, fuzzy-connectedness, vesselness filter and graph cuts. Global consistency error and confusion matrix analysis were used as accuracy measurements. For the proposed approach, the true positive rate was higher and the false negative rate was significantly lower compared to other fully automated methods. This indicates that segmentation based on CRF incorporated with texture descriptors has potential to efficiently support the analysis of complex topology of the ducts and aid in development of realistic breast anatomy phantoms.

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Tatyana Nuzhnaya, Predrag Bakic, Despina Kontos, Vasileios Megalooikonomou, and Haibin Ling "Segmentation of anatomical branching structures based on texture features and conditional random field", Proc. SPIE 8314, Medical Imaging 2012: Image Processing, 83141J (14 February 2012); https://doi.org/10.1117/12.911112

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KEYWORDS

Image segmentation

Breast

Image enhancement

Medical imaging

Image processing algorithms and systems

Neural networks

X-rays

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