Multiscale superpixel classification for tumor segmentation in breast ultrasound images
| Autor: | Yeong Kyeong Seong, Kuanhong Xu, Haibing Ren, Jiyeun Kim, Zhihui Hao, Qiang Wang |
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| Rok vydání: | 2012 |
| Předmět: |
Conditional random field
Contextual image classification medicine.diagnostic_test Segmentation-based object categorization Computer science business.industry ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Scale-space segmentation Pattern recognition Image segmentation Data set ComputingMethodologies_PATTERNRECOGNITION medicine Computer vision Segmentation Artificial intelligence business Breast ultrasound |
| Zdroj: | ICIP |
| DOI: | 10.1109/icip.2012.6467485 |
| Popis: | Tumor localization and segmentation in breast ultrasound (BUS) images is an important as well as intractable problem for computer-aided diagnosis (CAD) due to the high variation in shape and appearance. We propose a novel algorithm in this paper without making any assumption on tumor, compared to most previous works. Heterogeneous features are collected via a hierarchical over-segmentation framework, which we have shown has the multiscale property. The superpixels are then classified with their confidences nested into the bottom layer. The ultimate segmentation is made by using an efficient conditional random field model. Experiments on challenging data set show that our algorithm is able to handle almost all kinds of benign and malignant tumors, and also confirm the superiority of our work through a comparison with other two different approaches. |
| Databáze: | OpenAIRE |
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