Joint-ViVo: Selecting and Weighting Visual Words Jointly for Bag-of-Features based Tissue Classification in Medical Images
This work addresses tissue classification for medical imaging applications, but it is incremental as it builds on existing bag-of-features methods by integrating vocabulary learning and weighting.
The paper tackles the problem of tissue classification in medical images by proposing Joint-ViVo, a novel algorithm that jointly learns visual vocabulary and weighting in bag-of-features methods, showing effective performance on tasks like brain tissue identification in MRI, lung tissue classification in HRCT, and breast tissue density classification in mammograms.
Automatically classifying the tissues types of Region of Interest (ROI) in medical imaging has been an important application in Computer-Aided Diagnosis (CAD), such as classification of breast parenchymal tissue in the mammogram, classify lung disease patterns in High-Resolution Computed Tomography (HRCT) etc. Recently, bag-of-features method has shown its power in this field, treating each ROI as a set of local features. In this paper, we investigate using the bag-of-features strategy to classify the tissue types in medical imaging applications. Two important issues are considered here: the visual vocabulary learning and weighting. Although there are already plenty of algorithms to deal with them, all of them treat them independently, namely, the vocabulary learned first and then the histogram weighted. Inspired by Auto-Context who learns the features and classifier jointly, we try to develop a novel algorithm that learns the vocabulary and weights jointly. The new algorithm, called Joint-ViVo, works in an iterative way. In each iteration, we first learn the weights for each visual word by maximizing the margin of ROI triplets, and then select the most discriminate visual words based on the learned weights for the next iteration. We test our algorithm on three tissue classification tasks: identifying brain tissue type in magnetic resonance imaging (MRI), classifying lung tissue in HRCT images, and classifying breast tissue density in mammograms. The results show that Joint-ViVo can perform effectively for classifying tissues.