We present a new method for automatic detection of the lumbar vertebrae and disk structure from MR images.
In clinical settings, radiologists utilize several images of the lumbar structure for diagnosis of lumbar disorders.
These images are co-registered by technicians and represent orthogonal features of the lumbar region. We
combine information from T1W sagittal, T2W sagittal and T2W axial MR images to automatically label disks
and vertebral columns. The method couples geometric and tissue property information available from the three
types of images with image analysis approaches to achieve 98.8% accuracy for the disk labeling task on a test
set of 67 images containing 335 disks.
The design and performance of a system for spotting handwritten Arabic words in scanned document images is presented. Three main components of the system are a word segmenter, a shape based matcher for words and a search interface. The user types in a query in English within a search window, the system finds the equivalent Arabic word, e.g., by dictionary look-up, locates word images in an indexed (segmented) set of documents. A two-step approach is employed in performing the search: (1) prototype selection: the query is used to obtain a set of handwritten samples of that word from a known set of writers (these are the prototypes), and (2) word matching: the prototypes are used to spot each occurrence of those words in the indexed document database. A ranking is performed on the entire set of test word images-- where the ranking criterion is a similarity score between each prototype word and the candidate words based on global word shape features. A database of 20,000 word images contained in 100 scanned handwritten Arabic documents written by 10 different writers was used to study retrieval performance. Using five writers for providing prototypes and the other five for testing, using manually segmented documents, 55% precision is obtained at 50% recall. Performance increases as more writers are used for training.