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.
Reliable segmentation of the liver has been acknowledged as a significant step in several computational and
diagnostic processes. While several methods have been designed for liver segmentation, comparative analysis
of reported methods is limited by the unavailability of annotated datasets of the abdominal area. Currently
available generic data-sets constitute a small sample set, and most academic work utilizes closed datasets. We
have collected a dataset containing abdominal CT scans of 50 patients, with coordinates for the liver boundary.
The dataset will be publicly distributed free of cost with software to provide similarity metrics, and a liver
segmentation technique that uses Markov Random Fields and Active Contours. In this paper we discuss our
data collection methodology, implementation of similarity metrics, and the liver segmentation algorithm.
This paper describes an OCR-based technique for word
spotting in Devanagari printed documents. The system
accepts a Devanagari word as input and returns a sequence
of word images that are ranked according to their
similarity with the input query. The methodology involves
line and word separation, pre-processing document
words, word recognition using OCR and similarity
matching. We demonstrate a Block Adjacency Graph
(BAG) based document cleanup in the pre-processing
phase. During word recognition, multiple recognition hypotheses
are generated for each document word using a
font-independent Devanagari OCR. The similarity matching
phase uses a cost based model to match the word
input by a user and the OCR results. Experiments are
conducted on document images from the publicly available
ILT and Million Book Project dataset. The technique
achieves an average precision of 80% for 10 queries and
67% for 20 queries for a set of 64 documents containing
5780 word images. The paper also presents a comparison
of our method with template-based word spotting techniques.