Bio-medical Ontologies Maintenance and Change Management

Multimedia Medical Databases 125
Each image is automatically segmented into regions named "blobs" with associated
color and texture descriptors. Querying is based on the attributes of one or
two regions of interest, rather than a description of the entire image. The blob descriptions
were indexing using a tree in order to make a faster retrieval. Experiments
showed encouraging results for both querying and indexing [3, 8, 10].
In [64] the Schema Reference System is presented. This is a content-based image
retrieval system that employs multiple segmentation algorithms and indexing
and retrieval subsystems. These algorithms are the following:
• Pseudo Flat Zone Loop algorithm (PFZL), contributed by Munich University of
Technology -Institute for Integrated Systems.
• Modified Recursive Shortest Spanning Tree algorithm (MRSST), contributed
by Dublin City University
• K-Means-with-Connectivity-Constraint algorithm (KMCC), contributed by the
Informatics and Telematics Institute/Centre for Research and Technology -
Hellas.
• Expectation Maximization algorithm (EM) in a 6D colour/texture space, contributed
by Queen Mary University of London.
The automatic segmentation techniques were applied on various imaging modalities:
brain imaging, chest radiography, computed tomography, digital mammography
or ultrasound imaging.
A lot of studies were made on MR brain images in order to extract the brain
volume, to outline structures such as the cerebral cortex or the hippocampus or to
segment the brain tissue into gray matter, white matter and cerebrospinal fluid
with the help of classifier approaches, clustering approaches, neuronal network
and Markov random fields. For the segmentation of the cerebral cortex or other
structures (the ventricles, the corpus callosum, the hippocampus) the deformable
models were especially used. The atlas-guided methods are capable of fully segmentation
of the brain structures [76].
Automatic segmentation techniques in computed tomography were applied to
bone scans (thresholding, region growing, Markov random fields or deformable
models), to thoracic scans (deformable models, region growing combined with
watershed algorithms, region growing combined with fuzzy logic) or liver images
(deformable models) [53, 76, 68].
The automatic segmentation applied on digital mammography tries to distinguish
the tumors, the microcalcification clusters or other pathologies. In reference
materials two approaches can be found:
• Image initial segmentation and labeling the candidate regions as normal or suspicious
• Image processing in order to detect the presence of pathology and then image
segmentation to determine its precise location
The most used techniques are: thresholding, region growing and Markov
random fields, because pathological regions have often different texture characteristics
[76].