An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
DeQuant: a flexible multiresolution restoration framework
Signal Processing
DeQuant: a flexible multiresolution restoration framework
Signal Processing
Segmentation of kidney from ultrasound B-mode images with texture-based classification
Computer Methods and Programs in Biomedicine
Multi-resolution Image Parametrization in Stepwise Diagnostics of Coronary Artery Disease
AIME '07 Proceedings of the 11th conference on Artificial Intelligence in Medicine
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Automatic computer-aided sacroiliac joint index analysis for bone scintigraphy
Computer Methods and Programs in Biomedicine
Knowledge-based segmentation of spine and ribs from bone scintigraphy
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part I
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Bone scintigraphy or whole-body bone scan is one of the most common diagnostic procedures in nuclear medicine used in the last 25 years. Pathological conditions, technically poor image resolution and artefacts necessitate that algorithms use sufficient background knowledge of anatomy and spatial relations of bones in order to work satisfactorily. A robust knowledge based methodology for detecting reference points of the main skeletal regions that is simultaneously applied on anterior and posterior whole-body bone scintigrams is presented. Expert knowledge is represented as a set of parameterized rules which are used to support standard image-processing algorithms. Our study includes 467 consecutive, non-selected scintigrams, which is, to our knowledge the largest number of images ever used in such studies. Automatic analysis of whole-body bone scans using our segmentation algorithm gives more accurate and reliable results than previous studies. Obtained reference points are used for automatic segmentation of the skeleton, which is applied to automatic (machine learning) or manual (expert physicians) diagnostics. Preliminary experiments show that an expert system based on machine learning closely mimics the results of expert physicians.