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- Bårman, Håkan, et al.
(författare)
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Computer-Aided Analysis of Mammograms
- 1993
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Ingår i: Proceedings Nordic symposium on PACS, Digital Radiology and Telemedicine. ; , s. 76-
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Konferensbidrag (refereegranskat)
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- Bårman, Håkan
(författare)
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Curvature Estimation and Description
- 1989
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This paper presents a new method for detection and estimation of curvature. The algorithm is implemented in the hierarchical feature pyramid proposed in the GOP concept. Curvature is handled at the second level of the pyramid with a vector field description of the orientation of the image as input. This complex image is convolved with typically eight filters. The filter responses are combined into a description of curvature direction, curvature magnitude and curvature/linearity ratio. The procedure resembles in many ways the algorithms for the first level of the feature pyramid and seems to be a natural extension of these. The method is easy to implement and the tests made show that it performs well and can handle noisy conditions. Some comparisons with other algorithms have been carried out, and the results indicate that the methodology presented in this paper has a number of important advantages over other methods.
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- Bårman, Håkan, et al.
(författare)
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Feature Extraction for Computer-Aided Analysis of Mammograms
- 1994
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Ingår i: State of the Art in Digital Mammographic Image Analysis. - Singapore : World Scientific Publishing Co. Ltd. - 9810215096 - 9789810215095
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Bokkapitel (refereegranskat)abstract
- A framework for computer-aided analysis of mammograms is described. General computer vision algorithms are combined with application specific procedures in a hierarchical fashion. The system is under development and is currently limited to detection of a few types of suspicious areas. The image features are extracted by using feature extraction methods where wavelet techniques are utilized. A low-pass pyramid representation of the image is convolved with a number of quadrature filters. The filter outputs are combined according to simple local Fourier domain models into parameters describing the local neighborhood with respect to the model. This produces estimates for each pixel describing local size, orientation, Fourier phase, and shape with confidence measures associated to each parameter. Tentative object descriptions are then extracted from the pixel-based features by application-specific procedures with knowledge of relevant structures in mammograms. The orientation, relative brightness and shape of the object are obtained by selection of the pixel feature estimates which best describe the object. The list of object descriptions is examined by procedures, where each procedure corresponds to a specific type of suspicious area, e.g. clusters of microcalcifications.
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