Picture languages in automatic radiological palm interpretation
The paper presents a new technique for cognitive analysis and recognition of pathological wrist bone lesions. This method uses AI techniques and mathematical linguistics allowing us to automatically evaluate the structure of the said bones, based on palm radiological images. Possibilities of computer interpretation of selected images, based on the methodology of automatic medical image understanding, as introduced by the authors, were created owing to the introduction of an original relational description of individual palm bones. This description was built with the use of graph linguistic formalisms already applied in artificial intelligence. The research described in this paper demonstrates that for the needs of palm bone diagnostics, specialist linguistic tools such as expansive graph grammars and EDT-label graphs are particularly well suited. Defining a graph image language adjusted to the specific features of the scientific problem described here permitted a semantic description of correct palm bone structures. It also enabled the interpretation of images showing some in-born lesions, such as additional bones or acquired lesions such as their incorrect junctions resulting from injuries and synostoses.
- Albus J.S. and Meystel A.M. (2001): Engineering of Mind: An Introduction to the Science of Intelligent Systems. - New York: Wiley.
- Bankman I. (Eds.) (2002): Handbook of Medical Imaging: Processing and Analysis. - San Diego: Academic Press.
- Burgener F.A. and Kormano M. (1997): Bone and Joint Disorders. - Thieme: Stuttgart.
- Duda R.O., Hart P.E. and Stork D.G. (2001): Pattern Classifications, 2nd Ed.. - New York: Wiley.
- Flasiński M. (1993): On the parsing of deterministic graph languages for syntactic pattern recognition. - Pattern Recognition, Vol. 26,No. 1, pp. 1-16.
- Meyer-Baese A. (2003): Pattern Recognition in Medical Imaging. -San Diego: Elsevier.
- Ogiela M.R. and Tadeusiewicz R. (2003a): Artificial intelligence structural imaging techniques in visual pattern analysis and medical data understanding. - Pattern Recogn., Vol. 36, No. 10, pp. 2441-2452.
- Ogiela M.R. and Tadeusiewicz R. (2003b): Cognitive vision systems in medical applications. - Lect. Notes Artif. Intell., Vol. 2871, pp. 116-123.
- Ogiela M.R. and Tadeusiewicz R. (2003c): New approach for cognitive analysis and understanding of medical patterns and visualizations. - Proc. SPIE, Vol. 5203 Applications of Digital Image Processing XXVI, SPIE, Bellingham, WA, pp. 615-622.
- Pietka E., Gertych A., Pospiech S., Fei-Cao, Huang H.K. and Gilsanz V. (2001): Computer-assisted bone age assessment: Image preprocessing and epiphyseal/metahyseal ROI extraction. - IEEE Trans. Medical Imag., Vol. 20, No. 8, pp. 715-729.
- Skomorowski M. (1998): Parsing of random graphs for scene analysis. -Int. J. Mach. Graph. Vision, Vol. 7, No. 12, pp. 313-323.
- Tadeusiewicz R. and Ogiela M.R. (2004a): Medical Image Understanding Technology. - Berlin: Springer.
- Tadeusiewicz R. and Ogiela M.R. (2004b): The new concept in computer vision: automatic understanding of the images. - Lect. Notes Artif. Intell., Vol. 3070, pp. 133-144, Berlin: Springer.
- Tadeusiewicz R. and Ogiela M.R. (2004c): Processing, analysis, recognition, and automatic understanding of medical images, In: Optical Methods, Sensors, Image Processing, and Visualization in Medicine (A. Nowakowski and B.B. Kosmowski, Eds.). - Proc. SPIE, Vol. 5505 (Progress in Biomedical Optics and Imaging, Vol. 5, No. 31), pp. 101-109.