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Neural networks aided stone detection in thick slab MRCP images

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Abstract

This paper proposes a detection scheme for identifying stones in the biliary tract of the body, which is examined using magnetic resonance cholangiopancreatography (MRCP), a sequence of magnetic resonance imaging targeted at the pancreatobiliary region of the abdomen. The scheme enhances the raw 2D thick slab MRCP images and extracts the biliary structure in the images using a segment-based region-growing approach. Detection of stones is scoped within this extracted structure, by highlighting possible stones. A trained feedforward artificial neural network uses selected features of size and average segment intensity as its input to detect possible stones in MRCP images and eliminate false stone-like objects. The proposed scheme achieved satisfactory results in tests of clinical MRCP thick slab images, indicating potential for implementation in computer-aided diagnosis systems for the liver.

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Acknowledgment

The author would like to express his gratitude to the consulting specialist, Dr. Zaharah Musa from Selayang Hospital, Malaysia for the expert medical consultation and MRCP patient images used in this work. This work was supported by the Ministry of Science, Technology and Innovation, Malaysia, through the Intensification for Research in Priority Areas (IRPA) grant scheme.

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  1. Centre for Image Processing and Telemedicine, Faculty of Engineering, Multimedia University, 63100, Cyberjaya, Selangor, Malaysia

    Rajasvaran Logeswaran

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  1. Rajasvaran Logeswaran
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Correspondence to Rajasvaran Logeswaran.

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Logeswaran, R. Neural networks aided stone detection in thick slab MRCP images. Med Bio Eng Comput 44, 711–719 (2006). https://doi.org/10.1007/s11517-006-0083-8

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  • DOI: https://doi.org/10.1007/s11517-006-0083-8

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