Int J CARS (2017) 12:1511–1519 DOI 10.1007/s11548-017-1597-2 ORIGINAL ARTICLE An automatic segmentation method of a parameter-adaptive PCNN for medical images 1 2 3 4 1 Jing Lian · Bin Shi · Mingcong Li · Ziwei Nan · Yide Ma Received: 10 January 2017 / Accepted: 24 April 2017 / Published online: 5 May 2017 © CARS 2017 Abstract Conclusion The algorithm has a great potential to achieve Purpose Since pre-processing and initial segmentation the pre-processing and initial segmentation steps in various steps in medical images directly affect the ﬁnal segmentation medical images. This is a premise for assisting physicians to results of the regions of interesting, an automatic segmenta- detect and diagnose clinical cases. tion method of a parameter-adaptive pulse-coupled neural network is proposed to integrate the above-mentioned two Keywords Parameter-adaptive pulse-coupled neural segmentation steps into one. This method has a low compu- network · Image segmentation · Optimal histogram tational complexity for different kinds of medical images and threshold · Ultrasound image · Magnetic resonance image · has a high segmentation precision. Mammogram image Methods The method comprises four steps. Firstly, an opti- mal histogram threshold is used to determine the parameter Introduction α for different kinds of
International Journal of Computer Assisted Radiology and Surgery – Springer Journals
Published: May 5, 2017
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