NDLI: Efficient segmentation method using quantised and non-linear CeNN for breast tumour classification

Content Provider	IET Digital Library
Author	Liu, Zhongyang Zhuo, Cheng Xu, Xiaowei
Abstract	A new segmentation method for mammography imaging system is proposed. Segmentation of masses is always a difficult problem in radiological image interpretation. Conventional methods such as region growing suffer from their computational complexity and hence can hardly be used for segmentation of high-resolution images. In order to achieve efficiency in both computational complexity and accuracy, a novel digital cellular neural network (CeNN) based approach is presented for segmentation. The approach is featured with quantisation to significantly reduce the computational complexity and non-linear template for robustness. After segmentation, a multilayer perceptron classifier is used for feature extraction and classification. Compared with other prior works, the proposed work is able to reduce resource overhead up to 63% and energy consumption up to 41% on FPGA while maintaining only up to 1.5 and 0.6% accuracy deviations for mediolateral-oblique and cranial-caudal views, respectively.
Starting Page	737
Ending Page	738
Page Count	2
ISSN	00135194
Volume Number	54
e-ISSN	1350911X
Issue Number	Issue 12, Jun (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/el/54/12
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/el.2018.1213
Journal	Electronics Letters
Publisher Date	2018-05-11
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Biology And Medical Computing Biomedical Imaging/measurement Breast Tumour Classification Cancer Cellular Neural Nets Computational Complexity Computer Vision And Image Processing Technique Cranial-caudal Views Diagnostic Radiography Digital Cellular Neural Network Based Approach Efficient Segmentation Method Feature Extraction Field Programmable Gate Array FPGA High-resolution Image Image Classification Image Recognition Image Segmentation Mammography Mammography Imaging System Medical Image Processing Mediolateral-oblique Views Multilayer Perceptron Classifier Multilayer Perceptrons Neural Computing Technique Nonlinear Template Quantised-nonlinear CeNN Radiography And Computed Tomography Radiological Image Interpretation Region Growing Tumours X-Ray Technique X-Rays And Particle Beam
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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