NDLI: Semi-automatic spine extraction for disc space narrowing diagnosis

Content Provider	IEEE Xplore Digital Library
Author	Samanu, N.S. Zulkifley, M.A. Hussain, A.
Copyright Year	2015
Description	Author affiliation: Dept. of Electr., Electron. & Syst. Eng., Univ. Kebangsaan Malaysia, Bangi, Malaysia (Samanu, N.S.; Zulkifley, M.A.; Hussain, A.)
Abstract	This paper describes the development of a semi-automatic system for detection and diagnosis of vertebrae condition, focuses on cervical area. The goal of this system is to facilitate medical community to make a faster pre-screening based on the imaging modalities, especially X-ray image. The main challenges in diagnosing a disease through X-ray image are the issue of blur and noise. Therefore, to achieve this goal, a semi-automatic spine extraction to detect disc space narrowing (DSN) condition has been developed that focused on patient with back pain history. In general, this system was developed on Matlab platform that consists of four major modules, which are image enhancement, image segmentation, feature extraction and classification. Image enhancement module utilized Contrast-limited Adaptive Histogram Equalization (CLAHE) and filtering technique to improve the image quality. After that, the second module is performed to extract the desired region from the original X-ray image. Feature extraction module is then implemented to extract unique signature of the vertebrae bones based on the bone's condition. For the last module, feed-forward backpropagation artificial neural network is used to classify the existence of DSN. It needs to be trained before testing is performed so that the parameters can be tuned for optimal classification. The quantitative performance proved that the X-ray image quality has been improved and the system has managed to classify the DSN condition. Simulation results show that the proposed system provides good performance of accuracy with average of 99% for the tested X-ray images. As for future work, the system can be further improved by using more measurement points between the two neighboring vertebras.
Starting Page	36
Ending Page	40
File Size	667957
Page Count	5
File Format	PDF
ISBN	9781467393447
e-ISBN	9781467393454
DOI	10.1109/ELECSYM.2015.7380810
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-09-29
Publisher Place	Indonesia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Pain Image edge detection Image processing Artificial neural networks Disc space narrowing Feature extraction X-ray image Cervical vertebrae X-ray imaging Image enhancement
Content Type	Text
Resource Type	Article

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