NDLI: An efficient algorithm for textural feature extraction and detection of tumors for a class of brain MR imaging applications

Content Provider	IEEE Xplore Digital Library
Author	Parameshwari, D.S. Aparna, P.
Copyright Year	2014
Description	Author affiliation: Dept. of Electron. & Commun., Nat. Inst. of Technol., Mangalore, India (Parameshwari, D.S.; Aparna, P.)
Abstract	In this paper, we propose an efficient textural feature extraction algorithm (TFEA) based on higher order statistical cumulant namely Kurtosis for a class of brain MR imaging applications. Using a model that represents the wavelet coefficient energies of the sub-bands of multi-level decomposition of the image as a basis, a feature set involving three parameters for each band corresponding to probability density function (PDF) of generalized Gaussian type is derived. The logical correctness and working of the proposed TFEA are first verified based on MATLAB ver.2010a tool. The algorithm is applied in conjunction with one of the popularly used canny edge detection algorithm for segmenting a class of real and synthetic magnetic resonance (MR) images to detect the region of a tumor if present. The use of the proposed approach results in reduced feature set size thus obviating the need for employing specialized feature selection/reduction algorithms. A detailed look at the experimental results clearly show an improvement in the segmentation quality compared with conventional method.
Starting Page	339
Ending Page	344
File Size	423942
Page Count	6
File Format	PDF
ISBN	9781479946129
ISSN	21653577
DOI	10.1109/ICDSP.2014.6900683
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-08-20
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Tumors Signal processing algorithms Feature extraction Magnetic resonance imaging Digital signal processing Discrete wavelet transforms Kurtosis Textural Analysis Discrete Wavelet transform Feature Extraction
Content Type	Text
Resource Type	Article

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