کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
1807246 1025250 2009 11 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
A fully automated algorithm under modified FCM framework for improved brain MR image segmentation
موضوعات مرتبط
مهندسی و علوم پایه فیزیک و نجوم فیزیک ماده چگال
پیش نمایش صفحه اول مقاله
A fully automated algorithm under modified FCM framework for improved brain MR image segmentation
چکیده انگلیسی

Automated brain magnetic resonance image (MRI) segmentation is a complex problem especially if accompanied by quality depreciating factors such as intensity inhomogeneity and noise. This article presents a new algorithm for automated segmentation of both normal and diseased brain MRI. An entropy driven homomorphic filtering technique has been employed in this work to remove the bias field. The initial cluster centers are estimated using a proposed algorithm called histogram-based local peak merger using adaptive window. Subsequently, a modified fuzzy c-mean (MFCM) technique using the neighborhood pixel considerations is applied. Finally, a new technique called neighborhood-based membership ambiguity correction (NMAC) has been used for smoothing the boundaries between different tissue classes as well as to remove small pixel level noise, which appear as misclassified pixels even after the MFCM approach. NMAC leads to much sharper boundaries between tissues and, hence, has been found to be highly effective in prominently estimating the tissue and tumor areas in a brain MR scan. The algorithm has been validated against MFCM and FMRIB software library using MRI scans from BrainWeb. Superior results to those achieved with MFCM technique have been observed along with the collateral advantages of fully automatic segmentation, faster computation and faster convergence of the objective function.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Magnetic Resonance Imaging - Volume 27, Issue 7, September 2009, Pages 994–1004
نویسندگان
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