Segmentation for brain magnetic resonance images using dual-tree complex wavelet transform and spatial constrained self-organizing tree map

• Published in: International journal of imaging systems and technology Vol. 24; no. 3; pp. 208 - 214
• Main Authors: Zhang, Jingdan, Jiang, Wuhan
• Format: Journal Article
• Language: English
• Published: Hoboken, NJ Blackwell Publishing Ltd 01.09.2014; Wiley; Wiley Subscription Services, Inc
• Subjects: Applied sciences; Artificial intelligence; Biological and medical sciences; Computer science; control theory; systems; Connectionism. Neural networks; dual-tree complex wavelet transform; Exact sciences and technology; Investigative techniques, diagnostic techniques (general aspects); medical image segmentation; Medical sciences; MR images; Nervous system; Pattern recognition. Digital image processing. Computational geometry; Radiodiagnosis. Nmr imagery. Nmr spectrometry; self-organizing map; Positioning; Grey level image; Image processing; MR images; Subband; Low frequency; Self organization; Tree; self-organizing map; Threshold; Cartography; Subband decomposition; Spatial cognition; Neural network; Kohonen algorithm; Nuclear magnetic resonance imaging; medical image segmentation; Tree automaton; Unsupervised learning; Complex variable method; dual-tree complex wavelet transform; Image segmentation; Spatial data; Gray scale; Wavelet transformation; Medical imagery
• ISSN: 0899-9457; 1098-1098
• DOI: 10.1002/ima.22096

ABSTRACT In brain MR images, the noise and low‐contrast significantly deteriorate the segmentation results. In this paper, we introduce a novel application of dual‐tree complex wavelet transform (DT‐CWT), and propose an automatic unsupervised segmentation method integrating DT‐CWT with self‐organizing map for brain MR images. First, a multidimensional feature vector is constructed based on the intensity, low‐frequency subband of DT‐CWT, and spatial position information. Then, a spatial constrained self‐organizing tree map (SCSOTM) is presented as the segmentation system. It adaptively captures the complicated spatial layout of the individual tissues, and overcomes the problem of overlapping gray‐scale intensities for different tissues. SCSOTM applies a dual‐thresholding method for automatic growing of the tree map, which uses the information from the high‐frequency subbands of DT‐CWT. The proposed method is validated by extensive experiments using both simulated and real T1‐weighted MR images, and compared with the state‐of‐the‐art algorithms. © 2014 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 24, 208–214, 2014