Kalaiselvi Thiruvenkadam

Publications

• 1.27

  Impact points

  Automatic brain extraction methods for T1 magnetic resonance images using region labeling and morphological operations.

  K Somasundaram, T Kalaiselvi

  Computers in biology and medicine. 07/2011; 41(8):716-25.

  In this work we propose two brain extraction methods (BEM) that solely depend on the brain anatomy and its intensity characteristics. Our methods are simple, unsupervised and knowledge based. Using an adaptive intensity thresholding method on the magnetic resonance images of head scans, a binary ima... [more] In this work we propose two brain extraction methods (BEM) that solely depend on the brain anatomy and its intensity characteristics. Our methods are simple, unsupervised and knowledge based. Using an adaptive intensity thresholding method on the magnetic resonance images of head scans, a binary image is obtained. The binary image is labeled using the anatomical facts that the scalp is the boundary between head and background, and the skull is the boundary separating brain and scalp. A run length scheme is applied on the labeled image to get a rough brain mask. Morphological operations are then performed to obtain the fine brain on the assumption that brain is the largest connected component (LCC). But the LCC concept failed to work on some slices where brain is composed of more than one connected component. To solve this problem a 3-D approach is introduced in the BEM. Experimental results on 61 sets of T1 scans taken from MRI scan center and neuroimage web services showed that our methods give better results than the popular methods, FSL's Brain Extraction Tool (BET), BrainSuite's Brain Surface Extractor (BSE) gives results comparable to that of Model-based Level Sets (MLS) and works well even where MLS failed. The average Dice similarity index computed using the "Gold standard" and the specificity values are 0.938 and 0.992, respectively, which are higher than that for BET, BSE and MLS. The average processing time by one of our methods is ≈1s/slice, which is smaller than for MLS, which is ≈4s/slice. One of our methods produces the lowest false positive rate of 0.075, which is smaller than that for BSE, BET and MLS. It is independent of imaging orientation and works well for slices with abnormal features like tumor and lesion in which the existing methods fail in certain cases.
• 1.27

  Impact points

  Fully automatic brain extraction algorithm for axial T2-weighted magnetic resonance images.

  K Somasundaram, T Kalaiselvi

  Computers in biology and medicine. 10/2010; 40(10):811-22.

  In this paper we propose two brain extraction algorithms (BEA) for T2-weighted magnetic resonance imaging (MRI) scans. The T2-weighted image is first filtered with a low pass filter (LPF) to remove or subdue the background noise. Then the image is diffused to enhance the brain boundaries. Using Ridl... [more] In this paper we propose two brain extraction algorithms (BEA) for T2-weighted magnetic resonance imaging (MRI) scans. The T2-weighted image is first filtered with a low pass filter (LPF) to remove or subdue the background noise. Then the image is diffused to enhance the brain boundaries. Using Ridler's method a threshold value for intensity is obtained. Using the threshold value a rough binary brain image is obtained. By performing morphological operations and using the largest connected component (LCC) analysis, a brain mask is obtained from which the brain is extracted. This method uses only 2D information of slices and is named as 2D-BEA. The concept of LCC failed in few slices. To overcome this problem, 3D information available in adjacent slices is used which resulted in 3D-BEA. Experimental results on 20 MRI data sets show that the proposed 3D-BEA gave excellent results. The performance of this 3D-BEA is better than 2D-BEA and other popular methods, brain extraction tool (BET) and brain surface extractor (BSE).

• Brain extraction method for T1-weighted magnetic resonance scans

  K. Somasundaram, T. Kalaiselvi

  Signal Processing and Communications (SPCOM), 2010 International Conference on; 08/2010

  In this paper we propose a brain extraction method that solely depends on the brain anatomy and its intensity characteristics. Using an adaptive intensity thresholding method on the MRI head scans, a binary image is obtained. The binary image is labeled using the anatomical facts that the scalp is t... [more] In this paper we propose a brain extraction method that solely depends on the brain anatomy and its intensity characteristics. Using an adaptive intensity thresholding method on the MRI head scans, a binary image is obtained. The binary image is labeled using the anatomical facts that the scalp is the boundary between head and background, and the skull is the boundary separating brain and scalp. A run length scheme is employed on the labeled image to get the rough brain portion. Morphological operations are then performed to obtain the fine brain on the assumption that brain is the largest connected component. But this concept failed to work on some slices where brain is composed of more than one connected component. To solve this problem a 3-D approach is introduced in the proposed BEM. Experimental results on 47 sets of T1 scans taken from MRI scan centre and neuroimage web services showed that our method gives better results than the popular methods BET, BSE and MLS.

• A Comparative Study of Segmentation Techniques used for MR Brain Images.

  Somasundaram Karuppanagounder, Kalaiselvi Thiruvenkadam

  Proceedings of the 2009 International Conference on Image Processing, Computer Vision, & Pattern Recognition, IPCV 2009, July 13-16, 2009, Las Vegas, Nevada, USA, 2 Volumes; 01/2009

• A Novel Technique for Finding the Boundary between the Cerebral Hemispheres from MR Axial Head Scans.

  Somasundaram Karuppanagounder, Kalaiselvi Thiruvenkadam

  Proceedings of the 4th Indian International Conference on Artificial Intelligence, IICAI 2009, Tumkur, Karnataka, India, December 16-18, 2009; 01/2009