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Active Contour

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Charles Bih-shiou Tsang – 1st expert on this subject based on the ideXlab platform

  • Multigradient field Active Contour for multilayer detection of ultrasound rectal wall image
    2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2001
    Co-Authors: Di Xiao, Wan Sing Ng, U.r. Abeyratne, Charles Bih-shiou Tsang

    Abstract:

    This paper presents a novel multigradient field Active Contour algorithm with an extended ability for multiple object delineation, which overcomes some limitations of ordinary Active Contour models. One of the aims is to apply this technique for multilayer boundary detection of ultrasound rectal wall image, which is important in colorectal clinical diagnosis for rectal tumor staging. The core part in this algorithm is the proposal of multigradient vector field concept, which is used as image forces for alternate constraints on deformation of the Active Contour. Its application to the ultrasound rectal wall image is also given to illustrate the multiple layer detection ability.

  • Rectal wall structure delineation and broken layer recognition by multigradient field Active Contour
    The Seventh Australian and New Zealand Intelligent Information Systems Conference 2001, 2001
    Co-Authors: Di Xiao, Charles Bih-shiou Tsang, Wan Sing Ng, U.r. Abeyratne, Kwoh Chee Keong, Seow Cheon

    Abstract:

    This paper presents a novel multigradient field Active Contour algorithm with an extended ability for multiple object delineation, which overcomes some limitations of ordinary Active Contour model (snakes). One of the aims is to apply this technique for multilayer boundary detection from ultrasound rectal wall image, which is important in colorectal clinical diagnosis for rectal tumor staging. The core part in the algorithm is the proposal of multigradient vector field concept, which is used to replace the image forces in the kinetic equation of the Active Contour. For some broken layers on the rectal wall from the penetration by tumor, a modified local cost function approach is proposed to recognize the broken segments from the current obtained boundaries.

Di Xiao – 2nd expert on this subject based on the ideXlab platform

  • Multigradient field Active Contour for multilayer detection of ultrasound rectal wall image
    2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2001
    Co-Authors: Di Xiao, Wan Sing Ng, U.r. Abeyratne, Charles Bih-shiou Tsang

    Abstract:

    This paper presents a novel multigradient field Active Contour algorithm with an extended ability for multiple object delineation, which overcomes some limitations of ordinary Active Contour models. One of the aims is to apply this technique for multilayer boundary detection of ultrasound rectal wall image, which is important in colorectal clinical diagnosis for rectal tumor staging. The core part in this algorithm is the proposal of multigradient vector field concept, which is used as image forces for alternate constraints on deformation of the Active Contour. Its application to the ultrasound rectal wall image is also given to illustrate the multiple layer detection ability.

  • Rectal wall structure delineation and broken layer recognition by multigradient field Active Contour
    The Seventh Australian and New Zealand Intelligent Information Systems Conference 2001, 2001
    Co-Authors: Di Xiao, Charles Bih-shiou Tsang, Wan Sing Ng, U.r. Abeyratne, Kwoh Chee Keong, Seow Cheon

    Abstract:

    This paper presents a novel multigradient field Active Contour algorithm with an extended ability for multiple object delineation, which overcomes some limitations of ordinary Active Contour model (snakes). One of the aims is to apply this technique for multilayer boundary detection from ultrasound rectal wall image, which is important in colorectal clinical diagnosis for rectal tumor staging. The core part in the algorithm is the proposal of multigradient vector field concept, which is used to replace the image forces in the kinetic equation of the Active Contour. For some broken layers on the rectal wall from the penetration by tumor, a modified local cost function approach is proposed to recognize the broken segments from the current obtained boundaries.

Chirag Kamal Ahuja – 3rd expert on this subject based on the ideXlab platform

  • a novel content based Active Contour model for brain tumor segmentation
    Magnetic Resonance Imaging, 2012
    Co-Authors: Jainy Sachdeva, Vinod Kumar, Indra Gupta, Niranjan Khandelwal, Chirag Kamal Ahuja

    Abstract:

    Abstract Brain tumor segmentation is a crucial step in surgical and treatment planning. Intensity-based Active Contour models such as gradient vector flow (GVF), magneto static Active Contour (MAC) and fluid vector flow (FVF) have been proposed to segment homogeneous objects/tumors in medical images. In this study, extensive experiments are done to analyze the performance of intensity-based techniques for homogeneous tumors on brain magnetic resonance (MR) images. The analysis shows that the state-of-art methods fail to segment homogeneous tumors against similar background or when these tumors show partial diversity toward the background. They also have preconvergence problem in case of false edges/saddle points. However, the presence of weak edges and diffused edges (due to edema around the tumor) leads to oversegmentation by intensity-based techniques. Therefore, the proposed method content-based Active Contour (CBAC) uses both intensity and texture information present within the Active Contour to overcome above-stated problems capturing large range in an image. It also proposes a novel use of Gray-Level Co-occurrence Matrix to define texture space for tumor segmentation. The effectiveness of this method is tested on two different real data sets (55 patients — more than 600 images) containing five different types of homogeneous, heterogeneous, diffused tumors and synthetic images (non-MR benchmark images). Remarkable results are obtained in segmenting homogeneous tumors of uniform intensity, complex content heterogeneous, diffused tumors on MR images (T1-weighted, postcontrast T1-weighted and T2-weighted) and synthetic images (non-MR benchmark images of varying intensity, texture, noise content and false edges). Further, tumor volume is efficiently extracted from 2-dimensional slices and is named as 2.5-dimensional segmentation.

  • A novel content-based Active Contour model for brain tumor segmentation
    Magnetic Resonance Imaging, 2012
    Co-Authors: Jainy Sachdeva, Vinod Kumar, Indra Gupta, Niranjan Khandelwal, Chirag Kamal Ahuja

    Abstract:

    Brain tumor segmentation is a crucial step in surgical and treatment planning. Intensity-based Active Contour models such as gradient vector flow (GVF), magneto static Active Contour (MAC) and fluid vector flow (FVF) have been proposed to segment homogeneous objects/tumors in medical images. In this study, extensive experiments are done to analyze the performance of intensity-based techniques for homogeneous tumors on brain magnetic resonance (MR) images. The analysis shows that the state-of-art methods fail to segment homogeneous tumors against similar background or when these tumors show partial diversity toward the background. They also have preconvergence problem in case of false edges/saddle points. However, the presence of weak edges and diffused edges (due to edema around the tumor) leads to oversegmentation by intensity-based techniques. Therefore, the proposed method content-based Active Contour (CBAC) uses both intensity and texture information present within the Active Contour to overcome above-stated problems capturing large range in an image. It also proposes a novel use of Gray-Level Co-occurrence Matrix to define texture space for tumor segmentation. The effectiveness of this method is tested on two different real data sets (55 patients – more than 600 images) containing five different types of homogeneous, heterogeneous, diffused tumors and synthetic images (non-MR benchmark images). Remarkable results are obtained in segmenting homogeneous tumors of uniform intensity, complex content heterogeneous, diffused tumors on MR images (T1-weighted, postcontrast T1-weighted and T2-weighted) and synthetic images (non-MR benchmark images of varying intensity, texture, noise content and false edges). Further, tumor volume is efficiently extracted from 2-dimensional slices and is named as 2.5-dimensional segmentation. © 2012 Elsevier Inc.