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Author: search.filters.author.Naruephai, ChadapornSubject: search.filters.subject.Morphology

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    Morphology-based algorithm and application for mammographic masses segmentation
    (2013-01-01)
    Suapang, Piyamas
    ;
    Naruephai, Chadaporn
    ;
    Chivapreecha, Sorawat
    Medical images segmentation is an important work for object recognition of the human organs and it is an important pre-processing step in medical image segmentation and 3D reconstruction. Conventionally, segmentation is detected according to some early brought forward algorithms such as gradient-based algorithm and template-based algorithm, but they are not so good for noise medical image segmentation. In this paper, basic morphological theory and operations are introduced at first, and then a novel morphological segmentation algorithm is proposed to detect the segment of mammographic masses with salt-And-pepper noise. The experimental results show that the proposed algorithm is more efficient for medical image denoising and segmentation than the usually used template-based segmentation algorithms and general morphological segmentation algorithms. Furthermore, the application is helpful for physician and doctors in diagnosis of the breast cancer in further steps.
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    Mammographic masses segmentation based on morphology
    (2012-12-01)
    Suapang, Piyamas
    ;
    Naruephai, Chadaporn
    ;
    Thongyoun, Methinee
    ;
    Chivaprecha, Sorawat
    Medical images segmentation is an important work for object recognition of the human organs and it is an important pre-processing step in medical image segmentation and 3D reconstruction. Conventionally, segmentation is detected according to some early brought forward algorithms such as gradient-based algorithm and template-based algorithm, but they are not so good for noise medical image segmentation. In this paper, basic morphological theory and operations are introduced at first, and then a novel morphological segmentation algorithm is proposed to detect the segment of mammographic masses with salt-and-pepper noise. The experimental results show that the proposed algorithm is more efficient for medical image denoising and segmentation than the usually used template-based segmentation algorithms and general morphological segmentation algorithms. ©2012 IEEE.