Segmentation of pectoral muscle in mammograms using fractal method

Shanmugavadivu, P.; Sivakumar, V.

doi:10.1109/iccci.2013.6466299

Cited by 7 publications

(3 citation statements)

References 11 publications

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“…Self-similarity is defined as a property, where a subset is indistinguishable from the whole, when magnified to the size of the whole. Fractals are of rough geometric self similar and irregular shapes which can be subdivided in parts, each of which is reduced to similar of the whole [16][17][18][19].…”

Section: 1fractal Dimensionmentioning

confidence: 99%

Mammogram Mass Segmentation Using Fractal Oriented Gamma Transformation

Shanmugavadivu¹,

Sivakumar²

2016

IJCSITY

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Digital mammogram has become the reliable and most effective screening method for the early detection of breast cancer. A novel Fractal Hurst-based Gamma Transformation (FHGT) is presented in this paper for the segmentation of masses from mammograms. This method is a composition of two mechanisms namely detection of masses from digital mammograms and the segmentation of those detected masses. The artifacts removal and spatial enhancement are performed for pre-processing of the mammograms, which subsequently help in mass detection. The process of segmentation is performed using morphological operations. The proposed FHGT is proved to produce promising results in terms of segmentation that confirms its merit.

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Section: 1fractal Dimensionmentioning

confidence: 99%

Mammogram Mass Segmentation Using Fractal Oriented Gamma Transformation

Shanmugavadivu¹,

Sivakumar²

2016

IJCSITY

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“…The approach of Median filtering is a useful tool to smoothen the image and to reduce the excessive distortions such as thinning or thickening of object boundaries. Median filtering helps to remove the digitization noise and high frequency components from the mammography images [1,2,15,[19][20][21].…”

Section: Median Filteringmentioning

confidence: 99%

“…Opening with the structuring element is used to eliminate specific image details smaller than the structuring element. The definitions of these operations are dependent on the image types, such as binary, gray level or color of the image being processed [1,2,15,[19][20][21].…”

Section: Morphological Operationsmentioning

confidence: 99%

Wavelet Transformation-Based Detection of Masses in Digital Mammograms

Shanmugavadivu¹

2014

IJRET

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A Novel Wavelet Transformation-Based Detection of Masses in digital mammograms (WTBDM) is proposed in this paper that enables for the early prognosis of breast cancer. The wavelet analysis is explored for analyzing and identifying strong variations in intensities within the mammographic data which highlights and recognizes the masses effectively. The proposed algorithm, in addition to wavelet transformation, uses morphological preprocessing, region properties and seeded region growing to remove the digitization noises, to remove the pectoral muscle and to suppress radiopaque artifacts, thus segmenting the abnormal masses accurately. The combined potential of wavelet and region growing helps for effective mass segmentation that vouches the merit of the proposed technique.

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