Gradient Vector Flow Field and Mass Region Extraction in Digital Mammograms

Zou, Fengmei; Zheng, Yefeng; Zhou, Zhengdong; Agyepong, Kwabena

doi:10.1109/cbms.2008.117

Cited by 13 publications

(6 citation statements)

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“…Zou et al [13] proposed a method that uses gradient vector flow field (GVF) which is a parametric deformable contour model. After the enhancement of mammographic images with adaptive histogram equalization, the GVF field component with the larger entropy is used to generate the ROI.…”

Section: Mammogram Segmentation Methodsmentioning

confidence: 99%

Survey on Segmentation Methods for Locating Masses in a Mammogram Image

Bandyopadhyay¹

2010

IJCA

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A digital mammogram generally detects varying degrees of breast cancer such as clustered microcalcifications, speculated lesions, circumscribed masses, ill-defined masses, and architectural distortions. Many methods of analysing digital mammograms have been recently examined and yielded varied success.Common techniques from the field of image processing have been applied to digital mammograms in an effort to locate signs of cancer sooner and more precisely than previously possible. Research suggests that computerized techniques applied/utilized by radiologists will be highly successful in analysing digital mammograms. Computerized systems that draw attention to areas of suspicion, otherwise less noticeable to radiologists have the potential to greatly increase early detection.Previously, Algorithms that effectively segment mammogram images into major sub-components and also meets the goals of efficiency and generality has been lacking. This paper reviews some methods of mammogram segmentation process for detection of masses in breast.

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Section: Mammogram Segmentation Methodsmentioning

confidence: 99%

Survey on Segmentation Methods for Locating Masses in a Mammogram Image

Bandyopadhyay¹

2010

IJCA

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“…Finally, they proposed a blockbased performance criterion to measure the result accuracy. F.Zou et al [5] proposed an algorithm to extract the regions of suspicious mass. They applied an adaptive histogram equalization to enhance mammographic images.…”

Section: Introductionmentioning

confidence: 99%

Semi-automated detection of breast mass spiculation using active contour

Boonthong

Jantarakongkul

Rasmequan

et al. 2014

Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific

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The use of computer research for breast cancer diagnosis in digital mammograms has been studied by some researchers for years. The researches based on medical image processing were developed and published continuously. Theirs objective are to create a diagnostic tool that can increase the accuracy of risk analysis for breast cancer. At the early stage, cancers may be identified as spiculated masses revealing architectural distortion. This research proposes a semiautomated method to detect architectural distortion characterized by thin lines radiating from its margins. It will help physicians as second or minor opinion before biopsy operation. The proposed method involves following major steps in sequence. A combination of the object attributes thresholding, hill-climbing and region growing algorithm is applied to digital mammogram for background and breast pectoral muscle removal. The second is a region of interest (ROI) selection based on image splitting and breast ratio estimation. In the third step, the shade corrections of ROI are considered by using the contrast-limited adaptive histogram equalization. Next, we apply the modified hierarchical clustering to detect and enhance the possible cluster of spiculated masses. The other clusters will be a significant reduction. The final step is established to segment spiculated shape by employing the parametric active contour method. The numerical experiments of the proposed method are performed by testing on the digital database for screening mammography (DDSM) made up by the University of South Florida.

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“…Then, the quality of mammographic images is improved. Several techniques have been proposed for pre-processing such as thresholding [12][13][14], region-based techniques [15][16][17] and edge detection techniques [18,19]. In the feature extraction stage, the features are extracted from mammographic images so that the system can correctly classify benign and malign lesions.…”

Section: Introductionmentioning

confidence: 99%