Edge detection based on dynamic morphology

Shan, Duan; Qin, Qianqing

doi:10.1117/12.655209

Cited by 2 publications

(1 citation statement)

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“…In this work, the search is for suspicious tissue which can be identified by regions surrounded by an edge. Edges in images can be detected using several algorithms such as directional morphology, curve-linear structure detection, directional second-order Gaussian derivative, and convolution-based edge detection algorithms [14][15][16][17][18][19][20][21]. Also, edges may have several shapes such as straight lines, circles, ellipses, and parabola.…”

Section: Modeling Of Image Featuresmentioning

confidence: 99%

Unsupervised Detection of Suspicious Tissue Using Data Modeling and PCA

Abdel‐Qader

Shen

Jacobs

et al. 2006

International Journal of Biomedical Imaging

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Breast cancer is a major cause of death and morbidity among women all over the world, and it is a fact that early detection is a key in improving outcomes. Therefore development of algorithms that aids radiologists in identifying changes in breast tissue early on is essential. In this work an algorithm that investigates the use of principal components analysis (PCA) is developed to identify suspicious regions on mammograms. The algorithm employs linear structure and curvelinear modeling prior to PCA implementations. Evaluation of the algorithm is based on the percentage of correct classification, false positive (FP) and false negative (FN) in all experimental work using real data. Over 90% accuracy in block classification is achieved using mammograms from MIAS database.

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