Feature extraction method for breast cancer diagnosis in digital mammograms using multi-resolution transformations and SVM-fuzzy logic classifier

Manoharan, Prabukumar; Prasenjit, Nandi; Sangeetha, V.

doi:10.1504/ijcvr.2013.059102

Cited by 3 publications

(1 citation statement)

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“…The involuntary and accurate segmentation is important because labour-intensive segmentation of blood vessels in retinal images is both time intense and error-prone process for skilled physicians. [28][29][30]. Numerous numbers of papers published about the automatic blood vessels segmentation and still the segmentation accuracy results are not in the acceptable level.…”

Section: Introductionmentioning

confidence: 99%

Mathematical Morphology based Retinal Image Blood Vessels Segmentation

2019

IJITEE

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It is necessary to verify the state of blood vessel network in the retina for diagnosing various issues associated with eyes. In this research paper, an involuntary retinal vessel segmentation using mathematical morphology is proposed. The contrast of the retinal images is enhanced by contrast limited adaptive histogram equalisation technique. Ten blood vessels of the enhanced retinal image are detected using morphological processing. The hysteresis thresholding is applied on the blood vessels detected image to remove the unwanted back ground detail. Finally the properly segmented binary image of the retinal vessel is obtained using post processing process. Results of the presented method are verified by using most widely used for benchmarking retinal image databases such as, Child Heart and Health Study in England (CHASE_DB1) and Digital Retinal Images for Vessel Extraction (DRIVE) database by computing the evaluation metrics such as sensitivity, specificity, accuracy and precision. The better evaluation metrics achieved for the DRIVE dataset are 0.7493, 0.9687, 0.9524 and 0.6590, and the worst values are 0.6621, 0.9411, 0.9137 and 0.5491. The best evaluation metrics values for the CHASE_DB1 dataset are 0.5058, 0.8947, 0.9382 and 0.8856, and the worst values are 0.5639, 0.9581, 0.9137 and 0.7110. The investigational results show that the suggested approach provides the excellent accuracy in comparison with other approaches

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Section: Introductionmentioning

confidence: 99%