Abnormality Detection in Ovarian Ultrasound Images using Active Contours
Keywords:Image segmentation, Ovary, anisotropic diffusion filter, active contour,
AbstractAnalyzing ovarian size parameters and its shape is the first hand information required in Ovary diagnosis. Automated segmentation of Ovary and measuring its size and analyzing its shape can help doctors in mass screening programme in faster diagnosis. In this paper, we propose a new improved algorithm (anisotropic diffusion filter, CLAHE enhancement, and active contour segmentation) to extract the Ovary from its homogeneous background followed by geometrical and shape feature extraction of the Ovary for automated computer-assisted analysis. The proposed algorithm is tested on 75 Transvaginal ultrasound images of ovaries. The experimental results are validated against the manual measurements done by the expert and the results obtained by our algorithm are in good agreement with experts inputs. The proposed algorithm could achieve an average Error Percentage EM1 of 4% for Major-Axis length and average Error Percentage EM2 of 5.43% for Minor-Axis length and classification rate of 91.66%.
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