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Conference papers
Cancerous object detection using morphological region-based active contour in ultrasound images
Abstract : Ultrasound is widely utilized in radiological screening to achieve early cancer investigations. Alongside this utilization, the need of object detection and segmentation techniques highly increases as crucial procedure of computer aided diagnosis tool. In addition, the industrial revolution 4.0 also demands automation and digitization in every technological development including computer aided diagnosis systems. This paper presents a morphological region-based active contour called MoRbAC to automatically detect the cancerous objects. Global level set active contour is initially employed to quick segment all objects in the whole image. Inhomogeneity in ultrasound images will lead to erroneous segmentation result. Hence,several morphology operations are taken to enhance it. The cancerous objects are then specifically detected by comparative calculation of area similarity. The proposed MoRbAC was tested in the real ultrasound images of breast and thyroid which validated by manual ground truth localization. Measurements of overlapping and disagreement regions are applied as quantitative indexes to demonstrate its performance. Average values of each validation has achieved i.e accuracy 98.58±0.89%, sensitivity 89.58±7.69%, specificity 99.58±0.11%, precision 95.58±2.77%, similarity 92.36±4.67%. In conclusion, the high performance of MoRbAC indicates its potential for practical applications in ultrasound computer aided diagnosis.
Cited literature [16 references]
https://hal-emse.ccsd.cnrs.fr/emse-02472462
Contributor : Fatima Lillouch Connect in order to contact the contributor
Submitted on : Monday, February 10, 2020 - 11:50:34 AM
Last modification on : Wednesday, November 3, 2021 - 6:29:16 AM
Long-term archiving on: : Monday, May 11, 2020 - 2:30:59 PM
Contributor : Fatima Lillouch Connect in order to contact the contributor
Submitted on : Monday, February 10, 2020 - 11:50:34 AM
Last modification on : Wednesday, November 3, 2021 - 6:29:16 AM
Long-term archiving on: : Monday, May 11, 2020 - 2:30:59 PM
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