FCMLSM Segmentation of Micro-Vessels in Slight Defocused Microscopic Images
To address problems relating to microscopic micro-vessel images of living bodies, including poor vessel continuity, blurry boundaries between vessel edges and tissue and uneven field illuminance, and this paper put forward a fuzzy-clustering level-set segmentation algorithm. By this method, pre-trea...
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| Vydáno v: | Journal of advanced computational intelligence and intelligent informatics Ročník 23; číslo 6; s. 1073 - 1079 |
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| Hlavní autoři: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
20.11.2019
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| ISSN: | 1343-0130, 1883-8014 |
| On-line přístup: | Získat plný text |
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| Shrnutí: | To address problems relating to microscopic micro-vessel images of living bodies, including poor vessel continuity, blurry boundaries between vessel edges and tissue and uneven field illuminance, and this paper put forward a fuzzy-clustering level-set segmentation algorithm. By this method, pre-treated micro-vessel images were segmented by the fuzzy c-means (FCM) clustering algorithm to obtain original contours of interesting areas in images. By the evolution equations of the improved level set function, accurate segmentation of microscopic micro-vessel images was realized. This method can effectively solve the problem of manual initialization of contours, avoid the sensitivity to initialization and improve the accuracy of level-set segmentation. The experiment results indicate that compared with traditional micro-vessel image segmentation algorithms, this algorithm is of high efficiency, good noise immunity and accurate image segmentation. |
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| ISSN: | 1343-0130 1883-8014 |
| DOI: | 10.20965/jaciii.2019.p1073 |