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Detection of Lung Nodules on Medical Images by the Use of Fractal Segmentation
dc.contributor.author | Rezaie, Afsaneh Abdollahzadeh | |
dc.contributor.author | Habiboghli, Ali | |
dc.date | 2017-09 | |
dc.date.accessioned | 2021-09-06T09:01:36Z | |
dc.date.available | 2021-09-06T09:01:36Z | |
dc.identifier.issn | 1989-1660 | |
dc.identifier.uri | https://reunir.unir.net/handle/123456789/11782 | |
dc.description.abstract | In the present paper, a method for the detection of malignant and benign tumors on the CT scan images has been proposed. In the proposed method, firstly the area of interest in which the tumor may exist is selected on the original image and by the use of image segmentation and determination of the image’s threshold limit, the tumor’s area is specified and then edge detection filters are used for detection of the tumor’s edge. After detection of area and by calculating the fractal dimensions with less percent of errors and better resolution, the areas where contain the tumor are determined. The images used in the proposed method have been extracted from cancer imaging archive database that is made available for public. Compared to other methods, our proposed method recognizes successfully benign and malignant tumors in all cases that have been clinically approved and belong to the database. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | International Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI) | es_ES |
dc.relation.ispartofseries | ;vol. 4, nº 5 | |
dc.relation.uri | https://ijimai.org/journal/bibcite/reference/2589 | es_ES |
dc.rights | openAccess | es_ES |
dc.subject | segmentation | es_ES |
dc.subject | tumor | es_ES |
dc.subject | cancer | es_ES |
dc.subject | fractal theory | es_ES |
dc.subject | medicine | es_ES |
dc.subject | IJIMAI | es_ES |
dc.title | Detection of Lung Nodules on Medical Images by the Use of Fractal Segmentation | es_ES |
dc.type | article | es_ES |
reunir.tag | ~IJIMAI | es_ES |
dc.identifier.doi | http://doi.org/10.9781/ijimai.2017.452 |