Fuzzy Logic has played an important role in medical image (MI) segmentation in the last decade. Automatic blood vessel segmentation from 3D medical images is an emerging area where segmentation algorithms could be combined with evolutionary computation methods for better diagnosis and higher decision accuracy. This paper introduces an automatic blood vessel segmentation algorithm from 3D images using Fuzzy logic. The proposed fuzzy system decides degree of Vesselness according to Eigen values of Hessian matrix. 3D synthetic and real CTA clinical image database are used to test the proposed algorithm and show a correct voxel classification. The proposed method shows better segmentation results compared to manual and swarm intelligence methods. Furthermore, fuzzy has led to better time improvement.
According to the current study results it was concluded that:-hsa_circ_0067705 is overexpressed in NSCLC-MPE patients than non-MPE patients and the expression level was positively correlated with serum LDH.-hsa_circ_0067705could be a potential biomarker for the diagnosis of NSCLC-MPE and differentiate NSCLC-MPE from tuberculous effusion.
The study was conducted on 50 patients divided into two groups: Group I: 25 patients with MPE recently diagnosed with NSCLC-MPE, by demonstration of malignant cells in pleural fluid and/or on a pleural biopsy sample. Group II: 25 diagnosed patients with non-MPE of a matching age and sex as a comparable group. All patients included in the present study were subjected to the following: 1.Complete history taking. 2.Complete physical examination. 3.Radiological investigations. 3. Laboratory Investigations including: •Routine lab investigations: -Complete blood count (CBC). -Renal function tests (serum urea and creatinine) -Liver function tests (alanine aminotransferase (ALT), aspartate aminotransferase (AST), serum albumin and serum total protein • Pleural fluid examination (physical, chemical and microscopic).
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