Generally, radiologists analyse the Magnetic Resonance Imaging (MRI) by visual inspection to detect and identify the presence of tumour or abnormal tissue in brain MR images. The huge number of such MR images makes this visual interpretation process, not only laborious and expensive but often erroneous. Furthermore, the human eye and brain sensitivity to elucidate such images gets reduced with the increase of number of cases, especially when only some slices contain information of the affected area. Therefore, an automated system for the analysis and classification of MR images is mandatory. In this paper, we propose a new method for abnormality detection from T1-Weighted MRI of human head scans using three planes, including axial plane, coronal plane, and sagittal plane. Three different thresholds, which are based on texture features: mean, energy and entropy, are obtained automatically. This allowed to accurately separating the MRI slice into normal and abnormal one. However, the abnormality detection contained some normal blocks assigned wrongly as abnormal and vice versa. This problem is surmounted by applying the fine-tuning mechanism. Finally, the MRI slice abnormality detection is achieved by selecting the abnormal slices along its tumour region (Region of Interest-ROI).
In this work lactone (1) was prepared from the reaction of p-nitro phenyl hydrazine with ethylacetoacetate, which upon treatment with benzoyl chloride afforded the lactame (2). The reaction of (2) with 2-amino phenol produced a new Schiff base (L) in good yield. Complexes of V(IV), Zr(IV), Rh(III), Pd(II), Cd(II) and Hg(II) with the new Schiff base (L) have been prepared. The compounds (1, 2) were characterized by FT-IR and UV spectroscopy, as well as characterizing ligand (L) by the same techniques with elemental analysis (C.H.N) and (1H-NMR). The prepared complexes were identified and their structural geometries were suggested by using elemental analysis (C.H.N), flame atomic absorption technique, FT-IR and UV-Vis spectroscopy, in additio
... Show More