Journal of Biomedical Engineering and Medical Imaging <p>Journal of Biomedical Engineering and Medical Imaging is peer-reviewed open access bi-monthly on-line journal that provides a medium of the rapid publication of original research papers, review articles, book reviews and short communications in the field of bio-medical engineering; medical imaging, analysis and processing. It encourages interdisciplinary research and development activities in an international environment.</p> en-US Journal of Biomedical Engineering and Medical Imaging 2055-1266 <p>Authors wishing to include figures, tables, or text passages that have already been published elsewhere are required to obtain permission from the copyright owner(s) for both the print and online format and to include evidence that such permission has been granted when submitting their papers. 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Alternatively, you can submit it by email&nbsp;<a href=" : International Journal of Medical Imaging and Graphics!"></a></p> Parasites as Bioindicator for Health Status and Environmental quality of Freshwater Fish species in Ekiti State, Nigeria <p>The prevalence of parasites occurring on and in the internal organs of fish species was studied. Fish species (<em>Oreochromis niloticus, Clarias gariepinus, Tilapia zilli</em>) were randomly obtained from fishermen landing at the jetties of Ado, Ero, Ogbese, Ikun and Egbe dams, in Ekiti state. Samples were dissected; smears for the identification of ectoparasites were taken directly from the gills, operculum and skin.<strong> </strong>The parasites were excised, identified and counted. Nematode parasite with the total sum of 164(29.6%) was the most abundant parasite occurring in all the dams. This was followed by <em>Trichodina </em>sp (Ciliophora) with total abundance value of 147(26.5%) in all the dams. <em>Acanthocephalan, Gyrodactylus </em>sp, <em>Dactylogyrus </em>sp, <em>Diphyllobothrium latum</em> and <em>Clinostomum </em>sp have abundance values of 26 (4.7%), 27(4.9%), 21(3.8%), 146 (26.4%) and 23(4.2%) respectively with <em>Dactylogyrus sp </em>(Monogenea) being least abundant, 21(3.8%), in all the dams. The overall parasitic prevalence from the different dams showed that Ado dam had the highest parasitic infection with 27.3% which suggests that Ado dam has the lowest water quality. There is need for constant surveillance in rivers and reservoirs to detect early signs of parasitic infection. Viable preventive measures against fish parasitism in Nigerian freshwater bodies are highly recommended.</p> Adewole S.O Odeyemi D.F Fatunwase O.P Christopher V.N Omoyeni T.E Dada A.O Copyright (c) 2019 Journal of Biomedical Engineering and Medical Imaging 2019-07-03 2019-07-03 6 2 01 07 10.14738/jbemi.62.5847 Modified Self-Organizing Map Algorithm for Brain Tumor Detection and Analysis Using Magnetic Resonance Brain Images <p>medical image processing play an important role to help radiologists and support their decisions in diagnosis of the patient, magnetic resonance imaging (MRI) has ability to diagnosis the small details in the human body with a high resolution; in this paper, we propose modified self-organizing map algorithm (MSOM) for brain tumor detection and analysis using magnetic resonance brain images the significance of the (MSOM) algorithm is ability to detect tumor area in the magnetic resonance brain image (MRI) clearly with a high accuracy and best performance according of different values, the advantage of method proposed can segment and detect different types of MRI brain images FLAIR, T1 and T2-weight&nbsp; images with same performance and accuracy, the (MSOM) method start through input magnetic resonance brain image (MRI) and preprocessing applied to remove the noise from the image, applied modified self-organizing map (MSOM), applied tumor area, performance of the method, finally the applied of modified self-organizing map (MSOM) gave a best results us shown in the results.&nbsp;</p> Isselmou Abd El kader Copyright (c) 2019 Isselmou Abd El kader 2019-07-03 2019-07-03 6 2 27 34 10.14738/jbemi.62.6657 Brain Tumor Segmentation through Region-based, Supervised and Unsupervised Learning Methods: A Literature Survey <p>Image segmentation is one of the most trending fields in the domain of digital image processing. For years, researchers have shown a remarkable progress in the field of Image Segmentation, precisely, for brain tumor extraction from various medical imaging modalities including X-Ray, Computed Tomography and most importantly, Magnetic Resonance Images (MRI). In these medical imaging modalities, accurate and reliable brain tumor segmentation is extremely imperative to perform safe diagnose, healthy treatment planning and consistent treatment outcome evaluation in order to understand and cure the complexities of chronic diseases such as Cancer. This paper presents various image processing techniques that are currently being used for brain tumor extraction from medical images. Though some great work has been done in this domain but none of the techniques has been widely accepted to be brought into practice in real time clinical analysis. The paper concludes with proposing some solutions that would aid in refining the results of the techniques which will lead to clinical acceptance of these computer aided methods.</p> Muhammad Zawish Asad Ali Siyal Shahzad Hyder Shahani Aisha Zahid Junejo Aiman Khalil Copyright (c) 2019 Muhammad Zawish, Asad Ali Siyal, Shahzad Hyder Shahani, Aisha Zahid Junejo, Aiman Khalil 2019-07-03 2019-07-03 6 2 08 26 10.14738/jbemi.62.6725