An Intelligent Software for Measurements of Biological Materials: BioMorph
Abstract
Morphological characters have commonly been used in analysis of biological contexts. Researchers often use the arrangements of morphological landmarks in their studies to extract shape information from any biological materials and need to get bio-measurements using any computer aided tools. Getting landmarks and measurements from biological materials are a time-consuming process. Hence, this study is to provide an intelligent integrated software called BioMorph for morphological measurements. With the BioMorph, Family and species identification of a studied bio-object are automatically be determined using artificial neural network and k-nearest neighbor. The landmarks for discrimination of the bio-objects are automatically found from the given image using artificial neural network. In addition, network analysis methods such as the Euclid network distances, Truss network distances, Triangular network distances, some statistical measures such as mean, standard deviation, minimum and maximum values, etc. and image processing techniques such as image editing, image filtering, image segmentation, etc. are also integrated to the BioMorph.
Keywords
References
- Bookstein, F.L. (1997). Landmark methods for forms without landmarks: morphometrics of group differences in outline shape. Medical Image Analysis, 1 (3), 225-43.
- Breno, M., Leirs, H. & Van Dongen, S. (2011). Traditional and geometric morphometrics for studying skull morphology during growth in Mastomys natalensis (Rodentia: Muridae). Journal of Mammalogy, 92 (6), 1395-1406.
- Duda, R. O., Hart, P. E., & Stork, D. G. (2012). Pattern classification. Wiley-Interscience Publication. John Wiley & Sons. 680p.
- Hockaday, S., Beddow, T.A., Stone, M., Hancock, P., & Ross, L.G. (2000). Using truss networks to estimate the biomass of Oreochromis niloticus, and to investigage shape characteristics. Journal of Fish Biology, 57: 981-1000.
- Hossain, M. A., Nahiduzzaman, M., Saha, D., Khanam, M. U. H. & Alam, M. S. (2010). Landmark-based morphometric and meristic variations of the endangered Carp, Kalibaus Labeo calbasu, from stocks of two isolated rivers, the Jamuna and Halda, and a hatchery. Zoological studies, 49 (4), 556-563.
- İşçimen, B., Kutlu, Y. & Turan, C. (2017a). Classification of Serranidae Species Using Color Based Statistical Features. Natural and Engineering Sciences, 2 (1), 25-34.
- İşçimen, B., Kutlu, Y. & Turan, C. (2017b). Classification of Fish Families Using Texture Analysis. The 3rd International Symposium on EuroAsian Biodiversity (SEAB-2017), 23.
- İşçimen, B., Kutlu, Y. & Turan, C. (2017c). Performance Comparison of Different Sized Regions of Interest on Fish Classification. International Conference on Engineering Technologies, 222-227.
Details
Primary Language
English
Subjects
Computer Software
Journal Section
Research Article
Publication Date
May 17, 2018
Submission Date
December 21, 2017
Acceptance Date
May 4, 2018
Published in Issue
Year 2018 Volume: 3 Number: 2
Cited By
Constructing Domain Ontology for Alzheimer Disease Using Deep Learning Based Approach
Electronics
https://doi.org/10.3390/electronics11121890