Automatic Estimation of Artemia Hatching Rate Using an Object Discrimination Method
0
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
Digital image processing is a process to analyze a large volume of information on digital images. In this study, Artemia hatching rate was measured by automatically classifying and counting cysts and larvae based on color imaging data from cyst hatching experiments using an image processing technique. The Artemia hatching rate estimation consists of a series of processes; a step to convert the scanned image data to a binary image data, a process to detect objects and to extract their shape information in the converted image data, an analysis step to choose an optimal discriminant function, and a step to recognize and classify the objects using the function. The function to classify Artemia cysts and larvae is optimally estimated based on the classification performance using the areas and the plan-form factors of the detected objects. The hatching rate using the image data obtained under the different experimental conditions was estimated in the range of 34-48%. It was shown that the maximum difference is about 19.7% and the average root-mean squared difference is about 10.9% as the difference between the results using an automatic counting (this study) and a manual counting were compared. This technique can be applied to biological specimen analysis using similar imaging information.
Abstract
Digital image processing is a process to analyze a large volume of information on digital images. In this study, Artemia hatching rate was measured by automatically classifying and counting cysts and larvae based on color imaging data from cyst hatching experiments using an image processing technique. The Artemia hatching rate estimation consists of a series of processes; a step to convert the scanned image data to a binary image data, a process to detect objects and to extract their shape information in the converted image data, an analysis step to choose an optimal discriminant function, and a step to recognize and classify the objects using the function. The function to classify Artemia cysts and larvae is optimally estimated based on the classification performance using the areas and the plan-form factors of the detected objects. The hatching rate using the image data obtained under the different experimental conditions was estimated in the range of 34-48%. It was shown that the maximum difference is about 19.7% and the average root-mean squared difference is about 10.9% as the difference between the results using an automatic counting (this study) and a manual counting were compared. This technique can be applied to biological specimen analysis using similar imaging information.
참고문헌 (28)
- Arasan S, "Shape properties of natural and crushed aggregate using image analysis", Int J Eng Res Appl, 1(2), pp.221~227 (2010)
- Bates MC, "Low-cost computer-assisted image analysis for fisheries research", Prog Fish Cult, 59(3), pp.235~240 (1997)
- Baxes GA, "Digital image processing: A Practical Primer", pp.182~ (1984), Cascade Press
- Been TH, "Using image analysis for counting larvae of potato cyst nematodes (Globodera spp.)", Fund Appl Nematol, 19(3), pp.297~304 (1996)
- Brillon S, "Egg survival, embryonic development, and larvae characteristics of northern shrimp (Pandalus borealis) females object to different temperature and feeding conditions", Mar Biol, 147, pp.895~911 (2005)
- Brix KV, "Effects of copper, cadmium, and zinc on the hatching success of brine shrimp (Artermia franciscana)", Arch Environ Contam Toxicol, 51, pp.580~583 (2006)
- Brown LM, "Compter-based image analysis for the automated counting and morphological description of microalgae in culture", J Appl Phycol, 1, pp.211~225 (1989)
- Cadrin SX, "The utility of image processing techniques for morphometric analysis and stock identification", Fish Res, 43, pp.129~139 (1999)
- Friedland KD, "Automated egg counting and sizing from scanned images: Rapid sample processing and large data volumes for fecundity estimates", J Sea Res, 54, pp.307~316 (2005)
- Gonzalez RC, "Digital image processing", pp.503~ (1987), Addison-Wesley Pub. Co.
- Hu J, "Shape characterization of concrete aggregate", Acta Stereol, 25, pp.43~53 (2006)
- Imasogie BI, "Characterization of graphite particle shape in spheroidal graphite iron using a computer-based image analyzer", J Mineral Mater Charact Eng, 3(1), pp.1~12 (2004)
- Jiao L, "Characterizing land-use classes in remote sensing imagery by shape metrics", ISPRS J Photogramm, 72, pp.46~55 (2012)
- 김백운, "Image Processing for Video Images of buoy motion", Ocean Science Journal, 40(4), pp.213~220 (2005), 한국해양과학기술원
- Kim BO, "Nearshore wave measurement using single-video images of buoy motions", J Coastal Res, 24(6), pp.1481~1486 (2008)
- Letcher BH, "Effects of food density on growth and on patterns of prey depletion by larvae silverside fish, Menidia beryllina (Cope); a laboratory investigation with image analysis", J Exp Mar Biol Ecol, 167, pp.197~213 (1993)
- MathWorks, "Image processing toolboxTM User's Guide", The MathWorks, Inc, 596~ (2012)
- Onocha PA, "Chemical composition, brine shrimp toxicity and free-radical scavenging activity of leaf essential oil of Acalypha Ornata (Hochst)", Adv Environ Biol, 5, pp.188~193 (2011)
- Rhee PK, "Image processing and biometric recognition (In Korean)", pp.256~ (2005), Hongneung Science Pub.
- Rideout RM, "Effects of egg size, food supply and spawning time on early life history success of haddock Melanogrammus aeglefinus", Mar Ecol-Prog Ser, 285, pp.169~180 (2005)
- Umme Salma, "Effects of pH Change by CO2 Induction and Salinity on the Hatching Rate of Artemia franciscana", Fisheries and Aquatic Sciences, 15(2), pp.177~181 (2012), 한국수산과학회
- Sonka M, "Image processing, analysis, and machine vision. Second Edition", pp.770~ (1999), PWS Publishing
- Storbeck F, "Fish species recognition using computer vision and a neural network", Fish Res, 51, pp.11~15 (2001)
- Thorsen A, "A rapid method for estimation of oocyte size and potential fecundity in Atlantic cod using a computer-aided particle analysis system", J Sea Res, 46, pp.295~308 (2001)
- Treece GD, "Artemia production for marine larvae fish culture", Southern Regional Aquaculture Center, SPAC Publication No. 702, 4~ (2000)
- Tsuji T, "Automated identification of red tide phytoplankton Prorocentrum triestinum in coastal areas by image analysis", J Ocean Soc Jap, 40, pp.425~431 (1984)
- Wu J, "Scaling and uncertainty analysis in ecology: methods and applications", pp.352~ (2006), Springer
- Zorica B, "Preliminary data on the study of otolith morphology of five pelagic fish species from the Adriatic Sea (Croatia)", Acta Adriat, 51(1), pp.89~96 (2010)