被毛对热成像检测生猪体表温度精度的影响及噪声滤除方法
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摘要
生猪皮肤的温度分布是表征其生理状态和疾病的重要指标,通常由红外热成像技术(infrared thermography, IRT)检测,然而由于生猪体表附有被毛在热图像中产生大量的温度噪声,降低了IRT对皮肤温度的检测精度。该文针对此问题探索被毛对皮肤温度分布的影响规律,并设计消除被毛影响的热图像降噪算法,提高对温度分布的检测精度。通过对12头生猪试验,分析目标区域在正常被毛和剔除被毛后温度分布的统计量得出被毛在温度分布中产生大量的"峡谷"状低温噪声,显著降低了目标区域的最低温度及平均温度。根据毛发噪声的影响规律提出网格化最大值-双三次插值算法并确定算法的最佳邻域尺寸为4.25mm。采用均方误差、峰值信噪比等指标定量评价算法的有效性,结果表明经算法处理后,均方误差由0.38下降到0.05(P<0.01),峰值信噪比由45.14 dB上升到53.66 dB(P<0.01),说明该算法能够滤除热图像中毛发引起的噪声,可提高IRT对温度分布的检测精度。
The temperature distribution of pig skin is an important indicator to characterize its physiological state and disease. However, due to the surface hair coat, the skin temperature accuracy which detected by infrared thermography(IRT) is affected and its ability to diagnosis of fever and disease is reduced. The purpose of this paper is to explore the influence patterns of the coat on the skin temperature distribution and propose the thermal image processing method to eliminate the influence of the coat on temperature accuracy. The animals for experimental data were 12 sows in empty pregnant period with the average ambient temperature of 27.4 ℃ and humidity in the piggery of 80.3% respectively. The body surface temperature was measured by hand-held infrared thermal imager(Fluke, Ti 300) with a resolution of 240 pixels×180 pixels and sensitivity of 50 mK. And it also carried a laser distance measuring sensor with a resolution of 0.01 m to measure the distance between the measured object and the thermal imager. The statistics of the temperature distribution detected by IRT from the region of interest(ROI) under normal coat(NC) was compared to that under shed coat(SC) state. The statistical data indicated that the hair coat produced a large number of "canyon"-like low temperature noise in temperature distribution in NC state, which reduced the minimum temperature and average temperature of the ROI, but had no significant effect on the maximum temperature with diagnostic ability. According to the noise distribution characteristics and the influence pattern, an image noise filtering algorithm named the grid maximum value bicubic interpolation filter(GMBI) was proposed. The GMBI algorithm consisted of three steps including image mesh segmentation, filtering with maximum value and image bicubic interpolation. The key problem of GMBI was how to select the appropriate neighborhood size to ensure that each block contained at least one skin temperature value and the resolution was as high as possible. In this study, mathematical statistics was employed and it was found out that the optimal neighborhood size was 4.25 mm. In order to evaluate the validity of the algorithm quantitatively, the mean square error(MSE), peak signal-to-noise ratio(PSNR) and the difference of maximum, minimum and mean between the processed images by GMBI and the SC thermal images were calculated. The experimental data showed that the differences of minimum and average were greatly reduced from the original 1.59 and 0.47 to 0.13 and 0.07 ℃(P<0.01), which both were within the maximum allowable error range(±0.3 ℃) for disease diagnosis. Moreover, the MSE decreased from 0.38 to 0.05(P<0.01), while PSNR increased from 45.14 dB to 53.66 dB. In conclusion, the GMBI purposed in this study can filter the majority of noise caused by hair in temperature distribution and significantly improve skin temperature detection accuracy.
The temperature distribution of pig skin is an important indicator to characterize its physiological state and disease. However, due to the surface hair coat, the skin temperature accuracy which detected by infrared thermography(IRT) is affected and its ability to diagnosis of fever and disease is reduced. The purpose of this paper is to explore the influence patterns of the coat on the skin temperature distribution and propose the thermal image processing method to eliminate the influence of the coat on temperature accuracy. The animals for experimental data were 12 sows in empty pregnant period with the average ambient temperature of 27.4 ℃ and humidity in the piggery of 80.3% respectively. The body surface temperature was measured by hand-held infrared thermal imager(Fluke, Ti 300) with a resolution of 240 pixels×180 pixels and sensitivity of 50 mK. And it also carried a laser distance measuring sensor with a resolution of 0.01 m to measure the distance between the measured object and the thermal imager. The statistics of the temperature distribution detected by IRT from the region of interest(ROI) under normal coat(NC) was compared to that under shed coat(SC) state. The statistical data indicated that the hair coat produced a large number of "canyon"-like low temperature noise in temperature distribution in NC state, which reduced the minimum temperature and average temperature of the ROI, but had no significant effect on the maximum temperature with diagnostic ability. According to the noise distribution characteristics and the influence pattern, an image noise filtering algorithm named the grid maximum value bicubic interpolation filter(GMBI) was proposed. The GMBI algorithm consisted of three steps including image mesh segmentation, filtering with maximum value and image bicubic interpolation. The key problem of GMBI was how to select the appropriate neighborhood size to ensure that each block contained at least one skin temperature value and the resolution was as high as possible. In this study, mathematical statistics was employed and it was found out that the optimal neighborhood size was 4.25 mm. In order to evaluate the validity of the algorithm quantitatively, the mean square error(MSE), peak signal-to-noise ratio(PSNR) and the difference of maximum, minimum and mean between the processed images by GMBI and the SC thermal images were calculated. The experimental data showed that the differences of minimum and average were greatly reduced from the original 1.59 and 0.47 to 0.13 and 0.07 ℃(P<0.01), which both were within the maximum allowable error range(±0.3 ℃) for disease diagnosis. Moreover, the MSE decreased from 0.38 to 0.05(P<0.01), while PSNR increased from 45.14 dB to 53.66 dB. In conclusion, the GMBI purposed in this study can filter the majority of noise caused by hair in temperature distribution and significantly improve skin temperature detection accuracy.
引文
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[22]王海超,王春光,宗哲英,等.基于噪声类型及强度估计的狭叶锦鸡儿叶切片图像盲去噪[J].农业工程学报,2017,33(10):229-238.Wang Haichao,Wang Chunguang,Zong Zheying,et al.Blind image denoising of microscopic slices image of Caraganastenophylla Pojark based on noise type and intensity estimation[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2017,33(10):229-238.(in Chinese with English abstract)
[23]廖建尚,王立国,郝思媛.基于双边滤波和空间邻域信息的高光谱图像分类方法[J].农业机械学报,2017,48(8):140-146,211.Liao Jianshang,Wang Liguo,Hao Siyuan.Hyperspectral image classification method combined with bilateral filtering and pixel neighborhood information[J].Transactions of the Chinese Society for Agricultural Machinery,2017,48(8):140-146,211.(in Chinese with English abstract)
[24]钟宝江,陆志芳,季家欢.图像插值技术综述[J].数据采集与处理,2016,31(6):1083-1096.Zhong Baojiang,Lu Zhifang,Ji Jiahuan.Review on image interpolation techniques[J].Journal of Data Acquisition and Processing,2016,31(6):1083-1096.(in Chinese with English abstract)
[25]Keys R G.Cubic convolution interpolation for digital image processing[J].IEEE Transactions on Acoustics Speech and Signal Processing,1981,29(6):1153-1160.
[26]张玉存,张喜英,付献斌,等.基于小波与双三次插值的高温锻件红外图像增强方法[J].中国机械工程,2017,28(17):2095-2099.Zhang Yucun,Zhang Xiying,Fu Xianbin,et al.Infrared image enhancement algorithm for hot forgings based on wavelet transform and bicubic interpolation[J].China Mechanical Engineering,2017,28(17):2095-2099.(in Chinese with English abstract)
[27]王丽杰,杨羽翼,代敏,等.基于直方图分层映射的近红外光谱预处理算法[J].激光与光电子学进展,2017,54(9):393-401.Wang Lijie,Yang Yuyi,Dai Min,et al.Near infrared spectral pre-processing algorithm based on histogram layering mapping[J].Laser&Optoelectronics Progress,2017,54(9):393-401.(in Chinese with English abstract)
[28]刘姗姗,白美健,许迪,等.畦田灌溉模拟中田面微地形空间分布插值方法改进[J].农业工程学报,2015,31(17):108-114.Liu Shanshan,Bai Meijian,Xu Di,et al.Improvement of interpolation methods for surface micro-topography spatial distribution in border irrigation simulation[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2015,31(17):108-114.(in Chinese with English abstract)
[29]黄小乔,石俊生,杨健,等.基于色差的均方误差与峰值信噪比评价彩色图像质量研究[J].光子学报,2007,36(S1):295-298.Huang Xiaoqiao,Shi Junsheng,Yang Jian,et al.Study on color image quality evaluation by MSE and PSNR based on color difference[J].Acta Photonica Sinica,2007,36(S1):295-298.(in Chinese with English abstract)
[30]肖祥元,景文博,赵海丽.基于峰值信噪比改进的图像增强算法[J].长春理工大学学报:自然科学版,2017,40(4):83-86,92.Xiao Xiangyuan,Jing Wenbo,Zhao Haili.An improved image enhancement algorithm based on the peak-signal to noise ratio[J].Journal of Changchun University of Science and Technology:Natural Science Edition,2017,40(4):83-86,92.(in Chinese with English abstract)
[2]Caldara F R,dos Santos L S,Machado S T,et al.Piglets’surface temperature change at different weights at Birth[J]Asian-Australasian Journal of Animal Sciences,2014,27(3):431-438.
[3]Sathiyabarathi M,Jeyakumar S,Manimaran A,et al.Infrared thermography:A potential noninvasive tool to monitor udder health status in dairy cows[J].Veterinary World,2016,9(10):1075-1081.
[4]Menzel A,Beyerbach M,Siewert C,et al.Actinobacillus pleuropneumoniae challenge in swine:Diagnostic of lung alterations by infrared thermography[J].BMC Veterinary Research,2014,10(1):199.
[5]Simoes V G,Lyazrhi F,Picard-Hagen N,et al.Variations in the vulvar temperature of sows during proestrus and estrus as determined by infrared thermography and its relation to ovulation[J].Theriogenology,2014,82(8):1080-1085.
[6]Luno V,Gil L,Jerez R A,et al.Determination of ovulation time in sows based on skin temperature and genital electrical resistance changes[J].Veterinary Record,2013,172(22):579.
[7]Traulsen I,Naunin K,Mueller K,et al.Application of infrared thermography to measure body temperature of sows[J].Zuchtungskunde,2010,82(6):437-446.
[8]Chung T H,Jung W S,Nam E H,et al.Comparison of rectal and infrared thermometry for obtaining body temperature of gnotobiotic piglets in conventional portable germ free facility[J].Asian-Australasian Journal of Animal Sciences,2010,23(10):1364-1368.
[9]李春华,王英,蒋凤英,等.猪伪狂犬病研究进展[J].动物医学进展,2008,29(3):68-72.Li Chunhua,Wang Ying,Jiang Fengying,et al.Progress on porcine pseudorabies[J].Progress in Veterinary Medicine,2008,29(3):68-72.(in Chinese with English abstract)
[10]陈焕春.猪系统性疾病的流行现状与防控措施[J].饲料与畜牧,2018(2):45-50.
[11]Stewart M,Webster J R,Schaefer A L,et al.Infrared thermography as a non-invasive toot to study animal welfare[J].Animal Welfare,2005,14(4):319-325.
[12]Petry A,McGilvray W,Rakhshandeh A R,et al.Technical note:Assessment of an alternative technique for measuring body temperature in pigs[J].Journal of Animal Science,201795(7):3270-3274.
[13]曹哲,施正香,安欣,等.基于热成像技术的牛舍围护结构传热阻测试方法[J].农业工程学报,2017,33(24):235-241.Cao Zhe,Shi Zhengxiang,An Xin,et al.Evaluation on measure method of heat transfer resistance for enveloped structure of cattle barn based on infrared imaging method[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2017,33(24):235-241.(in Chinese with English abstract)
[14]Usamentiaga R,Venegas P,Guerediaga J,et al.Infrared thermography for temperature measurement and non-destructive testing[J].Sensors,2014,14(7):12305-12348.
[15]Lahiri B B,Bagavathiappan S,Jayakumar T,et al.Medical applications of infrared thermography:A review[J].Infrared Physics&Technology,2012,55(4):221-235.
[16]Sapkota A,Herr A,Johnson J S,et al.Core body temperature does not cool down with skin surface temperature during recovery at room temperature after acute heat stress exposure[J].Livestock Science,2016,191:143-147.
[17]Menzel A,Siewert C,Gasse H,et al.Infrared thermography of the pig thorax:An assessment of selected regions of interest by computed tomographical and anatomical parameters[J].AnatomiaHistologiaEmbryologia,2015,44(2):107-117.
[18]Bekkering J,Hoy S.Continuous monitoring of ear temperature in boars[J].Dtw Deutsche Tierarztliche Wochenschrift,2007,114(1):16-19.
[19]Zhang K,Jiao L,Zhao X,et al.An instantaneous approach for determining the infrared emissivity of swine surface and the influencing factors[J].Journal of Thermal Biology,2016,57:78-83.
[20]Soerensen D D,Clausen S,Mercer J B,et al.Determining the emissivity of pig skin for accurate infrared thermography[J].Computers and Electronics in Agriculture,2014,109:52-58.
[21]贾伟宽,赵德安,阮承治,等.苹果采摘机器人夜间图像降噪算法[J].农业工程学报,2015,31(10):219-226.Jia Weikuan,Zhao Dean,Ruan Chengzhi,et al.De-noising algorithm of night vision image for apple harvesting robot[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2015,31(10):219-226.(in Chinese with English abstract)
[22]王海超,王春光,宗哲英,等.基于噪声类型及强度估计的狭叶锦鸡儿叶切片图像盲去噪[J].农业工程学报,2017,33(10):229-238.Wang Haichao,Wang Chunguang,Zong Zheying,et al.Blind image denoising of microscopic slices image of Caraganastenophylla Pojark based on noise type and intensity estimation[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2017,33(10):229-238.(in Chinese with English abstract)
[23]廖建尚,王立国,郝思媛.基于双边滤波和空间邻域信息的高光谱图像分类方法[J].农业机械学报,2017,48(8):140-146,211.Liao Jianshang,Wang Liguo,Hao Siyuan.Hyperspectral image classification method combined with bilateral filtering and pixel neighborhood information[J].Transactions of the Chinese Society for Agricultural Machinery,2017,48(8):140-146,211.(in Chinese with English abstract)
[24]钟宝江,陆志芳,季家欢.图像插值技术综述[J].数据采集与处理,2016,31(6):1083-1096.Zhong Baojiang,Lu Zhifang,Ji Jiahuan.Review on image interpolation techniques[J].Journal of Data Acquisition and Processing,2016,31(6):1083-1096.(in Chinese with English abstract)
[25]Keys R G.Cubic convolution interpolation for digital image processing[J].IEEE Transactions on Acoustics Speech and Signal Processing,1981,29(6):1153-1160.
[26]张玉存,张喜英,付献斌,等.基于小波与双三次插值的高温锻件红外图像增强方法[J].中国机械工程,2017,28(17):2095-2099.Zhang Yucun,Zhang Xiying,Fu Xianbin,et al.Infrared image enhancement algorithm for hot forgings based on wavelet transform and bicubic interpolation[J].China Mechanical Engineering,2017,28(17):2095-2099.(in Chinese with English abstract)
[27]王丽杰,杨羽翼,代敏,等.基于直方图分层映射的近红外光谱预处理算法[J].激光与光电子学进展,2017,54(9):393-401.Wang Lijie,Yang Yuyi,Dai Min,et al.Near infrared spectral pre-processing algorithm based on histogram layering mapping[J].Laser&Optoelectronics Progress,2017,54(9):393-401.(in Chinese with English abstract)
[28]刘姗姗,白美健,许迪,等.畦田灌溉模拟中田面微地形空间分布插值方法改进[J].农业工程学报,2015,31(17):108-114.Liu Shanshan,Bai Meijian,Xu Di,et al.Improvement of interpolation methods for surface micro-topography spatial distribution in border irrigation simulation[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2015,31(17):108-114.(in Chinese with English abstract)
[29]黄小乔,石俊生,杨健,等.基于色差的均方误差与峰值信噪比评价彩色图像质量研究[J].光子学报,2007,36(S1):295-298.Huang Xiaoqiao,Shi Junsheng,Yang Jian,et al.Study on color image quality evaluation by MSE and PSNR based on color difference[J].Acta Photonica Sinica,2007,36(S1):295-298.(in Chinese with English abstract)
[30]肖祥元,景文博,赵海丽.基于峰值信噪比改进的图像增强算法[J].长春理工大学学报:自然科学版,2017,40(4):83-86,92.Xiao Xiangyuan,Jing Wenbo,Zhao Haili.An improved image enhancement algorithm based on the peak-signal to noise ratio[J].Journal of Changchun University of Science and Technology:Natural Science Edition,2017,40(4):83-86,92.(in Chinese with English abstract)