Poultry Skin Tumor Detection in Hyperspectral Images Using Radial Basis Probabilistic Neural Network

Kim, Intaek; Xu, Chengzhe; Kim, Moon S.

doi:10.1007/11760191_113

Cited by 6 publications

(7 citation statements)

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“…It demonstrates that the result of feature fusion outperforms the best individual classification result. To evaluate the performance of skin tumor detection in hyperspectral image, we compared the classification result with those in [3], [4], and [5] in Table 2. It should be noted that PCA method for feature extraction and SVM classifier was implemented in [3], while RBPNN classifier with band ratio feature extraction method was adopted in [4], the more recent paper [5] used classifier combination method for several features extracted by PCA, DWT, and KDA.…”

Section: Resultsmentioning

confidence: 99%

“…To evaluate the performance of skin tumor detection in hyperspectral image, we compared the classification result with those in [3], [4], and [5] in Table 2. It should be noted that PCA method for feature extraction and SVM classifier was implemented in [3], while RBPNN classifier with band ratio feature extraction method was adopted in [4], the more recent paper [5] used classifier combination method for several features extracted by PCA, DWT, and KDA. From table 2, it is evident that classifier combination method and feature fusion method is significantly reduce the classification error rather than using good performance classifier with good feature, it is also shows that fusion features extracted from different datasets outperform classifier combination for several features from same datasets.…”

Section: Resultsmentioning

confidence: 99%

“…To assure food safety, detection of chicken skin tumor using multispectral reflectance imaging has been reported to have the ability to differentiate wholesome and unwholesome agricultural products [2]. At the same time, studies on detecting chicken skin tumor using hyperspectral imaging have also carried out actively with the data acquired in the USDA Instrumentation and Sensing Laboratory (ISL) [3,4]. More recently, classifier combination technique for several features has been proposed to detect skin tumors on poultry carcasses using hyperspectral reflectance images [5].…”

Section: Introductionmentioning

confidence: 99%

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Feature level fusion for hyperspectral images

Kim

Kong

2009

SPIE Proceedings

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This paper presents a new method for detecting poultry skin tumors based on serial feature fusion in hyperspectral images. First, some transform methods, including principal component analysis, discrete wavelet transform and band ratio method, are used to generate largely independent datasets in the hyperspectral fluorescence images. Then, the kernel discriminant analysis is utilized to extract features from each represented dataset for the purpose of classification; another set of features are extracted from hyperspectral reflectance images by using kernel discriminant analysis. Finally, new fused features are made by combining aforementioned features. The experimental result based on the proposed method shows the better performance in detecting tumors compared with previous works.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Feature level fusion for hyperspectral images

Kim

Kong

2009

SPIE Proceedings

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“…The established model was able to identify 91% and 86% of normal and tumor tissue, respectively. Similarly, Kim and others () presented a hyperspectral fluorescence imaging system for detecting poultry skin tumors, achieving a classification rate of 98.2% based on neural network using 4 feature images.…”

Section: Evaluation Methodologies and Applicationsmentioning

confidence: 99%

Recent Advances in Data Mining Techniques and Their Applications in Hyperspectral Image Processing for the Food Industry

Dai

Sun

Xiong

et al. 2014

Comp Rev Food Sci Food Safe

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Hyperspectral imaging (HSI) facilitates better characterization of intrinsic and extrinsic properties of foods by integrating traditional spectral and image techniques, in which careful and sophisticated data processing plays an important role. In the past decade, much progress has been made on applying various algorithms to deal with hyperspectral images. This review first introduces the general procedure of hyperspectral data analysis and then illustrates the most typically and commonly used algorithms for denoising, feature selection, model establishment, and evaluation, as well as their applications for assessing food quality, safety, and authenticity. Finally, brief summaries for regression and classification methods are presented. This article will provide a guideline for data mining in the future development of HSI in the food field.

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“…It can provide a full database with internal and external features of the samples [10] , and different varieties of objectives may have various external and internal characteristics, such as color, texture and nutrition content, directly resulting in the spectral signature differently. The spectral signature from a certain pixel of the image is useful for the discrimination and classification of the objectives [11] . This technique has been applied in variety classification in many previous studies.…”

Section: Introduction mentioning

confidence: 99%

Modeling for mung bean variety classification using visible and near-infrared hyperspectral imaging

Xie

2018

International Journal of Agricultural and Biological Engineering

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This study was carried out to investigate the feasibility of using visible and near infrared hyperspectral imaging for the variety classification of mung beans. Raw hyperspectral images of mung beans were acquired in the wavelengths of 380-1023 nm, and all images were calibrated by the white and dark reference images. The spectral reflectance values were extracted from the region of interest (ROI) of each calibrated hyperspectral image, and then they were treated as the independent variables. The dependent variables of four varieties of mung beans were set as 1, 2, 3 and 4, respectively. The extreme learning machine (ELM) model was established using full spectral wavelengths for classification. Modified gram-schmidt (MGS) method was used to identify effective wavelengths. Based on the selected wavelengths, the ELM and linear discriminant analysis (LDA) models were built. All models performed excellently with the correct classification rates (CCRs) covering 99.17%-99.58% in the training sets and 99.17%-100% in the testing sets. Fifteen wavelengths (432 nm,

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Poultry Skin Tumor Detection in Hyperspectral Images Using Radial Basis Probabilistic Neural Network

Cited by 6 publications

References 6 publications

Feature level fusion for hyperspectral images

Feature level fusion for hyperspectral images

Recent Advances in Data Mining Techniques and Their Applications in Hyperspectral Image Processing for the Food Industry

Modeling for mung bean variety classification using visible and near-infrared hyperspectral imaging

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