<span style="font-family:Verdana;">Detecting and segmenting the lung regions in chest X-ray images is an important part in artificial intelligence-based computer-aided diagnosis/detection (AI-CAD) syst...<span style="font-family:Verdana;">Detecting and segmenting the lung regions in chest X-ray images is an important part in artificial intelligence-based computer-aided diagnosis/detection (AI-CAD) systems for chest radiography. However, if the chest X-ray images themselves are used as training data for the AI-CAD system, the system might learn the irrelevant image-based information resulting in the decrease of system’s performance. In this study, we propose a lung region segmentation method that can automatically remove the shoulder and scapula regions, mediastinum, and diaphragm regions in advance from various chest X-ray images to be used as learning data. The proposed method consists of three main steps. First, employ the simple linear iterative clustering algorithm, the lazy snapping technique and local entropy filter to generate an entropy map. Second, apply morphological operations to the entropy map to obtain a lung mask. Third, perform automated segmentation of the lung field using the obtained mask. A total of 30 images were used for the experiments. In order to verify the effectiveness of the proposed method, two other texture maps, namely, the maps created from the standard deviation filtering and the range filtering, were used for comparison. As a result, the proposed method using the entropy map was able to appropriately remove the unnecessary regions. In addition, this method was able to remove the markers present in the image, but the other two methods could not. The experimental results have revealed that our proposed method is a highly generalizable and useful algorithm. We believe that this method might act an important role to enhance the performance of AI-CAD systems for chest X-ray images.</span>展开更多
Pest attack,disease incidence,and nutrient deficiency are the major factors limiting the yield of paddy.Therefore,the paper proposes a classification system for the identification of pest,disease,and nutrient deficien...Pest attack,disease incidence,and nutrient deficiency are the major factors limiting the yield of paddy.Therefore,the paper proposes a classification system for the identification of pest,disease,and nutrient deficiency classes.This approach initially preprocesses leaf images using entropy filtering followed by a leaf segmentation process.Multiple layers are then constructed on the leaf image through which features are extracted.The Gray Level Co-occurrence Matrix(GLCM)algorithm and Principal Component Analysis(PCA)are used to extract the global texture features of the leaf image.A 1D-signal sequence is constructed on each layer,which is decomposed by the Empirical Mode Decomposition algorithm from which the phase features are estimated.The features are trained/classified using the decision tree classifiers that classify the pest attack,disease incidence,and nutrient deficiency categories.The proposed approach provides a precision,accuracy,specificity,sensitivity,and F1-score of 97%,97.88%,96.52%,96.7%,and 96.7%respectively.展开更多
文摘<span style="font-family:Verdana;">Detecting and segmenting the lung regions in chest X-ray images is an important part in artificial intelligence-based computer-aided diagnosis/detection (AI-CAD) systems for chest radiography. However, if the chest X-ray images themselves are used as training data for the AI-CAD system, the system might learn the irrelevant image-based information resulting in the decrease of system’s performance. In this study, we propose a lung region segmentation method that can automatically remove the shoulder and scapula regions, mediastinum, and diaphragm regions in advance from various chest X-ray images to be used as learning data. The proposed method consists of three main steps. First, employ the simple linear iterative clustering algorithm, the lazy snapping technique and local entropy filter to generate an entropy map. Second, apply morphological operations to the entropy map to obtain a lung mask. Third, perform automated segmentation of the lung field using the obtained mask. A total of 30 images were used for the experiments. In order to verify the effectiveness of the proposed method, two other texture maps, namely, the maps created from the standard deviation filtering and the range filtering, were used for comparison. As a result, the proposed method using the entropy map was able to appropriately remove the unnecessary regions. In addition, this method was able to remove the markers present in the image, but the other two methods could not. The experimental results have revealed that our proposed method is a highly generalizable and useful algorithm. We believe that this method might act an important role to enhance the performance of AI-CAD systems for chest X-ray images.</span>
文摘Pest attack,disease incidence,and nutrient deficiency are the major factors limiting the yield of paddy.Therefore,the paper proposes a classification system for the identification of pest,disease,and nutrient deficiency classes.This approach initially preprocesses leaf images using entropy filtering followed by a leaf segmentation process.Multiple layers are then constructed on the leaf image through which features are extracted.The Gray Level Co-occurrence Matrix(GLCM)algorithm and Principal Component Analysis(PCA)are used to extract the global texture features of the leaf image.A 1D-signal sequence is constructed on each layer,which is decomposed by the Empirical Mode Decomposition algorithm from which the phase features are estimated.The features are trained/classified using the decision tree classifiers that classify the pest attack,disease incidence,and nutrient deficiency categories.The proposed approach provides a precision,accuracy,specificity,sensitivity,and F1-score of 97%,97.88%,96.52%,96.7%,and 96.7%respectively.
