- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Severity analysis of diabetic retinopathy in retinal images using hybrid structure descriptor and modified CNNs
摘要： Imaging which plays a central role in the diagnosis and treatment planning of diabetic retinopathy and severity is an important diagnostic indicator in treatment planning and results assessment. Retinal image classification is an increasing attention among researchers in the field of computer vision, as it plays an important role in disease diagnosis. Computer Aided Diagnosis (CAD) is in wide practice in clinical work for the location and anticipation of different kinds of variations; the automated image classification systems used for such applications must be significantly efficient in terms of accuracy since false detection may lead to fatal results. Another requirement is the high convergence rate which accounts for the practical feasibility of the system. The overall classification accuracy of the proposed HTF with MCNNs is 98.41%, but the existing methods HTF with SVM and HTF with CNNs produce 97.84% and 96.65% respectively.
关键词： Segmentation,SVM,Medical image processing,Microaneurysms,Diabetic retinopathy,Classification
更新于2025-09-23 15:19:57
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Unsupervised evaluation-based region merging for high resolution remote sensing image segmentation
摘要： Image segmentation has a remarkable influence on the classification accuracy of object-based image analysis. Accordingly, how to raise the performance of remote sensing image segmentation is a key issue. However, this is challenging, primarily because it is difficult to avoid over-segmentation errors (OSE) and under-segmentation errors (USE). To solve this problem, this article presents a new segmentation technique by fusing a region merging method with an unsupervised segmentation evaluation technique called under- and over-segmentation aware (UOA), which is improved by using edge information. Edge information is also used to construct the merging criterion of the proposed approach. To validate the new segmentation scheme, five scenes of high resolution images acquired by Gaofen-2 and Ziyuan-3 multispectral sensors are chosen for the experiment. Quantitative evaluation metrics are employed in the experiment. Results indicate that the proposed algorithm obtains the lowest total error (TE) values for all test images (0.3791, 0.1434, 0.7601, 0.7569, 0.3169 for the first, second, third, fourth, fifth image, respectively; these values are averagely 0.1139 lower than the counterparts of the other methods), as compared to six state-of-the-art region merging-based segmentation approaches, including hybrid region merging, hierarchical segmentation, scale-variable region merging, size-constrained region merging with edge penalty, region merging guided by priority, and region merging combined with the original UOA. Moreover, the performance of the proposed method is better for artificial-object-dominant scenes than the ones mainly covering natural geo-objects.
关键词： region merging,high resolution remote sensing image,image segmentation,over- and under-segmentation aware,unsupervised evaluation
更新于2025-09-19 17:15:36
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Thermal face segmentation based on circular shortest path
摘要： Recently, research on thermal infrared human face analysis grows rapidly. However, due to the low contrast and limited information, human face in thermal infrared images is difficult to segment precisely. To overcome these shortcomings of thermal images, we propose an improved circular shortest path method in this paper. In our method, the proposed gradient based cost function enhances the gradient information and extracts detail of the original image. In addition, we propose a shape constraint by using ellipse derivative in the cost function. The constraint helps the contour to conform to the real human face. Moreover, the proposed certainty penalty term and straight path penalty term restrain the effect of the local minima regions and improve the robustness of our method. Our method could effectively extract the precise human face contour and thus segment the complete human face. Experimental results show that our method performs well for thermal infrared face segmentation, in both visual and quantitative ways.
关键词： thermal infrared face segmentation,shape constraint,Image segmentation,circular shortest path
更新于2025-09-19 17:15:36
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Another look on region merging procedure from seed region shift for high-resolution remote sensing image segmentation
摘要： Region merging method is widely used for remote sensing image segmentation in Geographic Object-Based Image Analysis (GEOBIA) because of its simplicity and effectiveness. Instead of improving the merging strategy, similarity measure, and stopping rule for region merging method as usual, we aim at exploring the effectiveness of the seed region shift on region merging-based segmentation. Different region merging procedures with different seed region shift frequencies are compared by fixing other conditions, demonstrating that the shift of seed regions serves as one of the key impacts to segmentation accuracy for region merging method. If the seed regions keep fixed during region merging procedure, it will lead to uneven expansion of regions and consequently low segmentation accuracy. However, if the seed regions can be dynamically shifted during region merging procedure, it will lead to even expansion of regions and achieve similar segmentation performance for different region merging strategies. The findings could be beneficial to selecting or further improving image segmentation method for GEOBIA.
关键词： High-resolution remote sensing,Geographic object-based image analysis,Region merging,Seed region,Image segmentation
更新于2025-09-19 17:15:36
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Secure medical image steganography through optimal pixel selection by EH-MB pipelined optimization technique
摘要： In today’s world, transmission of information over the channel is not secure for example patient records and other sensitive information. In order to protect this sensitive information, it is coded within the image, audio or text files which is decodable only with the help of a particular key. To enable security to the covert communication and safeguarding the information for securing medical data to avoid medical related cybercrimes, we have proposed a method for medical image steganography using Elephant Herding-Monarch Butterfly (EH-MB) Optimization algorithm for effective selection of pixels for embedding the secret message (i.e. image/text medical report data) in the cover image. Initially, the cover is converted to frequency domain using multilevel DWT, where, the pixel selection is done optimally in the high frequency components using EH-MB algorithm. EH-MB based pixel selection procedure uses a fitness function that depends on the cost function, which calculates the edge, entropy, and intensity of the pixel for evaluating fitness. Simulation was done in the working platform of MATLAB and comparison of the proposed steganography approach was done with the other existing methods in terms of Peak-Signal-to Noise-Ratio and Mean Square Error to prove the effectiveness of the proposed approach.
关键词： Intensity,Elephant herding,Steganography,Object,MSE,PSNR,Entropy,Covert,Segmentation,Edge,Monarch butterfly
更新于2025-09-19 17:15:36
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[IEEE 2018 International Conference on Platform Technology and Service (PlatCon) - Jeju (2018.1.29-2018.1.31)] 2018 International Conference on Platform Technology and Service (PlatCon) - Classification of Daytime and Night Based on Intensity and Chromaticity in RGB Color Image
摘要： Classification of daytime and night in the color image is a very important task in image processing based on color images acquired from CCTV. Also, weather classification must be performed before performing image processing such as weather report, shadow removal and fog detection. In this paper, we proposed the classification, whether a color image is daytime or night. We first set the range of pixels in the gray level image from 0 to 50, from 51 and over 101, and we estimated each range as daytime, evening and night. In the first step, it is estimated based on the intensity and chromaticity of the image. If the classification result based on the intensity and chromaticity image is the same, the process is terminated. Otherwise, the k-means segmentation is used in the second step to determine the final classification. Some experiments are conducted so as to verify the proposed method, and the classification is well performed. The execution time results up to the first step are about 0.31 seconds on average, and the execution up to the second step is changed according to the resolution of the image.
关键词： daytime and night,k-means segmentation,intensity,classification,chromaticity
更新于2025-09-19 17:15:36
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A learning-based automatic segmentation and quantification method on left ventricle in gated myocardial perfusion SPECT imaging: A feasibility study
摘要： Background. The performance of left ventricular (LV) functional assessment using gated myocardial perfusion SPECT (MPS) relies on the accuracy of segmentation. Current methods require manual adjustments that are tedious and subjective. We propose a novel machine-learning-based method to automatically segment LV myocardium and measure its volume in gated MPS imaging without human intervention. Methods. We used an end-to-end fully convolutional neural network to segment LV myocardium by delineating its endocardial and epicardial surface. A novel compound loss function, which encourages similarity and penalizes discrepancy between prediction and training dataset, is utilized in training stage to achieve excellent performance. We retrospectively investigated 32 normal patients and 24 abnormal patients, whose LV myocardial contours automatically segmented by our method were compared with those delineated by physicians as the ground truth. Results. The results of our method demonstrated very good agreement with the ground truth. The average DSC metrics and Hausdorff distance of the contours delineated by our method are larger than 0.900 and less than 1 cm, respectively, among all 32 + 24 patients of all phases. The correlation coefficient of the LV myocardium volume between ground truth and our results is 0.910 ± 0.061 (P < 0.001), and the mean relative error of LV myocardium volume is -1.09 ± 3.66%. Conclusion. These results strongly indicate the feasibility of our method in accurately quantifying LV myocardium volume change over the cardiac cycle. The learning-based segmentation method in gated MPS imaging has great promise for clinical use.
关键词： Myocardial perfusion,machine learning,segmentation,SPECT
更新于2025-09-19 17:15:36
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[IEEE 2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR) - Bangalore, India (2017.12.27-2017.12.30)] 2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR) - Segmentation of Natural Image Based on Colour Cohesion and Spatial Criteria
摘要： Segmenting a natural image is a complex task. Different semantic units may share similar visual features. On the other hand, such features can have variations even within a single unit. Proposed methodology relies on colour cohesion and spatial relationship between the components with cohesive colour. At ?rst image colour space is clustered to map the original colour to a reduced set. Number of cluster is automatically detected by analyzing the intensity histograms of the colour channels. Based on the similarity in terms of mapped colours, pixels are grouped. Subsequently, the spatial inclusiveness criteria is considered to merge the pixels groups where one group is contained within another. Finally, an attempt is made to merge the adjacent regions based on colour gradient. Colour cohesion is conceptualized by the process of colour space clustering, grouping of pixels in terms of colour similarity and region merging based on colour gradient. The spatial criteria is taken into account in terms of spatial inclusiveness at intermediate level and adjacency at ?nal stage. Proposed methodology is tested on Berkley segmentation dataset. Performance comparison with few other methodologies indicates the effectiveness of proposed methodology.
关键词： Colour space clustering,Segmentation,Spatial inclusiveness,Graph based merging
更新于2025-09-19 17:15:36
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Shipnet for Semantic Segmentation on VHR Maritime Imagery
摘要： For VHR maritime images, sematic segmentation is a new research hotspot and plays an important role in coast line navigation, resource management and territory protection. Without enough labeled training data, it is a challenge to separate small objects on a large scale while segment the big area clearly. To deal with it, we propose a novel ShipNet and design a weighted loss function for simultaneous sea-land segmentation and ship detection. To prove the proposed method, we also built and opened a new dataset to the community which contains VHR multiscale maritime images. Compared with the FCN and ResNet, the proposed method got much better F1 scores 85.90% for ship class and 97.54% overall accuracy. Compared with multiscale FCN, the ShipNet could obtain details results like sharp edges. Even for images with bad quality, the ShipNet could also keep robust and get good results.
关键词： CNN,ship detection,Sea-land segmentation,remote sensing image
更新于2025-09-19 17:15:36
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[IEEE 2018 Eighth International Conference on Image Processing Theory, Tools and Applications (IPTA) - Xi'an, China (2018.11.7-2018.11.10)] 2018 Eighth International Conference on Image Processing Theory, Tools and Applications (IPTA) - Human-Computer Interaction using Finger Signing Recognition with Hand Palm Centroid PSO Search and Skin-Color Classification and Segmentation
摘要： This paper presents a novel image processing technique for recognizing finger signs language alphabet. A human-computer interaction system is built based on the recognition of sign language which constitutes an interface between the computer and hearing-impaired persons, or as an assistive technology in industrial robotics. The sign language recognition is articulated on the extraction of the contours of the sign language alphabets, therefore, converting into one dimensional signal processing, which improves the recognition efficiency and significantly reduces the processing time. The pre-processing of images is performed by a novel skin-color region segmentation defined inside the standard RGB (sRGB) color space, then a morphological filtering is used for non-skin residuals removal. Afterwards, a circular correlation achieves the identification of the sign language after extracting the sign closed contour vector and performing matching between extracted vector and target alphabets vectors. The closed contour vector is generated around the hand palm centroid with position optimized by a particle swarm optimization algorithm search. Finally, a multi-objective function is used for computing the recognition score. The results presented in this paper for skin color segmentation, centroid search and pattern recognition show high effectiveness of the novel artificial vision engine.
关键词： Skin-color,Pattern recognition,Sign language,Segmentation,Particle Swarm Optimization,Human-Machine Interaction
更新于2025-09-19 17:15:36