- 标题
- 摘要
- 关键词
- 实验方案
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Blind Noisy Image Quality Assessment Using Sub-Band Kurtosis
摘要： Noise that afflicts natural images, regardless of the source, generally disturbs the perception of image quality by introducing a high-frequency random element that, when severe, can mask image content. Except at very low levels, where it may play a purpose, it is annoying. There exist significant statistical differences between distortion-free natural images and noisy images that become evident upon comparing the empirical probability distribution histograms of their discrete wavelet transform (DWT) coefficients. The DWT coefficients of low- or no-noise natural images have leptokurtic, peaky distributions with heavy tails; while noisy images tend to be platykurtic with less peaky distributions and shallower tails. The sample kurtosis is a natural measure of the peakedness and tail weight of the distributions of random variables. Here, we study the efficacy of the sample kurtosis of image wavelet coefficients as a feature driving an extreme learning machine which learns to map kurtosis values into perceptual quality scores. The model is trained and tested on five types of noisy images, including additive white Gaussian noise, additive Gaussian color noise, impulse noise, masked noise, and high-frequency noise from the LIVE, CSIQ, TID2008, and TID2013 image quality databases. The experimental results show that the trained model has better quality evaluation performance on noisy images than existing blind noise assessment models, while also outperforming general-purpose blind and full-reference image quality assessment methods.
关键词： sub-band,discrete wavelet transform (DWT),extreme learning machine (ELM),kurtosis,Blind noisy image quality assessment
更新于2025-09-23 15:23:52
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[IEEE 2018 IEEE International Conference on Imaging Systems and Techniques (IST) - Krakow, Poland (2018.10.16-2018.10.18)] 2018 IEEE International Conference on Imaging Systems and Techniques (IST) - Application of ANN and ANFIS for detection of brain tumors in MRIs by using DWT and GLCM texture analysis
摘要： In this work we combine different methodologies in order to develop algorithms for Computer-Aided Diagnosis (CAD) for brain tumors from the axial plane (T2 MRI). All methods utilize texture analysis by extracting features from raw data, without post-processing, based on different techniques, such as Gray Level Co-Occurrence Matrix (GLCM), or Discrete Wavelet Transform (DWT) and different classification methods, based on ANN or ANFIS. All of our proposed methodologies are developed, validated and verified on various sub data including 65% non-healthy MRIS. The total used database consists of 202 MRIs from non-healthy patients and 18 from healthy, segmented visually by an experienced neurosurgeon. Combining different subsets of features, our best results are by using 4 GLCM features for a 4 input and two hidden layers ANN, giving sensitivity 100%, specificity 77.8% accuracy 94.3%. It is proved that the input data to train such a CAD are considered to be unbiased if the ratio between healthy/un-healthy tissue MRIs is about 35%/65%, respectively.
关键词： MRI tumor CAD diagnosis,DWT,ANFIS,GLCM,ANN
更新于2025-09-23 15:23:52
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An Efficient Lossless ROI Image Compression Using Wavelet-Based Modified Region Growing Algorithm
摘要： Nowadays, medical imaging and telemedicine are increasingly being utilized on a huge scale. The expanding interest in storing and sending medical images brings a lack of adequate memory spaces and transmission bandwidth. To resolve these issues, compression was introduced. The main aim of lossless image compression is to improve accuracy, reduce the bit rate, and improve the compression efficiency for the storage and transmission of medical images while maintaining an acceptable image quality for diagnosis purposes. In this paper, we propose lossless medical image compression using wavelet transform and encoding method. Basically, the proposed image compression system comprises three modules: (i) segmentation, (ii) image compression, and (iii) image decompression. First, the input medical image is segmented into region of interest (ROI) and non-ROI using a modified region growing algorithm. Subsequently, the ROI is compressed by discrete cosine transform and set partitioning in hierarchical tree encoding method, and the non-ROI is compressed by discrete wavelet transform and merging-based Huffman encoding method. Finally, the compressed image combination of the compressed ROI and non-ROI is obtained. Then, in the decompression stage, the original medical image is extracted using the reverse procedure. The experimentation was carried out using different medical images, and the proposed method obtained better results compared to different other methods.
关键词： DWT,Medical image,non-ROI,modified region growing,DCT,region of interest,merging-based Huffman encoding,SPIHT
更新于2025-09-23 15:22:29
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Radiographic Evaluation of Gas Tungsten Arc Welded Joints Used in Nuclear Applications by X- and Gamma-rays
摘要： Radiographic inspection of welds in non-standard industrial components often require development of new techniques or modifications to existing ones on the basis of geometry, size, sensitivity requirements, accessibility, nature of defects expected etc. This paper proposes a modified Double Wall Technique (DWT) for evaluation of a weldment in cylindrical component fabricated by Gas Tungsten Arc Welding (GTAW) for use in nuclear facility. The technique has been validated by experimental measurements using film-based gamma radiography with Iridium-192 source. The study also includes evaluation of other joints of the component by X-ray computed radiography (CR) modality. The experimental results show that the proposed technique has met ASME Section V code requirements and yields contrast sensitivity of about 2%.
关键词： gamma radiography,X-ray Computed Radiography (CR),contrast sensitivity,Double Wall Technique (DWT),Gas Tungsten Arc Welding (GTAW)
更新于2025-09-10 09:29:36
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Robust partitioning and indexing for iris biometric database based on local features
摘要： Explosive growth in the volume of stored biometric data has resulted in classification and indexing becoming important operations in image database systems. Consequently, researchers are focused on finding suitable features of images that can be used as indexes. Stored templates have to be classified and indexed based on these extracted features in a manner that enables access to and retrieval of those data by efficient search processes. This paper proposes a method that extracts the most relevant features of iris images to facilitate minimisation of the indexing time and the search area of the biometric database. The proposed method combines three transformation methods DCT, DWT and SVD to analyse iris images and extract their local features. Further, the scalable K-means++ algorithm is used for partitioning and classification processes, and an efficient parallel technique that divides the features groups causing the formation of two b-trees based on index keys is applied for search and retrieval. Moreover, search within a group is achieved using a proposed half search algorithm. Experimental results on three different publicly iris databases indicate that the proposed method results in a significant performance improvement in terms of bin miss rate and penetration rate compared with conventional methods.
关键词： b-trees,DWT,half search algorithm,DCT,SVD,local features,iris biometric database,scalable K-means++
更新于2025-09-10 09:29:36
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[IEEE 2018 2nd International Conference on Engineering Innovation (ICEI) - Bangkok (2018.7.5-2018.7.6)] 2018 2nd International Conference on Engineering Innovation (ICEI) - Diabetic retinopathy fundus image classification using discrete wavelet transform
摘要： Diabetes is an incurable disease which erodes away body slowly, this disease in becoming common and becoming a cause of social distress. The only solution to this problem is early detection of disease and take precautionary measure to keep its effects to minimum. Since it affects various parts of body, the affected organ also includes eye which is very sensitive to any kind of distress. Diabetic Retinopathy effects of diabetes on eye retina, which includes rupturing of retina blood vessels and abnormal growth of blood vessels in retina, which ultimately causes blindness. Diabetic Retinopathy can be identified by examining the retinoscopy images. In this paper, retinoscopy images were processed using wavelet transform. Wavelet coefficients extracted from the images were obtained to identify Diabetic Retinopathy. KNN and SVM were used to classify the retinoscopy images. This papers have shown remarkable improvement as compared to previous studies, with KNN at 98.16 % accuracy and SVM at 97.85 % accuracy.
关键词： sensitivity,specificity,Discrete Wavelet Transform (DWT),accuracy,KNN,Diabetic Retinopathy (DR),histogram equalization,SVM
更新于2025-09-10 09:29:36