International Journal of Image, Graphics and Signal Processing @ijigsp
Статьи журнала - International Journal of Image, Graphics and Signal Processing
Все статьи: 1128
Bio-chip design using multi-rate system for EEG signal on FPGA
Статья научная
Digital Signal Processing (DSP) is one of the fastest growing techniques in the electronics industry. The signal-rate system in digital signal processing has evolved the key of fastest speed in digital signal processor. Field Programmable Gate Array (FPGA) offers good solution for addressing the needs of high performance DSP systems. The focus of this paper is on the basic DSP functions, namely filtering signals to remove unwanted frequency bands. Multi-rate Digital Filters (MDFs) are the main theme to build bio-chip design in this paper. For different purposes DSP systems need to change the sampling rate of the signal to achieve some applications. This can be done using multi-rate system where designers can increase or decrease the operating sampling rate. This bio-chip has attractive features like, low requirement of the coefficient word lengths, significant saving in computation time and storage which results in a reduction in its dynamic power consumption. This paper introduces an efficient FPGA realization of multi-rate digital filter with narrow pass-band and narrow transition band to reduce noises and changing the frequency sampling rate by factor which is required according to application. This bio-chip works on bio-signals like EEG signal.
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Bit Serial Architecture for Variable Block Size Motion Estimation
Статья научная
H.264/AVC is the latest video coding standard adopting variable block size, quarter-pixel accuracy and motion vector prediction and multi-reference frames for motion estimations. These new features result in higher computation requirements than that for previous coding standards.The computational complexity of motion estimation is about 60% in the H.264/AVC encoder. In this paper most significant bit (MSB first) arithmetic based bit serial Variable Block Size Motion Estimation (VBSME) hardware architecture is proposed. MSB first bit serial architecture main feature is, its early termination SAD computation compared to normal bit serial architectures. With this early termination technique, number computations are reduced drastically. Hence power consumption is also less compared to parallel architectures. An efficient bit serial processing element is proposed and developed 2D architecture for processing of 4x4 block in parallel .Inter connect structure is developed in such way that data reusability is achieved between PEs. Two types of adder trees are employed for variable block size SAD calculation with less number of adders. The proposed architecture can generate up to 41 motion vectors (MVs) for each macroblock. The inter connection complexity between PEs reduced drastically compared to parallel architectures. The architecture supports processing of SDTV (640x480) with 30fps at 172.8 MHz for search range [+8, -7]. We could reduce 14% of computations by using early termination technique.
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Block Texture Pattern Detection Based on Smoothness and Complexity of Neighborhood Pixels
Статья научная
In this paper, a novel method for detecting Block Texture Patterns (BTP), based on two measures: smoothness and complexity of neighborhood pixels is proposed. With these two measures, a new classification for texture detection is defined. Texture detection with these measures can be used in many image processing and computer vision applications. As an example, the applicability of BTP on data hiding algorithms is discussed, and the advantages of this classification on these algorithms are shown.
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Block-Based Compressive Sensed Thermal Image Reconstruction using Greedy Algorithms
Статья научная
This paper implements a block based compressive sensing technique for thermal image reconstruction using greedy algorithms. A total of fourteen different sensing patterns were tested for data acquisition. Orthogonal Matching Pursuit (OMP) and Regularized Orthogonal Matching Pursuit (ROMP) with two different thresholds were implemented for image reconstruction with OMP having an edge over ROMP in terms of error and PSNR. ROMP was faster in terms of iterations needed for reconstruction. As the threshold for ROMP was increased the number of iterations needed decreased. Gaussian, Bernoulli and Hadamard patterns were the best for reconstruction. Hadamard matrix, Bernoulli matrix with +/-1 entries and Bernoulli matrix with 0/1 entries have the added advantage of being more conducive for hardware implementation. This paper used Discrete Cosine Transform as the sparsifying basis for reconstruction.
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Block-based Local Binary Patterns for Distant Iris Recognition Using Various Distance Metrics
Статья научная
Nowadays iris recognition has become a promising biometric for human identification and authentication. In this case, feature extraction from near-infrared (NIR) iris images under less-constraint environments is rather challenging to identify an individual accurately. This paper extends a texture descriptor to represent the local spatial patterns. The iris texture is first divided into several blocks from which the shape and appearance of intrinsic iris patterns are extracted with the help of block-based Local Binary Patterns (LBPb). The concepts of uniform, rotation, and invariant patterns are employed to reduce the length of feature space. Additionally, the simplicity of the image descriptor allows for very fast feature extraction. The recognition is performed using a supervised machine learning classifier with various distance metrics in the extracted feature space as a dissimilarity measure. The proposed approach effectively deals with lighting variations, blur focuses on misaligned images and elastic deformation of iris textures. Extensive experiments are conducted on the largest and most publicly accessible CASIA-v4 distance image database. Some statistical measures are computed as performance indicators for the validation of classification outcomes. The area under the Receiver Operating Characteristic (ROC) curves is illustrated to compare the diagnostic ability of the classifier for the LBP and its extensions. The experimental results suggest that the LBPb is more effective than other rotation invariants and uniform rotation invariants in local binary patterns for distant iris recognition. The Braycurtis distance metric provides the highest possible accuracy compared to other distance metrics and competitive methods.
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Blur Classification using Ridgelet Transform and Feed Forward Neural Network
Статья научная
The objective of image restoration approach is to recover a true image from a degraded version. This problem can be stated as blind or non-blind depending upon whether blur parameters are known prior to the restoration process. Blind restoration deals with parameter identification before deconvolution. Though there exists multiple blind restorations techniques but blur type recognition is extremely desirable before application of any blur parameters estimation approach. In this paper, we develop a blur classification approach that deploys a feed forward neural network to categories motion, defocus and combined blur types. The features deployed for designing of classification system include mean and standard deviation of ridgelet energies. Our simulation results show the preciseness of proposed method.
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Brain Tissue Classification from Multispectral MRI by Wavelet based Principal Component Analysis
Статья научная
In this paper, we propose a multispectral analysis system using wavelet based Principal Component Analysis (PCA), to improve the brain tissue classification from MRI images. Global transforms like PCA often neglects significant small abnormality details, while dealing with a massive amount of multispectral data. In order to resolve this issue, input dataset is expanded by detail coefficients from multisignal wavelet analysis. Then, PCA is applied on the new dataset to perform feature analysis. Finally, an unsupervised classification with Fuzzy C-Means clustering algorithm is used to measure the improvement in reproducibility and accuracy of the results. A detailed comparative analysis of classified tissues with those from conventional PCA is also carried out. Proposed method yielded good improvement in classification of small abnormalities with high sensitivity/accuracy values, 98.9/98.3, for clinical analysis. Experimental results from synthetic and clinical data recommend the new method as a promising approach in brain tissue analysis.
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Статья научная
Brain MRI is mainly affected by noise and intensity inhomogeneity (IIH) during its acquisition. Brain tissue segmentation plays an important role in biomedical research and clinical applications. Brain tissue segmentation is essential for physicians for the proper diagnosis and right treatment of brain-related disorders. Fuzzy C-means (FCM) clustering is one of the widely used algorithms for brain tissue segmentation. Traditional FCM has the limitations of misclassification of pixels that leads to inaccurate cluster centers. Due to this, it is unable to address the issues of noise and IIH. In FCM there exists uncertainty in controlling the fuzziness of the clusters as the fuzzifier is fixed. This paper proposed a novel linguistic fuzzifier-based FCM (LFFCM) to overcome the limitations of traditional FCM during brain tissue segmentation from the MR images. In this method, a linguistic fuzzifier is used instead of a fixed fuzzifier. The spatial information incorporated in the membership function can reduce the misclassification of pixels. The proposed LFFCM can handle IIH, due to having highly accurate cluster centers. The inclusion of the adaptive weights in the membership function results in accurate cluster centers. Various brain MR images are used to evaluate the proposed technique and the results are compared with some state-of-the-art techniques. The results reveal that the proposed method performed better than the other.
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Статья научная
Tumor boundary detection is one of the challenging tasks in the medical diagnosis field. The proposed work constructed brain tumor boundary using bi-modal fuzzy histogram thresholding and edge indication map (EIM). The proposed work has two major steps. Initially step 1 is aimed to enhance the contrast in order to make the sharp edges. An intensity transformation is used for contrast enhancement with automatic threshold value produced by bimodal fuzzy histogram thresholding technique. Next in step 2 the EIM is generated by hybrid approach with the results of existing edge operators and maximum voting scheme. The edge indication map produces continuous tumor boundary along with brain border and substructures (cerebrospinal fluid (CSF), sulcal CSF (SCSF) and interhemispheric fissure) to reach the tumor location easily. The experimental results compared with gold standard using several evaluation parameters. The results showed better values and quality to proposed method than the traditional edge detection techniques. The 3D volume construction using edge indication map is very useful to analysis the brain tumor location during the surgical planning process.
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Brain Tumor Classification Using Back Propagation Neural Network
Статья научная
The conventional method for medical resonance brain images classification and tumors detection is by human inspection. Operator-assisted classification methods are impractical for large amounts of data and are also non-reproducible. Hence, this paper presents Neural Network techniques for the classification of the magnetic resonance human brain images. The proposed Neural Network technique consists of the following stages namely, feature extraction, dimensionality reduction, and classification. The features extracted from the magnetic resonance images (MRI) have been reduced using principles component analysis (PCA) to the more essential features such as mean, median, variance, correlation, values of maximum and minimum intensity. In the classification stage, classifier based on Back-Propagation Neural Network has been developed. This classifier has been used to classify subjects as normal, benign and malignant brain tumor images. The results show that BPN classifier gives fast and accurate classification than the other neural networks and can be effectively used for classifying brain tumor with high level of accuracy.
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Breast Cancer Classification from Ultrasound Images using VGG16 Model based Transfer Learning
Статья научная
Ultrasound based breast screening is gaining attention recently especially for dense breast. The technological advancement, cancer awareness, and cost-safety-availability benefits lead rapid rise of breast ultrasound market. The irregular shape, intensity variation, and additional blood vessels of malignant cancer are distinguishable in ultrasound images from the benign phase. However, classification of breast cancer using ultrasound images is a difficult process owing to speckle noise and complex textures of breast. In this paper, a breast cancer classification method is presented using VGG16 model based transfer learning approach. We have used median filter to despeckle the images. The layers for convolution process of the pretrained VGG16 model along with the maxpooling layers have been used as feature extractor and a proposed fully connected two layers deep neural network has been designed as classifier. Adam optimizer is used with learning rate of 0.001 and binary cross-entropy is chosen as the loss function for model optimization. Dropout of hidden layers is used to avoid overfitting. Breast Ultrasound images from two databases (total 897 images) have been combined to train, validate and test the performance and generalization strength of the classifier. Experimental results showed the training accuracy as 98.2% and testing accuracy as 91% for blind testing data with a reduced of computational complexity. Gradient class activation mapping (Grad-CAM) technique has been used to visualize and check the targeted regions localization effort at the final convolutional layer and found as noteworthy. The outcomes of this work might be useful for the clinical applications of breast cancer diagnosis.
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Breast lesion segmentation and area calculation for MR images
Статья научная
In this paper, our goal is to determine the boundaries of lesion and then calculate the area of existing lesion in breast magnetic resonance (MR) images to provide a useful information to the radiologists. For this purpose, at first stage region growing (RG) method and active contour model (Snake) is applied to the images to make the boundaries of lesion visible. RG method is one of the simplest approaches for image segmentation and provides accurate results with lower computation time due to its seed point initialization step. Snake method molds a closed contour to the boundary of a region in an image and is also popular in medical image segmentation studies. In the presented study, both of these methods are utilized to determine the lesion boundaries. After determining the boundaries of lesion accurately in the second stage of the study, bit-quad method is applied to the segmented images. Bit quad method is used to compute the area and perimeter of binary lesion images based on matching the logical state of regions of image to binary patterns. Finally, to evaluate the performance of the proposed study, computer simulations are performed. It is demonstrated via computer simulations that the lesion area and parameter values are very close to real values. By means of this study it is aimed to support radiologists during diagnosis and assessment of breast lesions.
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Статья научная
The growing quality and safety concern about fresh agricultural produce among consumers have led to the development of non-destructive quality assessment and testing techniques of fruits and vegetables. Humans judge the quality of fruits based on sensory attributes like taste, aroma etc. The shape, size, color, presence of defects which are external to fruits also influence the degree of consumer acceptability of produce. The traditional time consuming, manual fruit quality inspection is replaced by automated, fast, consistent, non-destructive techniques using computer vision in combination with learning algorithms. But the lack of benchmark datasets for agricultural produce has made an objective comparison of the proposed methods difficult. Hence, the proposed work aims to build a medium scale dataset for mango fruits of “Alphonso” cultivar with three classes: chilling injury, defective and non-defective. The reliability of the proposed dataset consisting of 2279 color images of mango fruits with 736 samples in chilling injury class, 632 samples in defective class and 911 samples in non-defective class, was established using a novel approach of developing a predictive model based on discriminant function analysis (DFA) which assigns group membership to each sample of the dataset. Extensive benchmarking analysis is established on the validated dataset using statistical and deep learning algorithms like support vector machine (SVM) and convolutional neural network (CNN), respectively. SVM achieved significant disease classification accuracy of 95% and 91.52% accuracy was achieved by custom CNN. The results of the proposed work indicate that the proposed color image dataset of mango fruits can be used as a benchmark dataset by other researchers for objective comparison in quality evaluation of mango fruits.
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Статья научная
Colon cancer is a growth of cells that begins in a part of the large intestine called the colon. Colon cancer happens when cells in the colon develop changes in their DNA. Consequently, fewer infections and fatalities may result from early identification of this cancer. Histological analysis is used for a final diagnosis of colon cancer. Histopathology, or the microscopic examination of damaged tissue, is crucial for both cancer diagnosis and treatment. This work suggests a novel deep learning technique for colon cancer detection effectively. Histopathology images are collected from various type of sources. To enhance the quality of raw images, pre-processed techniques such as image scaling, colour map improved image sharpening, and image restoration are used. Resize the image's dimensions in image resizing to minimize the processing time. A colour map enhances the sharpness of an image by combining two techniques: The contrast adjustment technique is used to alter the image's contrast first. The resultant image is then enhanced by applying the image sharpening process and scaling it using a weighting fraction. As using the final image has increased quality, blur and undesirable noise are removed using image restoration. Next, the pre-data are used in the Attention U-Net segmentation procedure, which segments the region of the pre-data. To extract features from this segmented image to perform an accurate diagnosis, efficientnetB0 is used. In data extraction, the Bidirectional GRU model is used to process the data further in order to develop predictions. When processing input sequences in both directions with the BiGRU model, it is feasible to gather contextual information to increase accuracy and predict colon cancer effectively. In the proposed model colon disease prediction classifier offer 97% accuracy, 96% specificity and 95.49% F1_score. Thus, the proposed model effectively predicts colon cancer and improves accuracy.
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CNN and GAN Based Stroke Detection Using CT Scan Images
Статья научная
The objective of the research work is to detect stroke using CT scan images. In the research work an analysis of 3D CNN method for stroke detection is presented. The work also presents a new method of stroke detection using semi-supervised Adversarial Networks (SGAN).3D CNN is the traditional approach to any type of image classification problem. But being data-hungry, it becomes difficult to use them when data is scarce. High-quality medical data is difficult to find and hence alternative approaches seem worth approaching. The relatively new GANs can generate images like the training images, and its SGAN variant can use these generated images for training the classifier. We investigate the usefulness of SGANs comparatively with CNNs in this paper. The proposed SGAN method is compared with state of art methods in literature using accuracy, sensitivity and specificity. The SGAN method demonstrates an accuracy of 93%, Sensitivity of 100% and Specificity of 90%. For small data sets in medical imaging the proposed SGAN method exhibit an encouraging performance as compared to other methods using large datasets. In the research paper, we propose methodologies for detecting strokes by using 2 approaches: 3D CNNs and SGANs. The relatively new GANs can generate images like the training images, and its SGAN variant can use these generated images for training the classifier. We investigate the usefulness of SGANs comparatively with CNNs in this paper.
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COVID-19 Automatic Detection from CT Images through Transfer Learning
Статья научная
Identification of COVID-19 may help the community and patient to prevent the disease containment and plan to attend disease in right time. Deep neural network models widely used to analyze the medical images of COVID-19 for automatic detection and give the decision support for radiologists to summarize the accurate remarks. This paper proposed deep transfer learning for chest CT scan images to detection and diagnosis of COVID-19. VGG19, InceptionRestNetV3, InceptionV3 and DenseNet201 neural network used for automatic detection of COVID-19 disease form CT scan images (SARS-CoV-2 CT scan Dataset). Four deep transfer learning models were developed, tested and compared. The main objective of this paper is to use pre-trained features and converge pre-trained features with targeted features to improve the classification accuracy. It is observed that DenseNet201 noted the best performance and the classification accuracy is 99.98% for 300 epochs. The findings of the experiments show that the deeper networks struggle to train adequately and give less consistency when there is limited data. The DenseNet201 model adopted for COVID-19 identification from lung CT scans has been intensively optimized with optimal hyper parameters and performs at noteworthy levels with precision 99.2%, recall 100%, specificity 99.2%, and F1 score 99.2%.
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Статья научная
This work introduces the novelty as an application of histogram-based bins approach with statistical moments for detecting and classifying malaria using blood smear images into parasitized and uninfected cell images and the rising disease of COVID-19/Normal lung images. Proposed algorithms greatly vary as compared to the previous work. This work aims to improve accuracy in detection and classification and reduce feature vector dimensionality. It focuses on detailed image contents extracted into 8 bins by considering the significance of the R, G, and B color component relationship in the formation of each pixel. The texture features are represented by the first four moments for each of the three colors separately. This leads to the generation of 12 features vectors, each of size 8 components for each image in the database. Feature dimensionality reduction is achieved by applying different feature selection techniques to obtain desired optimum feature space. The comprehensive feature analysis presented here identifies many useful findings in order to validate the contribution of each image content uniquely in detection and classification. The proposed approach experimented with two image datasets: the malaria dataset obtained from the National Library of Medicine (NLM) and the lung image dataset acquired from the Radiography Database from Kaggle. The performance of work presented here is evaluated and compared with previous work with the same set of parameters, namely precision, recall, F1 score, and the AUC. We have achieved and improved the performances compared to previous work and also achieved better results even for the COVID-19 dataset.
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CRDL-PNet: An Efficient DeepLab-based Model for Segmenting Polyp Colonoscopy Images
Статья научная
Colorectal cancers are the third-largest kind of cancer in the world. However, detecting and removing precursor polyps with adenomatous cells using optical colonoscopy images helps to prevent this type of cancer. Moreover, hyperplastic polyps are benign cancers; adenomatous polyps are more likely to grow into cancerous tumors. Therefore, the detection and segmentation of polyps provide further histological evaluation. However, the main challenge is the extensive range of infected polyp features inside the colon and the lack of contrast between normal and infected areas. To solve these issues, the proposed novel Customized ResNet50 with DeepLabV3Plus Network (CRDL-PNet) model provided a scheme for segmenting polyps from colonoscopy images. The customized ResNet50 extracted features from polyp colonoscopy images. Furthermore, Atrous Spatial Pyramid Pooling (ASPP) is used to handle scale variation during training and improve feature selection maps in an upsampling layer. Additionally, the Gateaux Derivatives (GD) approach is used to segment boundary boxes of polyp regions. The proposed method has been evaluated on four datasets, namely the Kvasir-SEG, ETIS-PolypLaribDB, CVC-ClinicDB, and CVC-ColonDB datasets, for segmenting and detecting polyps. The simulation results have been examined by evaluation metrics, such as accuracy, Intersection-Over-Union (IOU), mean IOU, precision, recall, F1-score, dice, Jaccard, and Mean Process Time per Frame (MPTF) for proper validation. The proposed scheme outperforms the existing State-Of-The-Arts (SOTA) model on the same polyp datasets.
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Capsaicin-induced Thermal Enhancement on Target Tissues in Hyperthermia
Статья научная
Local thermal enhancement in target tissue is of great interest in tumor hyperthermia. In this study, we proposed a brand-new thermal enhancement protocol for tumor hyperthermia using heat generated from thermoge-nesis evoked by capsaicin, which can safely deliver a totally localized heating to target tissue. A healthy male volunteer was recruited, whose partial areas of the dorsum of hand and posterior aspect of forearm were smeared with 1% (w/w) capsaicin solution, to determine the increase of ther-mogenesis in human body. In addition, animal experiments on healthy Kunming (KM) mice (20-22g) were performed to test the feasibility and efficacy of capsaicin-induced thermal enhancement. These KM mice were first locally smeared with, subcutaneous or intraperitoneal injected of the same capsaicin solution, and then heated by near infrared laser. Preliminary experiments on the volunteer showed an effec-tive temperature increase in the skin area. Animal experi-ments indicated that distinct thermal enhancement in heat-ing effect, and that the thermal enhancement induced by intraperitoneal injection of capsaicin is more obvious than the other two ways. Thus capsaicin can be used as a poten-tial therapeutic adjuvant to locally enhance heating effects in target tissue during tumor hyperthermia.
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Central Moment and Multinomial Based Sub Image Clipped Histogram Equalization for Image Enhancement
Статья научная
The visual appearance of a digital image can be improved through image enhancement algorithm by reducing the noise in an image, improving the color, brightness and contrast of an image for more analysis. This paper introduces an image enhancement algorithm. The image histogram is processed through multinomial curvature fitting function to reduces the number of pixels for each intensity value through minimizing the sum of squared residuals. Then resampling is done to smooth out the computed data. After then histogram clipping threshold is computed by central moment processed on the resampled data value to restrict the over enhancement rate. Histogram is equally divided into two sub histograms. The sub histograms are equalized by transfer function to merged the sub images into one output image. The output image is further improved by reducing the environmental haze effect by applying Matlab imreducehaze method, which gives the final output image. Matlab simulation results demonstrate that the proposed method outperforms than other compared methods in terms of both quantitative and qualitative performance evaluation applied on colorfulness based PCQI (C-PCQI), and blind image quality measure of enhanced images (BIQME) image quality metrics.
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