Traditional Image Processing Approaches Research Articles

The Deep Hybrid Neural Network and an Application on Polyp Detection

Mathematical morphology and convolution operators are two different methods to extract the characteristics and structures of images. Over the past decades, Deep Convolutional Neural Networks (DCNN) have been proven to be more powerful than traditional image-processing approaches. In this paper, we propose a novel structure called Deep Hybrid Neural Network (DHNN) by taking advantage of the convolution and morphological neural layers. Its practical application to polyp detection in medical images is illustrated. For experimental completeness, we adopt nine polyp image datasets, including publicly available data and our own collected data. For performance comparisons, we select three backbone models. Experimental results show that our DHNN achieves the best performance in comparisons in terms of computational complexity and accurate performance.

A method for detecting spatiotemporal patterns of cancer biomarkers-evoked activity using radial basis function network extracted time-domain features from calcium imaging data

BackgroundTwo-photon calcium imaging is widely used to study the odor-evoked glomerular activity in the dorsal olfactory bulb of macrosmatic animals. The nonstationary character of activated patterns sets a limit on the use of a traditional image processing approaches. New methodThe developed method makes it possible to automatically map cancer biomarkers-activated glomeruli in the rat dorsal olfactory bulb. We interpolated fluorescence intensity of calcium dynamics based on the Gaussian RBF network and synthesized the physiological fluorescence model of the receptive glomerular field. ResultsThe experiments on 5 rats confirmed the correctness of the developed approach. Patterns evoked by the 6-methyl-5-hepten-2-one (stomach cancer biomarker) and benzene (lung cancer biomarker) were correctly identified. Comparison with existing methodsThe proposed method was compared with the nonnegative matrix factorization method and with the method based on computer vision algorithms. The developed approach showed better accuracy in experiments and provided the mathematical models of the odor-evoked patterns synthesis. These models can be used to generate synthetic images of odor-evoked glomerular activity and thus to overcome the problem of small experimental data collected in calcium imaging. ConclusionsThe proposed method should be considered part of the toolkit for fully automatic analysis of calcium imaging-based studies. Currently available methodology is not able to use breath biomarkers to reliably discriminate between cancer patients and healthy controls. Nevertheless, the effective identification of the spatial patterns of cancer biomarkers-evoked glomerular activity can serve as the foundation for highly sensitive biohybrid systems for cancer screening.

IMPROVING ULTRASOUND IMAGE EDGE ENHANCEMENT WITH AADFP METHOD

Image processing is a constantly evolving field that is actively seeking innovative solutions to improve the quality and accuracy of image analysis. Smoothing the edges of ultrasound images using filters is a common processing method that reduces noise and improves image clarity. The choice of filtering method and its parameters depends on the specific clinical or research requirements and the nature of the ultrasound images being processed. However, traditional image processing approaches, especially smoothing, have their limitations. An example is medical imaging, where the preservation of clear anatomical image structures is of paramount importance. Excessive smoothing in such cases leads to the erasure of important anatomical details, potentially affecting diagnostic accuracy. In "computer-aided imaging", excessive smoothing of the edges of the investigated objects may prevent their accurate recognition, which is particularly important when the object is characterised by a variety of features. Due to the limitations of traditional methods, the introduction of better approaches is required to address this problem. The AADFP (Adaptive Anisotropic Diffusion with Feature Priority) method is a technique for improving the quality of ultrasound images, which allows a new approach to their processing, by simultaneously smoothing edges and reducing noise, while preserving important anatomical features. The method is based on adaptive anisotropic diffusion, which adjusts the diffusion process depending on image filling, combined with the integration of features previously extracted from a database of annotated ultrasound images. The use of AADFP allows to significantly improve image quality by selectively smoothing out areas with noise while preserving the main structures in the image. Keywords: Ultrasound image processing, AADFP Method, Image Processing, Medical Imaging, Filtering methods, Feature recognition.

Novel Artificial Intelligence-Based Approaches for Ab Initio Structure Determination and Atomic Model Building for Cryo-Electron Microscopy.

Single particle cryo-electron microscopy (cryo-EM) has emerged as the prevailing method for near-atomic structure determination, shedding light on the important molecular mechanisms of biological macromolecules. However, the inherent dynamics and structural variability of biological complexes coupled with the large number of experimental images generated by a cryo-EM experiment make data processing nontrivial. In particular, ab initio reconstruction and atomic model building remain major bottlenecks that demand substantial computational resources and manual intervention. Approaches utilizing recent innovations in artificial intelligence (AI) technology, particularly deep learning, have the potential to overcome the limitations that cannot be adequately addressed by traditional image processing approaches. Here, we review newly proposed AI-based methods for ab initio volume generation, heterogeneous 3D reconstruction, and atomic model building. We highlight the advancements made by the implementation of AI methods, as well as discuss remaining limitations and areas for future development.

Architecture of Deep Convolutional Encoder-Decoder Networks for Building Footprint Semantic Segmentation

Building extraction from high-resolution aerial images is critical in geospatial applications such as telecommunications, dynamic urban monitoring, updating geographic databases, urban planning, disaster monitoring, and navigation. Automatic building extraction is a massive task because buildings in various places have varied spectral and geometric qualities. As a result, traditional image processing approaches are insufficient for autonomous building extraction from high-resolution aerial imaging applications. Automatic object extraction from high-resolution images has been achieved using semantic segmentation and deep learning models, which have become increasingly important in recent years. In this study, the U-Net model was used for building extraction, initially designed for biomedical image analysis. The encoder part of the U-Net model has been improved with ResNet50, VGG19, VGG16, DenseNet169, and Xception. However, three other models have been implemented to test the performance of the model studied: PSPNet, FPN, and LinkNet. The performance analysis through the intersection of union method has shown that U-Net with the VGG16 encoder presents the best results compared to the other models with a high IoU score of 83.06%. This research aims to examine the effectiveness of these four approaches for extracting buildings from high-resolution aerial data.

A New Automation System for Equipment Status and Efficiency Detection with Machine Learning Based Image Processing

Overall equipment effectiveness (OEE) is a necessary metric for monitoring and improving production processes in industry [Nakajima, 1988]. In order to make the OEE calculation properly, one needs to digitize the accurate data coming from the production line on the shopfloor, which is a challenge itself. The solution we present provides the accurate collection of production line status and OEE data required for monitoring and decision making. The problems found in existing solutions are overcome with advanced analytical methods such as video image processing and deep learning / machine learning. There are many solutions in the literature using traditional image processing approaches [Dalal, 2005] or machine learning methods [Felzenszwalb, 2010] to solve the object detection problem in the video. In recent years, deep learning methods have also yielded successful results in object detection [Girshick, 2014]. The innovative aspect of the solution we offer is that it is a system that learns patterns that may be different for each production line, and automatically predicts the production line status.

GAN-Based Image Dehazing for Intelligent Weld Shape Classification and Tracing Using Deep Learning

Weld seam identification with industrial robots is a difficult task since it requires manual edge recognition and traditional image processing approaches, which take time. Furthermore, noises such as arc light, weld fumes, and different backgrounds have a significant impact on traditional weld seam identification. To solve these issues, deep learning-based object detection is used to distinguish distinct weld seam shapes in the presence of weld fumes, simulating real-world industrial welding settings. Genetic algorithm-based state-of-the-art object detection models such as Scaled YOLOv4 (You Only Look Once), YOLO DarkNet, and YOLOv5 are used in this work. To support actual welding, the aforementioned architecture is trained with 2286 real weld pieces made of mild steel and aluminum plates. To improve weld detection, the welding fumes are denoised using the generative adversarial network (GAN) and compared with dark channel prior (DCP) approach. Then, to discover the distinct weld seams, a contour detection method was applied, and an artificial neural network (ANN) was used to convert the pixel values into robot coordinates. Finally, distinct weld shape coordinates are provided to the TAL BRABO manipulator for tracing the shapes recognized using an eye-to-hand robotic camera setup. Peak signal-to-noise ratio, the structural similarity index, mean square error, and the naturalness image quality evaluator score are the dehazing metrics utilized for evaluation. For each test scenario, detection parameters such as precision, recall, mean average precision (mAP), loss, and inference speed values are compared. Weld shapes are recognized with 95% accuracy using YOLOv5 in both normal and post-fume removal settings. It was observed that the robot is able to trace the weld seam more precisely.

CSE-GAN: A 3D conditional generative adversarial network with concurrent squeeze-and-excitation blocks for lung nodule segmentation

Lung nodule segmentation plays a crucial role in early-stage lung cancer diagnosis, and early detection of lung cancer can improve the survival rate of the patients. The approaches based on convolutional neural networks (CNN) have outperformed the traditional image processing approaches in various computer vision applications, including medical image analysis. Although multiple techniques based on convolutional neural networks have provided state-of-the-art performances for medical image segmentation tasks, these techniques still have some challenges. Two main challenges are data scarcity and class imbalance, which can cause overfitting resulting in poor performance. In this study, we propose an approach based on a 3D conditional generative adversarial network for lung nodule segmentation, which generates better segmentation results by learning the data distribution, leading to better accuracy. The generator in the proposed network is based on the famous U-Net architecture with a concurrent squeeze & excitation module. The discriminator is a simple classification network with a spatial squeeze & channel excitation module, differentiating between ground truth and fake segmentation. To deal with the overfitting, we implement patch-based training. We have evaluated the proposed approach on two datasets, LUNA16 data and a local dataset. We achieved significantly improved performances with dice coefficients of 80.74% and 76.36% and sensitivities of 85.46% and 82.56% for the LUNA test set and local dataset, respectively.

Image Preprocessing in Classification and Identification of Diabetic Eye Diseases

Diabetic eye disease (DED) is a cluster of eye problem that affects diabetic patients. Identifying DED is a crucial activity in retinal fundus images because early diagnosis and treatment can eventually minimize the risk of visual impairment. The retinal fundus image plays a significant role in early DED classification and identification. An accurate diagnostic model’s development using a retinal fundus image depends highly on image quality and quantity. This paper presents a methodical study on the significance of image processing for DED classification. The proposed automated classification framework for DED was achieved in several steps: image quality enhancement, image segmentation (region of interest), image augmentation (geometric transformation), and classification. The optimal results were obtained using traditional image processing methods with a new build convolution neural network (CNN) architecture. The new built CNN combined with the traditional image processing approach presented the best performance with accuracy for DED classification problems. The results of the experiments conducted showed adequate accuracy, specificity, and sensitivity.

Copy-Move Forgery Detection (CMFD) Using Deep Learning for Image and Video Forensics.

With the exponential growth of high-quality fake images in social networks and media, it is necessary to develop recognition algorithms for this type of content. One of the most common types of image and video editing consists of duplicating areas of the image, known as the copy-move technique. Traditional image processing approaches manually look for patterns related to the duplicated content, limiting their use in mass data classification. In contrast, approaches based on deep learning have shown better performance and promising results, but they present generalization problems with a high dependence on training data and the need for appropriate selection of hyperparameters. To overcome this, we propose two approaches that use deep learning, a model by a custom architecture and a model by transfer learning. In each case, the impact of the depth of the network is analyzed in terms of precision (P), recall (R) and F1 score. Additionally, the problem of generalization is addressed with images from eight different open access datasets. Finally, the models are compared in terms of evaluation metrics, and training and inference times. The model by transfer learning of VGG-16 achieves metrics about 10% higher than the model by a custom architecture, however, it requires approximately twice as much inference time as the latter.

Deep-Learning Techniques Classify Cuttings Volume of Shale Shakers

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 194084, “Classifying Cutting Volume at Shale Shakers in Real Time by Video Streaming Using Deep-Learning Techniques,” by Xunsheng Du, Yuchen Jin, and Xuqing Wu, University of Houston, et al., prepared for the 2019 SPE/IADC International Drilling Conference and Exhibition, The Hague, 5–7 March. The paper has been peer approved and is scheduled for publication in SPE Drilling & Completion. A real-time deep-learning model is proposed to classify the volume of cuttings from a shale shaker on an offshore drilling rig by analyzing the real-time monitoring video stream. As opposed to the traditional, time-consuming video-analytics method, the proposed model can implement a real-time classification and achieve remarkable accuracy. The approach is composed of three modules. Compared with results manually labeled by engineers, the model can achieve highly accurate results in real time without dropping frames. Introduction A complete work flow already exists to guide the maintenance and cleaning of the borehole for many oil and gas companies. A well-formulated work flow helps support well integrity and reduce drilling risks and costs. One traditional method needs human observation of cuttings at the shale shaker and a hydraulic and torque-and-drag model; the operation includes a number of cleanup cycles. This continuous manual monitoring of the cuttings volume at the shale shaker becomes the bottleneck of the traditional work flow and is unable to provide a consistent evaluation of the hole-cleaning condition because the human labor cannot be available consistently, and the torque-and-drag operation is discrete, containing a break between two cycles. Most of the previous work used image-analysis techniques to perform quantitative analyses on the cuttings volume. The traditional image-processing approach requires significant work on feature engineering. Because the raw data are usually noisy with missing components, pre- processing and augmenting the data play an important role in making the learning model more efficient and productive. The deep-learning framework, on the other hand, automatically discovers the representations needed for feature detection or classification from raw data. It can help overcome the difficulties in setting up and monitoring devices in a harsh environment, and the data-acquisition requirement for a cuttings-volume-monitoring system at the offshore rig might be relaxed. The objective of this study is to verify the feasibility of building a real-time, automatic cuttings-volume-monitoring sys-tem on a remote site with a limited data-transmission bandwidth. The minimum data-acquisition hardware requirement includes the following: Single uncalibrated charged-coupled-device camera Inconsistent lighting sources Low-bit-rate transmission Image-processing unit without graphics-processing-unit support (e.g., a laptop) A deep neural network (DNN) is adopted to perform the image processing and classification on cuttings volumes from a shale shaker at a remote rig site. Specifically, the convolutional neural networks are implemented as feature extractors and classifiers in the described model. The main contributions of this study can be summarized as follows: A deep-learning framework that can classify the volume of cuttings in real time A real-time video analysis system that requires minimum hardware setup efforts, capable of processing low-resolution images An object-detection work flow to detect automatically the region covered by cuttings A multithread video encoder/decoder implemented to improve real-time video-streaming processing

Düşük Seviye Görsel Öznitelikler ile Basit Bir Konvolüsyonel Sinir Ağından Elde Edilen Özniteliklerin Birleştirilmesi

In the traditional image processing approaches, first low-level image features are extracted and then they are sent to a classifier or a recognizer for further processing. While the traditional image processing techniques employ this step-by-step approach, majority of the recent studies prefer layered architectures which both extract features and do the classification or recognition tasks. These architectures are referred as deep learning techniques and they are applicable if sufficient amount of labeled data is available and the minimum system requirements are met. Nevertheless, most of the time either the data is insufficient or the system sources are not enough. In this study, we experimented how it is still possible to obtain an effective visual representation by combining low-level visual features with features from a simple deep learning model. As a result, combinational features gave rise to 0.80 accuracy on the image data set while the performance of low-level features and deep learning features were 0.70 and 0.74 respectively.

Feature extraction using traditional image processing and convolutional neural network methods to classify white blood cells: a study.

White blood cells play a vital role in monitoring health condition of a person. Change in count and/or appearance of these cells indicate hematological disorders. Manual microscopic evaluation of white blood cells is the gold standard method, but the result depends on skill and experience of the hematologist. In this paper we present a comparative study of feature extraction using two approaches for classification of white blood cells. In the first approach, features were extracted using traditional image processing method and in the second approach we employed AlexNet which is a pre-trained convolutional neural network as feature generator. We used neural network for classification of WBCs. The results demonstrate that, classification result is slightly better for the features extracted using the convolutional neural network approach compared to traditional image processing approach. The average accuracy and sensitivity of 99% was obtained for classification of white blood cells. Hence, any one of these methods can be used for classification of WBCs depending availability of data and required resources.

Comparison of traditional image processing and deep learning approaches for classification of white blood cells in peripheral blood smear images

Automated classification and morphological analysis of white blood cells has been addressed since last four decades, but there is no optimal method which can be used as decision support system in laboratories due to biologically complex nature of the cells. Automated blood cell analysis facilitates quick and objective results and can also handle massive amount of data without compromising with efficiency. In the present study, we demonstrate classification of white blood cells into six types namely lymphocytes, monocytes, neutrophils, eosinophils, basophils and abnormal cells. We provide the comparison of traditional image processing approach and deep learning methods for classification of white blood cells. We evaluated neural network classifier results for hand-crafted features and obtained the average accuracy of 99.8%. We also used full training and transfer learning approaches of convolutional neural network for the classification. An accuracy around 99% was obtained for full training CNN.

Deep learning of structured environments for robot search

Robots often operate in built environments containing underlying structure that can be exploited to help predict future observations. In this work, we present a deep learning based approach to predict exit locations of buildings. This technique exploits the inherent structure of buildings to create a model. A convolutional neural network is trained using a database of building blueprints and used to guide a search within a building. This technique is compared to standard frontier exploration and a traditional image processing approach of extracting features through histogram of gradients (HOG) and training a support vector machine (SVM). After validation through simulation, we show that the proposed deep learning technique reduces the amount of building exploration required to find the goal by 36%.

Efficient Crack Detection Method for Tunnel Lining Surface Cracks Based on Infrared Images

AbstractThe detection of tunnel lining cracks is a very key procedure in the inspection of tunnels. Traditional image-processing approaches are commonly based on the characteristic that the graysca...

Bacterial colony counting by Convolutional Neural Networks.

Counting bacterial colonies on microbiological culture plates is a time-consuming, error-prone, nevertheless fundamental task in microbiology. Computer vision based approaches can increase the efficiency and the reliability of the process, but accurate counting is challenging, due to the high degree of variability of agglomerated colonies. In this paper, we propose a solution which adopts Convolutional Neural Networks (CNN) for counting the number of colonies contained in confluent agglomerates, that scored an overall accuracy of the 92.8% on a large challenging dataset. The proposed CNN-based technique for estimating the cardinality of colony aggregates outperforms traditional image processing approaches, becoming a promising approach to many related applications.

Vein Image Processing based on Morphology for Medical Diagnosis

Medical diagnosis based on vein characteristics attracted large attentions. The change and some feature of vein will reflect the diseases which can be observed from the diagnosis from specialists. Thus, it is very important to process the vein image for medical diagnosis. In order to process the vein image efficiently and effectively, this paper introduces a vein image processing approach based on the morphology principle, which is suitable for examining the shape of veins. The vein images captured from human fingers are used for the experiments. It is observed that the vein shape could be examined clearly so that any diseases or potential risks could be diagnosed by the doctors, comparing with traditional image processing approaches.