Image Pre-processing Techniques Research Articles

Concrete roadway crack segmentation using encoder-decoder networks with range images

Recently, researchers have utilized DCNN for pixel-wise crack classification through semantic segmentation. Nevertheless, some issues in current DCNN-based roadway crack segmentation are yet to be fully addressed. For example, image pre-processing techniques are often required to eliminate the surface variations in range images, which may bring uncertainties due to subjective parameter selection; besides, disturbances from many non-crack patterns such as pavement grooves can deteriorate the crack segmentation performance, which remains a challenge for current DCNN-based methodologies. This paper proposes a methodology based on encoder-decoder networks to achieve pixel-wise crack classification performance on laser-scanned range images, under the disturbance of surface variations and grooved patterns in concrete pavements. The raw range data is directly applied in this methodology without any pre-processing. A comparative study is performed to determine the optimal architecture layout among twelve proposed candidates. Meanwhile, the influence of residual connections on DCNN performance is investigated and demonstrated.

Efficient Caoshu Character Recognition Scheme and Service Using CNN-Based Recognition Model Optimization.

Deep learning-based artificial intelligence models are widely used in various computing fields. Especially, Convolutional Neural Network (CNN) models perform very well for image recognition and classification. In this paper, we propose an optimized CNN-based recognition model to recognize Caoshu characters. In the proposed scheme, an image pre-processing and data augmentation techniques for our Caoshu dataset were applied to optimize and enhance the CNN-based Caoshu character recognition model’s recognition performance. In the performance evaluation, Caoshu character recognition performance was compared and analyzed according to the proposed performance optimization. Based on the model validation results, the recognition accuracy was up to about 98.0% in the case of TOP-1. Based on the testing results of the optimized model, the accuracy, precision, recall, and F1 score are 88.12%, 81.84%, 84.20%, and 83.0%, respectively. Finally, we have designed and implemented a Caoshu recognition service as an Android application based on the optimized CNN based Cahosu recognition model. We have verified that the Caoshu recognition service could be performed in real-time.

Use of multi-temporal satellite data for monitoring pool surface areas occurring in non-perennial rivers in semi-arid environments of the Western Cape, South Africa

Hydrological monitoring networks do not adequately cover non-perennial rivers in most parts of Africa partly because river flows do not frequently occur on these rivers. However, in the arid and semi-arid regions flows occurring on these rivers are an important resource that have to be managed judiciously. The lack of data on magnitudes, timing and frequency of non-perennial river flows constrain sustainable management of their water resources. The availability of moderate resolution satellite data (e.g. Sentinel-2 Multispectral Instrument and Landsat 8 Operational Land Imager) offers an opportunity to monitor the surface water availability specifically changes in surface areas covered with water in pools along these rivers. In this study, we investigated the use of remote sensing data to detect and monitor changes of water surface areas of pools along three non-perennial rivers and one perennial river in the Western Cape, South Africa, from 2016 to 2017, using Landsat 8 and Sentinel-2 datasets. We assessed a range of image pre-processing and image classification techniques to determine the most suitable method for surface water identification and pool area estimation along the selected rivers. The following indices were used; Normalised Difference Water Index (NDWI), Modified NDWI (MNDWI), Normalised Difference Vegetation Index (NDVI), Automated Water Extraction Index for shadowed (AWEIsh), non-shadowed regions (AWEInsh), and the Multi-Band Water Index (MBWI). The NDWI computed from Sentinel-2 TOA reflectance datasets was found to be the most suitable index for mapping pools, with an overall accuracy ranging between 60 and 86%. This index was used successfully to establish the variations of surface areas of pools covered with water along the Breede, Nuwejaars, Tankwa and Touws Rivers during the 2016–2017 period. The temporal variations of pool areas could be explained as expected by changes of rainfall and potential evapotranspiration. Overall, the study demonstrated the feasibility of using moderate resolution datasets to monitor pool changes of areas covered with water in non-perennial rivers, which contributes to improved management of water resources particularly in arid and semi-arid environments.

Pre-Processing and Image Segmentation Techniques

image are known as image analysis. First step in image analysis is to segment an image. Image segmentation
 is a key step for partioning an image into several segments to find objects, borders &meaningful objects for
 an image. In image processing for any application when the work is done on an image, the initial step is to
 divide the image in order to solve the difficulty. Divided images are widely used in a multitude of different
 applications like density of tissue volumes, pathology, Satellite imaging, Extracting features & recognizing
 objects, treatment planning & few computer integrated surgeries etc.
 This paper gives an overview of different image pre-processing techniques& image segmentation techniques.
 Before we do segmentation of an image, we need to pre-process an image to remove noise.

Detection of Breast Lesion Using Improved GLCM Feature Based Extraction in Mammogram Images

A classic mammogram image processing method commonly illustrates of mammogram image pre-processing, segmentation, feature extraction and classification techniques. Feature extraction is a significant measure in Computer Assisted Diagnosis (CAD) of Breast cancer abnormalities. Feature Extraction is the method of shrinking the dimension of image data by finding an essential detail as of the segmented image. The visual substance of a segmented image portions can be confined using this procedure. Feature extraction is one of the Image retrieval methods for achieving higher accuracy. In this paper an Improved Gray Level Co-occurrence Matrix (GLCM) Feature based Extraction with shape and Tamura features are discussed. Tamura features are extracted from Optimized Kernel Fuzzy Clustering Algorithm (OKFCA) which is one of the segmentation methods. This paper proposed Improved GLCM Feature Based Extraction algorithm to obtain the trained features which will be used for further processing in future. Experimental results of 322 images were evaluated from Mammographic Image Analysis Society (MIAS) database and used to evaluate the feature extraction process.

RETRACTED ARTICLE: Pre-processing on remotely sensed data with unsupervised classification analysis

The use of remote sensing and geographic information system (GIS) technologies grow drastically in recent years by ecologists around the globe. At present, using sophisticated sensors, there is a massive challenge in handling the high dense remotely captured information with spatial, spectral, temporal, and radiometric resolutions. This article addresses how to handle such large volume remotely sensed data using R programming with the aid of RStudio. We aim broadly two categories, such as image preprocessing and classification techniques on remotely sensed data. Image preprocessing methods such as false-color composite, pan-sharpening, single event upset error mitigation, and a view on the hyper spectral image. The other category comes with a focus on landscape spatial features analysis and unsupervised classification to analyze vegetation land. The CLARA (Clustering LARge Application) algorithm is used in this study which exploits k-medoids approaches for the unsupervised classification. Also, for results comparison, different vegetation indices such as normalized difference water index (NDWI), modified NDWI (MNDWI), and soil adjusted vegetation index are used for vegetation (land) analysis. Also, for the unsupervised classification, the Silhouette index is used to compare the clustering algorithms.

Efficacy of deep convolutional neural network algorithm for the identification and classification of dental implant systems, using panoramic and periapical radiographs: A pilot study.

Convolutional neural networks (CNNs), a particular type of deep learning architecture, are positioned to become one of the most transformative technologies for medical applications. The aim of the current study was to evaluate the efficacy of deep CNN algorithm for the identification and classification of dental implant systems.A total of 5390 panoramic and 5380 periapical radiographic images from 3 types of dental implant systems, with similar shape and internal conical connection, were randomly divided into training and validation dataset (80%) and a test dataset (20%). We performed image preprocessing and transfer learning techniques, based on fine-tuned and pre-trained deep CNN architecture (GoogLeNet Inception-v3). The test dataset was used to assess the accuracy, sensitivity, specificity, receiver operating characteristic curve, area under the receiver operating characteristic curve (AUC), and confusion matrix compared between deep CNN and periodontal specialist.We found that the deep CNN architecture (AUC = 0.971, 95% confidence interval 0.963–0.978) and board-certified periodontist (AUC = 0.925, 95% confidence interval 0.913–0.935) showed reliable classification accuracies.This study demonstrated that deep CNN architecture is useful for the identification and classification of dental implant systems using panoramic and periapical radiographic images.

Evaluation of Deep Neural Networks for Semantic Segmentation of Prostate in T2W MRI.

In this paper, we present an evaluation of four encoder–decoder CNNs in the segmentation of the prostate gland in T2W magnetic resonance imaging (MRI) image. The four selected CNNs are FCN, SegNet, U-Net, and DeepLabV3+, which was originally proposed for the segmentation of road scene, biomedical, and natural images. Segmentation of prostate in T2W MRI images is an important step in the automatic diagnosis of prostate cancer to enable better lesion detection and staging of prostate cancer. Therefore, many research efforts have been conducted to improve the segmentation of the prostate gland in MRI images. The main challenges of prostate gland segmentation are blurry prostate boundary and variability in prostate anatomical structure. In this work, we investigated the performance of encoder–decoder CNNs for segmentation of prostate gland in T2W MRI. Image pre-processing techniques including image resizing, center-cropping and intensity normalization are applied to address the issues of inter-patient and inter-scanner variability as well as the issue of dominating background pixels over prostate pixels. In addition, to enrich the network with more data, to increase data variation, and to improve its accuracy, patch extraction and data augmentation are applied prior to training the networks. Furthermore, class weight balancing is used to avoid having biased networks since the number of background pixels is much higher than the prostate pixels. The class imbalance problem is solved by utilizing weighted cross-entropy loss function during the training of the CNN model. The performance of the CNNs is evaluated in terms of the Dice similarity coefficient (DSC) and our experimental results show that patch-wise DeepLabV3+ gives the best performance with DSC equal to . This value is the highest DSC score compared to the FCN, SegNet, and U-Net that also competed the recently published state-of-the-art method of prostate segmentation.

Identification of Diabetic Retinopathy from Color Fundus Images using Deep Convolutional Neural Network

Existing methods on retinal disease detection are mostly depends on lesion detection techniques or multiple instance learning framework. However extensive research efforts fail to address effective representations of the different lesions from fundus images. In this paper, a innovative techniques is offered built on pre-examined entirely convolutional neural network (CNN) over and done with transfer learning. The proposed method utilizes the effective learning from recent deep CNN models with use of SVM classifier at the end. Meanwhile, additional retinal image pre-processing technique is applied for the better classification results. The improved result has contributed to the area of computer aided diagnosis for retinal screening system. Extensive experiments have been conducted on Messidor and IDRiD database with desired obtained accuracy of 96.29 % and 94.82 %. The proposed method supports retinal disease screening effectively by deep learning methods.

Gaze Tracking and Point Estimation Using Low-Cost Head-Mounted Devices.

In this study, a head-mounted device was developed to track the gaze of the eyes and estimate the gaze point on the user’s visual plane. To provide a cost-effective vision tracking solution, this head-mounted device is combined with a sized endoscope camera, infrared light, and mobile phone; the devices are also implemented via 3D printing to reduce costs. Based on the proposed image pre-processing techniques, the system can efficiently extract and estimate the pupil ellipse from the camera module. A 3D eye model was also developed to effectively locate eye gaze points from extracted eye images. In the experimental results, average accuracy, precision, and recall rates of the proposed system can achieve an average of over 97%, which can demonstrate the efficiency of the proposed system. This study can be widely used in the Internet of Things, virtual reality, assistive devices, and human-computer interaction applications.

Reliable Tuberculosis Detection Using Chest X-Ray With Deep Learning, Segmentation and Visualization

Tuberculosis (TB) is a chronic lung disease that occurs due to bacterial infection and is one of the top 10 leading causes of death. Accurate and early detection of TB is very important, otherwise, it could be life-threatening. In this work, we have detected TB reliably from the chest X-ray images using image pre-processing, data augmentation, image segmentation, and deep-learning classification techniques. Several public databases were used to create a database of 700 TB infected and 3500 normal chest X-ray images for this study. Nine different deep CNNs (ResNet18, ResNet50, ResNet101, ChexNet, InceptionV3, Vgg19, DenseNet201, SqueezeNet, and MobileNet), which were used for transfer learning from their pre-trained initial weights and trained, validated and tested for classifying TB and non-TB normal cases. Three different experiments were carried out in this work: segmentation of X-ray images using two different U-net models, classification using X-ray images, and segmented lung images. The accuracy, precision, sensitivity, F1-score, specificity in the detection of tuberculosis using X-ray images were 97.07 %, 97.34 %, 97.07 %, 97.14 % and 97.36 % respectively. However, segmented lungs for the classification outperformed than whole X-ray image-based classification and accuracy, precision, sensitivity, F1-score, specificity were 99.9 %, 99.91 %, 99.9 %, 99.9 %, and 99.52 % respectively. The paper also used a visualization technique to confirm that CNN learns dominantly from the segmented lung regions results in higher detection accuracy. The proposed method with state-of-the-art performance can be useful in the computer-aided faster diagnosis of tuberculosis.

Detecting anomalous growth of skin lesion using threshold-based segmentation algorithm and Fuzzy K-Nearest Neighbor classifier.

Skin cancer is a complex and life-threatening disease caused primarily by genetic instability and accumulation of multiple molecular alternations. Currently, there is a great interest in the prospects of image processing to provide quantitative information about a skin lesion, that can be relevance for the clinical images and also used as a stand-alone cautioning tool. To accomplish a powerful approach to recognize skin cancer without performing any unnecessary skin biopsies, this article presents a new hybrid technique for the classification of skin images using Firefly with K-Nearest Neighbor algorithm (FKNN). FKNN classifier is used to predict and classify skin cancer along with threshold-based segmentation and ABCD feature extraction. Image preprocessing and feature extraction techniques are mandatory for any image-based applications. Initially, it is essential to eliminate the illumination variation and the other unwanted shadow areas present in the skin image, which is done by homomorphic filtering called preprocessing. The comparison of our proposed method with other existing methods and a comprehensive discussion is explored based on the obtained results. The proposed FKNN provides a quantitative information about a skin lesion through hybrid KNN and firefly optimization that helps for recognizing the skin cancer efficiently than other technique with low computational complexity and time.

Assorted Image Processing Techniques for Retinal OCT in Glaucoma Detection

This research is focused on image processing techniques for automatic detection of glaucoma in Retinal Optical Image Coherence Tomography. In the recent year, the interest on the automated glaucoma diagnosis techniques using the image processing techniques are exploding at an optimal rate which may help in the early diagnosis of the patients to recover from the permanent blindness. The manual diagnosis process for glaucoma detection within the medical community is tedious and time-consuming process that is susceptible to error. On the contrary image processing-based algorithms in constituent to deep learning can give better computation possibility in a trustworthy manner. The primary objective of the proposed research is to define proper image pre-processing techniques that aid in the process of the diagnosis. The aim is to aid in glaucoma detection by proposing image processing techniques to process the Optical Coherence Tomography images of the retina. The saturation removal technique is used for the intensity correction with histogram equalization of flattening the images. Resizing, filtering, and argumentation is also proposed in this research as possible image processing techniques. To show the generalized nature of the proposed approach, pre-processing techniques are employed on the OCT images which resulted in good results.

Analysing thalamus and its sub nuclei in MRI brain image to distinguish schizophrenia subjects using back propagation neural network

In this paper, we presented precise and proficient techniques for measuring the human thalamus, medial dorsal and the pulvinar nucleus with magnetic resonance imaging (MRI). In spite of the fact that thalamic nuclei are not straightforwardly visible on traditional MRI image. We applied a novel and competent image pre-processing techniques to enhance the visual quality of MRI image. In addition to this we have used various segmentation algorithms to accurately extract entire thalamus from brain MRI images. Diffusion MRI is used to extract various nucleus of thalamus. Several optimal features such as textures, morphological are derived from thalamus and medial dorsal regions which are then used to train the artificial neural network model (ANN). Our artificial neural network model accurately classifies between schizophrenic and healthy subjects based on thalamic anatomy for larger sample sizes.

A Novel Method to Detect and Classify Oral Cavity Cancer System

The primary objective of this article is to build a Computer Aided Diagnostic (CAD) technique for the segmentation of Oral cavity cancer. The frequency of oral cancer occurrence has been observed globally in recent decades. The death ratio from oral cancers is excessive and keeps on rising. This article has concentrated on oral Computed Tomography (CT) image pre-processing and segmentation techniques to enhance resolution of the image and readability to enhance classification outcome. The proposed model targeted on image pre-processing and segmentation steps to improve the resolution of the image, then enhance the precision of swelling recognition and classification. The American Cancer Society calculates that there will be almost 60,000 new patients and roughly 9,000 deaths from oral or throat cancer in 2017. The preliminary prediction of oral cancer is to check out the ocular areas cautiously and record the holes present in the teeth of the damaged person as true-color digital images. The choice about the extra cure of the oral cancer damaged person is principally based on the growth of the lesion. Oral cancers are a type of cancer, the place few irregular wounds or patches will present in the holes present in the teeth. As it is challenging to recognize it in the beginning states, it has one of the bad survival ratios. The suggested method receives the Computerized Tomography (CT) scanned images of the cancer damaged vicinity and can detect the presence of malignancy. The managing of oral cancer is a multidisciplinary attempt, as every damaged person provides the treating clinicians with a sole collection of difficulties, the managing of which affects on each living and high- condition of life.

Exploration and Performance Assessment of Efficient Histopathology Image Pre-Processing and Segmentation Techniques for Breast Cancer Prediction

Evaluation of features after segmentation is a significant process in image processing, especially in the medical field. Medical imaging technologies are widely utilized in clinical diagnosis to guide therapeutic and surgical decisions and to monitor disease advancement, understanding occurrence and re-occurrence of infection and view treatment response. Among all cancer kinds, Breast Cancer (BC) now-a-days has turn out to be a common form of cancer amongst ladies around the world and is the second common cause of most cancers deaths. At present, there aren't any powerful methods to save you and remedy breast cancer, because its cause is not absolutely known. Early detection is the most effective way to enhance breast cancer survivals and might deliver a better hazard of full healing. The main motive of this paper is to implement various efficient image segmentation techniques after preprocessing histopathology BC images of different magnification level and exhaustively compare them to achieve best results. We analyze the outcomes of widely used edge detection based image segmentation methods such as Adaptive K-Means, Multi Class Fuzzy C-Means, Canny, Gabor filter and Homogeneity based PSO and evaluate them via metrics which includes accuracy, precision, recall and F-degree. Various preprocessing strategies like histogram equalization (HE), Adaptive equalization (AE) and Contrast Stretching (CS) are compared and used to enhance the performance of above segmentation methods. Contrast Improvement Index (CII) is chosen as a selection criteria for preprocessed image. Finally one vs. all multiclass SVM classifier is used for BC image classification. Our implementation uses breast cancer dataset having two classes as benign and malignant each in turn having four sub-classes. Images of different magnification levels such as 40x, 100x, 200x and 400x, are considered for BC detection.

An Automated Method for MRI Based Brain Tumor Detection using Berkeley Wavelet Transformation and Support Vector Machine

In this research work, a new automated system is developed for brain tumor detection by using Magnetic Resonance Imaging (MRI) on the basis of machine learning techniques. The major concerns in the brain tumor detection are time consuming, and the classification accuracy dependsonly on clinician’s experience. To address these issues, a new supervised system is developed for brain tumor detection. In this research study, a new segmentation approach was used for improving the brain tumor detection performance and to diminish the complexity of the system. Initially, Anisotropic Diffusion Filter (ADF) was used as an image pre-processing technique for removing noise from the collected brain image. Then, Berkeley Wavelet Transformation (BWT) was utilized for converting the spatial form of pre-processed MRI image into temporal domain frequency. Besides, Support Vector Machine (SVM) was usedas a classification technique to classify the normal and abnormal regions. SVM classifier effectively diminishes the size of resulting dual issue by developing a relaxed classification error bound. In addition, the undertaken classification approach quickly speed up the training process by maintaining a competitive classification accuracy. From the experimental analysis, the proposed system improved dice coefficient >0.9 compared to the existing systems. The experimental investigation validated and evaluated that the proposed system showed good performance related to the existing systems in light of dice coefficient and accuracy.

Preliminary value of CT radiomics in predicting anaplastic lymphoma kinase fusion gene expression in lung adenocarcinoma

Objective To explore the value of quantitative CT radiomics features in predicting the anaplastic lymphoma kinase (ALK) mutation status in lung adenocarcinoma patients. Methods This retrospective study reviewed one hundred and ninety-five lung adenocarcinoma patients (including 60 patients with ALK mutation) whose ALK genetic test results were available from Nov 2015 to May 2018 in PUMCH. VOIs were labeled by an automatic pulmonary nodule detection and segmentation algorithm and were later revised and confirmed by two senior radiologists. The PyRadiomics tools were used to resample the labeled regions, followed by image pre-processing (Wavelet filter or Laplacian of Gaussian (LoG) filter) and feature extraction. Normalized features were selected based on their representativeness on Dr. Wise research platform. Multivariate logistic regression was performed to develop prediction models of ALK mutation gene based on different image pre-processing techniques and different radiomics feature types. The results were validated by ten runs of five-fold cross validation. ROC curve analysis and Delong test were used to compare the predictive performance among models. Results Fifteen radiomics features with the highest representativeness were selected from the original 1 232 features. The prediction model based on these radiomics features showed good performance (AUC=0.88 in the training set and 0.78 in the validation set) and was not significantly different from the prediction models based on radiomics features of different pre-processing images (AUC=0.76, P=0.1, original CT images; AUC=0.75, P=0.3, Wavelet-filtered images; AUC=0.76, P=0.2, LoG-filtered images). Among the models built with radiomics features of different types, the one based on GLCM feature (a subtype of texture feature) showed the best performance in predicting ALK genetic status (AUC=0.83, accuracy=0.74, sensitivity=0.85 and specificity=0.69). The model based on first-order statistic features had an AUC of 0.80. Conclusion Quantitative CT radiomics features have a good potential to anticipate the expression of ALK fused gene in patients with lung adenocarcinoma. Key words: Lung neoplasm; Radiomics; Tomography, X-ray computed; Anaplastic lymphoma kinase

Instance Segmentation on Real time Object Detection using Mask R CNN

In the ever-advancing field of computer vision, image processing plays a prominent role. We can extend the applications of Image processing into solving real-world problems like substantially decreasing Human interaction over the art of driving. In the process of achieving this task, we face several challenges like Segmentation and Detection of objects. The proposed thesis overcomes the challenges effectively by introducing Instance segmentation and Binary masks along with Keras and Tensorflow. Instance segmentation is used to delineate and detect every unique object of interest according to their pixel characteristics in an image. Mask RCNN is the superior model over the existing CNN models and yields accurate detection of objects more efficiently. Unlike conventional Neural Networks which employs selective search algorithm to identify object of interest, Mask RCNN employs Regional Proposal Networks(RPN) to identify object of interest. For better results Image pre-processing techniques and morphological transformations are employed to reduce the noise and increase pixel clarity.

Brain structural disorders detection and classification approaches: a review

This paper is an effort to encapsulate the various developments in the domain of different unsupervised, supervised and half supervised brain anomaly detection approaches or techniques proposed by the researchers working in the domain of the Medical image segmentation and classification. As researchers are constantly working hard in the domain of image segregation, interpretation and computer vision in order to automate the task of tumour segmentation, anomaly detection, classification and other structural disorder prediction at an early stage with the aid of computer. The different medical imaging modalities are used by the doctors in order to diagnose the brain tumour and other structural brain disorders which are an integral part of diagnosis and prognosis process. When these different medical image modalities are used along with various image segmentation methods and machine learning approaches tends to perform brain structural disorder detection and classification in a semi-automated or fully automated manner with high accuracy. This paper presents all such approaches using various medical image modalities for the accurate detection and classification of brain tumour and other brain structural disorders. In this paper, all the major phases of any brain tumour or brain structural disorder detection and classification approach is covered begin with the comparison of various medical image pre-processing techniques then major segmentation approaches followed by the approaches based on machine learning. This paper also presents an evaluation and comparison among the various popular texture and shape based feature extraction methods used in combination with different machine learning classifiers on the BRATS 2013 dataset. The fusion of MRI modalities used along with the hybrid features extraction methods and ensemble model delivers the best result in terms of accuracy.