Otsu's Approach Research Articles

LRFE: A NOVEL LOCAL RESPONSE FEATURE ELIMINATION PROCESS FOR IDENTIFICATION OF LUNG CANCER CELLS

One of the main causes of cancer-related mortality across the globe is lung cancer. Early-stage lung cancer frequently exhibits no symptoms, which delays diagnosis until the illness has progressed. Before symptoms manifest, screening and early detection techniques can aid in the early diagnosis and treatment of lung cancer. Many olden research papers have implemented image processing and few latest papers have implemented computer vision techniques to detect lung cancer. Particularly when dealing with minor or subtle anomalies, image processing algorithms may not be able to detect lung cancer lesions with sufficient sensitivity and specificity. It is still difficult to increase the detection algorithms' accuracy and dependability, especially when dealing with early-stage lesions or situations where attributes overlap. It takes a lot of processing power, such as high-performance GPUs and enormous memory capacities, to train deep learning models, particularly large-scale convolutional neural networks (CNNs). In this proposed research, the model pre-processes the images using the ostu and sober filter mechanisms because Otsu's approach adjusts to the features of the input image, including noise, contrast, and lighting fluctuations. It is capable of handling images with varying dynamic ranges and intensity distributions without depending on pre-established threshold settings. When it comes to image noise, the Sobel filter is more resilient than other edge detection methods. It produces clearer edge maps and fewer false detections by determining the gradient magnitude, which amplifies edge information while suppressing noise. The features are extracted using the tuned AlexNet pre-trained model, in AlexNet there is a layer known as “Layer-wise Relevance Propagation”. By giving each pixel or feature in the input image a relevance score, the LRP layer offers fine-grained feature attribution. This makes it possible to analyze in great depth which particular elements or areas of the input image are most important for the network to forecast, which helps to clarify the underlying patterns that the network has learned. At last, the extracted features are further reduced using the enhanced feature elimination method. By iteratively selecting subsets of features based on their importance, RFE helps to identify the most relevant features for the classification task. Integrating RFE with SVM classifiers can lead to improved model performance by focusing on the subset of features that are most discriminative and informative for the classification problem.

Development of a method for computer processing of fractographic images to assess the cohesion of inclusions to the matrix in the weld metal after its operational degradation and hydrogenation

In this paper, the method for automated processing of digital fractographic SEM images based on the modified local Otsu approach was developed. The effectiveness of the developed method was demonstrated. The method is used to identify inclusions or their traces at the bottom of dimples in weld metal fractures with subsequent assessment of their quantity. It is assumed that a decrease in the cohesion of inclusions to the matrix is one of the signs of damage to the weld metal at the microstructural level. Consequently, the more inclusions were found at the bottom of ductile fracture dimples, the stronger their connection with the matrix and, accordingly, the less damage in the weld metal. The effect of operational degradation of the weld metal on a circumferential weld on thick pipes of thermal power plant steam pipelines is analyzed. For this purpose, the features of cohesion of inclusions with the matrix in the weld metal in the initial state and after operation on the main steam pipeline for 23 years were compared. The influence of hydrogen (absorbed by the weld metal during long-term operation, as well as during its additional electrolytic charging) on the cohesion of inclusions in its structure with the surrounding matrix is also considered. A decrease in cohesion between these two components of the weld metal structure under the influence of all analized factors is shown. Thus, a method for quantitative assessment of fractographic features caused by the decohesion of inclusions from the matrix developed on the basis of analysis and computer processing of scanning electron microscope images, can be used to assess the operational damage of the weld metal at the microscopic level.

Gabor wavelet transform combined with area CNN in appearance intelligent detection of stayed cables

The major structure for transmitting forces in a cable-stayed bridge is the stay cable. If the stay cable system fails, the whole bridge will collapse. At present, there is no very mature technical means for the detection of bridge cables. The most common method is for workers to perform regular manual inspection and replacement of cables. Although the defect condition of the cable can be detected to a certain extent, the real-time monitoring of its health cannot be realized. Therefore, the research used the median filter, mean standardization method and Retinex algorithm to preprocess the image data. And after extracting the stay cable picture’s macroscopic characteristics using the Gabor wavelet transform, it implemented image segmentation using the OTSU approach. The obtained feature images were applied to the Faster Regional Convolution Neural Network (RCNN) recognition model to detect microscopic defects. During the research, the training efficiency of traditional Convolution Neural Network (CNN) was not high. Therefore, Faster RCNN built a cable defect recognition model. Based on this, the research built an intelligent detection model for apparent defects of stay cables based on Gabor wavelet transform and improved RCNN. Through the experimental analysis, the model built for the study has a recognition accuracy rate of 94.14 %, which can achieve dynamic bridge health monitoring and instantly identify the condition of the stay cables, and maintain the safety of the bridge.

Multi-level thresholding image segmentation for rubber tree secant using improved Otsu's method and snake optimizer.

The main disease that decreases the manufacturing of natural rubber is tapping panel dryness (TPD). To solve this problem faced by a large number of rubber trees, it is recommended to observe TPD images and make early diagnosis. Multi-level thresholding image segmentation can extract regions of interest from TPD images for improving the diagnosis process and increasing the efficiency. In this study, we investigate TPD image properties and enhance Otsu's approach. For a multi-level thresholding problem, we combine the snake optimizer with the improved Otsu's method and propose SO-Otsu. SO-Otsu is compared with five other methods: fruit fly optimization algorithm, sparrow search algorithm, grey wolf optimizer, whale optimization algorithm, Harris hawks optimization and the original Otsu's method. The performance of the SO-Otsu is measured using detail review and indicator reviews. According to experimental findings, SO-Otsu performs better than the competition in terms of running duration, detail effect and degree of fidelity. SO-Otsu is an efficient image segmentation method for TPD images.

Concrete bridge crack detection by image processing technique by using the improved OTSU method

Crack detection is a critical part of observing the structural health and guaranteeing structural safety and moreover, is time consuming factors. The inspectors find difficult to identify the exact depth or size of the concrete bridge crack by visual methods and other traditional image processing methods. This makes to establishes a smart and easy way to identify and analyses the crack. The proposed method is the gray intensity adjustment model. To preprocess the image, the gray level discrimination approach is used. The method is used to improve the accuracy of the crack detection results (area and orientation).This method will decrease the failure on the identification of cracks. The Otsu method was used to set the range of threshold. The Sobel’s filter is deployed to detect the crack on the edges of image pixels. This strategy on various design investigation calculation which yield the best test brings about computerized pictures. The findings demonstrate that the successful detection of objects in a picture can be achieved by combining the OTSU approach and the gray level discrimination method. The built extension model will be the value apparatuses for the administration, administrations and development engineers in the mission of support in span. By the proposed method, the detection of bridge crack accuracy will be a maximum of 95%; but the limitations for the user approach is the aspect ratio RA and margin parameter.

Increasing The Taxonomic Accuracy of Remote Sensing Data Using Traditional Pattern Recognition Methods

The most significant outcomes of remote sensing are maps of Land Use and Land Cover (LULC), which can be controlled by a procedure known as image classification. Using image processing methods, we are developing a system in this effort to categorize satellite photos and extract information. Satellite pictures have been classified into usable and unused areas, as well as sub-classifying each class into numerous further classes. Used satellite photos are further divided into residential, commercial, highway, and agricultural areas, while unused images are divided into forest, river, desert, and beach areas. Since K-means classifiers analyze images features and Otsu's approach for multilevel image thresholding is efficient for automatic classification of satellite images, that is the main topic of this research.

Cast Shadow Angle Detection in Morphological Aerial Images Using Faster R-CNN

With the tremendous advancements in digital image processing technology over the last few years, it is now possible to resolve many challenging issues. In light of this, this study proposes that digital image processing can be used to detect shadows in photographs. Since unmanned aerial vehicles and satellite devices have become more common image generating devices. The significant issue in the generated images is of its shadow. Shadows are inevitable in remote sensing photographs, particularly in metropolitan environments, due to the block of high-rise objects and the influence of the sun's altitude. This results in missing information in the shadow zone. The state-of-the-art shadow detection algorithms require manual alignment and predefined specific parameters. Most of those existing algorithms fail to deliver precise results in a variety of lighting and ecological conditions. To overcome these limitations, we propose a framework Multi Layered Linked approach with Tagged Feature Model for Shadow Angle Detection (MLTFM-SAD). The aim of the proposed model is to detect the shadows from aerial photographs and angle of those shadows. The proposed framework is a three-step approach. Initially, the image segmentation is applied on the input images. Second, hybrid Gaussian mixing mode and Otsu's approach is applied on the segmented shadow mask map and corresponding pixel set is generated. As a result, an initial shadow mask map is refined using object spectral attributes and spatial correlations between objects. Finally, the angle at which the shadow appears in the given image is recognised and analysed. The proposed method's performance is compared to that of all current approaches and the results revealed that the proposed model performance levels are superior.

Enhanced Fuzzy Elephant Herding Optimization-Based OTSU Segmentation and Deep Learning for Alzheimer’s Disease Diagnosis

Several neurological illnesses and diseased sites have been studied, along with the anatomical framework of the brain, using structural MRI (sMRI). It is critical to diagnose Alzheimer’s disease (AD) patients in a timely manner to implement preventative treatments. The segmentation of brain anatomy and categorization of AD have received increased attention since they can deliver good findings spanning a vast range of information. The first research gap considered in this work is the real-time efficiency of OTSU segmentation, which is not high, despite its simplicity and good accuracy. A second issue is that feature extraction could be automated by implementing deep learning techniques. To improve picture segmentation’s real-timeliness, enhanced fuzzy elephant herding optimization (EFEHO) was used for OTSU segmentation, and named EFEHO-OTSU. The main contribution of this work is twofold. One is utilizing EFEHO in the recommended technique to seek the optimal segmentation threshold for the OTSU method. Second, dual attention multi-instance deep learning network (DA-MIDL) is recommended for the timely diagnosis of AD and its prodromal phase, mild cognitive impairment (MCI). Tests show that this technique converges faster and takes less time than the classic OTSU approach without reducing segmentation performance. This study develops a valuable tool for quick picture segmentation with good real-time efficiency. Compared to numerous conventional techniques, the suggested study attains improved categorization performance regarding accuracy and transferability.

Rough Set and Otsu Approach based Hybrid Image Classification under Uneven Lighting Conditions

Rough Set and Otsu Approach based Hybrid Image Classification under Uneven Lighting Conditions

Automatic Segmentation of Retinal Capillaries in Adaptive Optics Scanning Laser Ophthalmoscope Perfusion Images Using a Convolutional Neural Network.

PurposeAdaptive optics scanning laser ophthalmoscope (AOSLO) capillary perfusion images can possess large variations in contrast, intensity, and background signal, thereby limiting the use of global or adaptive thresholding techniques for automatic segmentation. We sought to develop an automated approach to segment perfused capillaries in AOSLO images.Methods12,979 image patches were extracted from manually segmented AOSLO montages from 14 eyes and used to train a convolutional neural network (CNN) that classified pixels as capillaries, large vessels, background, or image canvas. 1764 patches were extracted from AOSLO montages of four separate subjects, and were segmented manually by two raters (ground truth) and automatically by the CNN, an Otsu's approach, and a Frangi approach. A modified Dice coefficient was created to account for slight spatial differences between the same manually and CNN-segmented capillaries.ResultsCNN capillary segmentation had an accuracy (0.94), a Dice coefficient (0.67), and a modified Dice coefficient (0.90) that were significantly higher than other automated approaches (P < 0.05). There were no significant differences in capillary density and mean segment length between manual ground-truth and CNN segmentations (P > 0.05).ConclusionsClose agreement between the CNN and manual segmentations enables robust and objective quantification of perfused capillary metrics. The developed CNN is time and computationally efficient, and distinguishes capillaries from areas containing diffuse background signal and larger underlying vessels.Translational RelevanceThis automatic segmentation algorithm greatly increases the efficiency of quantifying AOSLO capillary perfusion images.

Object-Oriented Key Point Vector Distance for Binary Land Cover Change Detection Using VHR Remote Sensing Images

Very high-resolution (VHR) remote sensing images can geometrically depict ground targets in detail but are usually insufficient in the spectral domain. This characteristic leads to a considerable amount of noise and pseudo change in the produced binary change detection maps (BCDMs) when VHR remote sensing images are used for change detection. Here, to solve the aforementioned problem, an object-oriented key point vector distance (KPVD) is proposed to measure the change magnitude between bitemporal VHR images when land cover changes are detected. The proposed KPVD-based change detection approach comprises the following major steps. First, multiscale objects based on a postevent image are extracted by the fractional net evaluation segmentation approach, and then, the segments are taken as the unit for measuring the change magnitude between bitemporal images. Second, key points and the corresponding vector are defined to describe the object feature instead of using the total pixels within the object. Finally, KPVD is proposed to measure the change magnitude between the local areas referenced to the object in the bitemporal images. The change magnitude image (CMI) between the bitemporal images is generated while the entire images are scanned and processed object by object. A well-known automatic binary method, the Otsu approach, is employed in this article to divide CMI into a BCDM. Experimental results conducted on four real data sets demonstrate the feasibility and outperformance of the proposed KPVD-based change detection approach compared with five state-of-the-art methods in terms of visual performance and quantitative measurements.

Mobile Screening Framework of Anterior Segment Photographed Images

This work presents a qualitative measurement of anterior segment photographed images (ASPIs) to identify between normal eyes and eyes with pterygium and pinguecula through Otsu multi-thresholding approach without contrast enhancement. In addition, we also propose a mobile screening framework of ASPIs through smartphones. ASPIs were directly sent to the cloud storage once an ASPI was captured using a smartphone camera, and then each image was processed through a digital image processing approach in a processing platform. Three important steps, namely, pre-processing, image segmentation and qualitative assessment, are involved in the processing platform of the mobile screening framework. The ASPIs are pre-processed to minimise or eliminate any unwanted areas within the image. Then, these ASPIs are segmented through multi-thresholding Otsu approach with clustering number n = 3. Segmentation result shows that the accuracy of the proposed method is 87.5%, which is comparable with the previously established work that has applied three-step differencing (3SD) method. However, the proposed approach has better computational time which is six times faster than the 3SD method. These results demonstrate a remarkable effort to produce a simple but straightforward digital image processing approach to be implemented in cloud computing for future studies.

A Novel Supervised Bi-Level Thresholding Technique Based on Particle Swarm Optimization

Thresholding is an important step in many computer vision tasks such as image segmentation. However, selection of the optimal threshold value often appears as a challenging problem to researchers. In this paper, a novel approach for determining of the optimal threshold value is proposed which works based on supervised version of particle swarm optimization (PSO) method. Our proposed technique employs the ability of Otsu method for minimizing within-class variance as well as a preprocessing step aimed at transferring maximum region uniformity to output image. In preprocessing stage, we attempt to maintain as much information as possible by obtaining a Canonical image which includes the most visually important features of the original image. Afterwards, the PSO algorithm is applied to find the optimal threshold value. The PSO method chooses the initial values by exploiting Otsu approach along with an objective function which maximizes the similarity to the Canonical image. The objective function utilizes the sequential connected component labelling algorithm. The presented approach has been tested against standard datasets for comparison with well-known thresholding methods in the literature. Experimental results demonstrate the superiority of our algorithm to other methods in both subjective and objective evaluations.

A comparison of different automated methods for the detection of white matter lesions in MRI data

White matter hyperintensities (WMH) are the focus of intensive research and have been linked to cognitive impairment and depression in the elderly. Cumbersome manual outlining procedures make research on WMH labour intensive and prone to subjective bias. This study compares fully automated supervised detection methods that learn to identify WMH from manual examples against unsupervised approaches on the combination of FLAIR and T1 weighted images. Data were collected from ten subjects with mild cognitive impairment and another set of ten individuals who fulfilled diagnostic criteria for dementia. Data were split into balanced groups to create a training set used to optimize the different methods. Manual outlining served as gold standard to evaluate performance of the automated methods that identified each voxel either as intact or as part of a WMH. Otsu's approach for multiple thresholds which is based only on voxel intensities of the FLAIR image produced a high number of false positives at grey matter boundaries. Performance on an independent test set was similarly disappointing when simply applying a threshold to the FLAIR that was found from training data. Among the supervised methods, precision-recall curves of support vector machines (SVM) indicated advantages over the performance achieved by K-nearest-neighbor classifiers (KNN). The curves indicated a clear benefit from optimizing the threshold of the SVM decision value and the voting rule of the KNN. Best performance was reached by selecting training voxels according to their distance to the lesion boundary and repeated training after replacing the feature vectors from those voxels that did not form support vectors of the SVM. The study demonstrates advantages of SVM for the problem of detecting WMH at least for studies that include only FLAIR and T1 weighted images. Various optimization strategies are discussed and compared against each other.

Ore image thresholding using bi-neighbourhood Otsu's approach

A new thresholding algorithm based on two neighbourhoods and Otsu&apos;s method is proposed. For each pixel, its two neighbourhoods are chosen by image and object sizes. The thresholds of neighbourhoods are optimised by Otsu&apos;s method, and then every pixel is binarised through the minimum of thresholds. Experimental results show that the proposed algorithm improves thresholding performance efficiently, especially for multi-object and uneven lighting images.

Fabric defect detection method based on texture structure analysis

A new fabric defect segmentation was proposed based on texture structure analysis in this paper.Firstly in accordance with the characteristics of the well-regulated texture,texture primitive template was calculated using auto-correlation function and defect image was enhanced using difference between each texture primitive and primitive template.Secondly local roughness of texture image was calculated to locate defect,and defect image was segmented using threshold value gained according to Otsu's approach to detect fabric defect.Lastly validity and robustness of this approach is proved by different fabric defect image segmentation in our experiment.