Local Adaptive Threshold Method Research Articles

Evaluation of thresholding methods for the quantification of [68Ga]Ga-PSMA-11 PET molecular tumor volume and their effect on survival prediction in patients with advanced prostate cancer undergoing [177Lu]Lu-PSMA-617 radioligand therapy

PurposeThe aim of this study was to systematically evaluate the effect of thresholding algorithms used in computer vision for the quantification of prostate-specific membrane antigen positron emission tomography (PET) derived tumor volume (PSMA-TV) in patients with advanced prostate cancer. The results were validated with respect to the prognostication of overall survival in patients with advanced-stage prostate cancer.Materials and methodsA total of 78 patients who underwent [177Lu]Lu-PSMA-617 radionuclide therapy from January 2018 to December 2020 were retrospectively included in this study. [68Ga]Ga-PSMA-11 PET images, acquired prior to radionuclide therapy, were used for the analysis of thresholding algorithms. All PET images were first analyzed semi-automatically using a pre-evaluated, proprietary software solution as the baseline method. Subsequently, five histogram-based thresholding methods and two local adaptive thresholding methods that are well established in computer vision were applied to quantify molecular tumor volume. The resulting whole-body molecular tumor volumes were validated with respect to the prognostication of overall patient survival as well as their statistical correlation to the baseline methods and their performance on standardized phantom scans.ResultsThe whole-body PSMA-TVs, quantified using different thresholding methods, demonstrate a high positive correlation with the baseline methods. We observed the highest correlation with generalized histogram thresholding (GHT) (Pearson r (r), p value (p): r = 0.977, p < 0.001) and Sauvola thresholding (r = 0.974, p < 0.001) and the lowest correlation with Multiotsu (r = 0.877, p < 0.001) and Yen thresholding methods (r = 0.878, p < 0.001). The median survival time of all patients was 9.87 months (95% CI [9.3 to 10.13]). Stratification by median whole-body PSMA-TV resulted in a median survival time from 11.8 to 13.5 months for the patient group with lower tumor burden and 6.5 to 6.6 months for the patient group with higher tumor burden. The patient group with lower tumor burden had significantly higher probability of survival (p < 0.00625) in eight out of nine thresholding methods (Fig. 2); those methods were SUVmax50 (p = 0.0038), SUV ≥3 (p = 0.0034), Multiotsu (p = 0.0015), Yen (p = 0.0015), Niblack (p = 0.001), Sauvola (p = 0.0001), Otsu (p = 0.0053), and Li thresholding (p = 0.0053).ConclusionThresholding methods commonly used in computer vision are promising tools for the semiautomatic quantification of whole-body PSMA-TV in [68Ga]Ga-PSMA-11-PET. The proposed algorithm-driven thresholding strategy is less arbitrary and less prone to biases than thresholding with predefined values, potentially improving the application of whole-body PSMA-TV as an imaging biomarker.

Multiple weld seam laser vision recognition method based on the IPCE algorithm

Robotic weldingis a critical technology in manufacturing. Vision sensor is critical for increasing the flexibility and adaptability of robot welding, and the line-structured laser(LSL) visionsensor, in particular, is a long-standing research hotspot in robot intelligent welding.The key technology of the LSL vision sensor used in robot welding is the weld seam recognition algorithm.However, the common weld seam recognition algorithm is hampered by heavyarc and spatter noise during welding. Furthermore, many common weld seam recognition methods can only recognize specifictypes of weld seams and have limitations in terms of flexibility and robustness. To overcome thesechallenges,in this paper, we present a fast, accurate, and robustmultiple-type weld seam recognition algorithm.The algorithm consists primarily of our proposed dynamic ROI method, local adaptive threshold method, internal propulsioncenter extracting(IPCE)algorithm, a weld seam edge point detection operator, and a center analyzing method. Experiment results show that the proposed algorithm can recognize multiple types of weld seams, including I-groove butt weld seams, V-groove butt weld seams, single bevel groove butt weld seams, lap weld joints, etc., even in the presence of heavy arc and spatter noise.

Oil Spill Monitoring of Shipborne Radar Image Features Using SVM and Local Adaptive Threshold

In the case of marine accidents, monitoring marine oil spills can provide an important basis for identifying liabilities and assessing the damage. Shipborne radar can ensure large-scale, real-time monitoring, in all weather, with high-resolution. It therefore has the potential for broad applications in oil spill monitoring. Considering the original gray-scale image from the shipborne radar acquired in the case of the Dalian 7.16 oil spill accident, a complete oil spill detection method is proposed. Firstly, the co-frequency interferences and speckles in the original image are eliminated by preprocessing. Secondly, the wave information is classified using a support vector machine (SVM), and the effective wave monitoring area is generated according to the gray distribution matrix. Finally, oil spills are detected by a local adaptive threshold and displayed on an electronic chart based on geographic information system (GIS). The results show that the SVM can extract the effective wave information from the original shipborne radar image, and the local adaptive threshold method has strong applicability for oil film segmentation. This method can provide a technical basis for real-time cleaning and liability determination in oil spill accidents.

Continuous speech segmentation using local adaptive thresholding technique in the blocking block area method

Continuous speech is a form of natural human speech that is continuous without a clear boundary between words. In continuous speech recognition, a segmentation process is needed to cut the sentence at the boundary of each word. Segmentation becomes an important step because a speech can be recognized from the word segments produced by this process. The segmentation process in this study was carried out using local adaptive thresholding technique in the blocking block area method. This study aims to conduct performance comparisons for five local adaptive thresholding methods (Niblack, Sauvola, Bradley, Guanglei Xiong and Bernsen) in continuous speech segmentation to obtain the best method and optimum parameter values. Based on the results of the study, Niblack method is concluded as the best method for continuous speech segmentation in Indonesian language with the accuracy value of 95%, and the optimum parameter values for such method are window = 75 and k = 0.2.

Marine Radar Oil-Spill Monitoring through Local Adaptive Thresholding

Marine oil spills affect the environment, economy, and quality of life for coastal inhabitants. This article presents a method of X-band marine radar oil-spill identification by considering the marine radar images of the 2010 Dalian 7-16 accident. The Prewitt operator was improved and a linear interpolation was proposed to suppress co-channel interferences. In addition, a model of a gray-intensity-correcting matrix is proposed to smooth a whole image, thus displaying the oil film more intuitively. Furthermore, a contrast-limited adaptive histogram equalization method was used to increase the contrast inside and outside the oil film. Moreover, the local adaptive thresholding method was improved to segment the oil spills. The results show that the proposed method is an improvement on similar previous approaches for this task when employing X-band marine radar images. The proposed method can provide technical and theoretical bases for emergency response, damage assessment, and liability identification of oil spills.

Reproducibility of Macular Vessel Density Calculations Via Imaging With Two Different Swept-Source Optical Coherence Tomography Angiography Systems.

PurposeTo evaluate the reproducibility of vessel density calculations using different binarization methods obtained via two commercially available swept-source optical coherence tomography angiography (SS-OCTA) systems.MethodsHealthy volunteers were imaged using two swept-source optical coherence tomography angiography (SS-OCTA) devices, PLEXElite and Triton. SS-OCTA examinations were performed using a 3 × 3-mm volume scan pattern centered on the fovea. A total of six methods were used for binarization in ImageJ, two global thresholding and four local adaptive thresholding methods. Resultant vessel density values were compared between the instruments and binarization methods. Images for 60 eyes from 30 healthy subjects were assessed by two reviewers who were blinded to the scanning system used.ResultsTwenty-two eyes were excluded due to poor image quality (17 eyes from Triton, 4 eyes from PLEXElite, and 1 eye from both instruments, P = 0.003). A final 38 eyes from 23 subjects were eligible for analysis. Each binarization method and instrument led to different median values. The coefficients of variation for vessel density measurements ranged from 0.3% to 2.3% and 0.6% to 4.7% for the PLEXElite and Triton, respectively. Local adaptive thresholding methods revealed higher reproducibility than did global thresholding methods for both devices.ConclusionsMacular scans with both SS-OCTA instruments showed good reproducibility for vessel density measurements. PLEXElite recorded fewer poor images and had higher reproducibility than did Triton. These findings will inform the selection of proper binarization methods for the clinical detection of vascular diseases affecting the central retina.Translational RelevanceThe reproducibility for macular vessel measurements with SS-OCTA instruments was good. PLEXElite recorded fewer poor images and had higher reproducibility than did Triton.

Local adaptive threshold segmentation method for subapture spots of Shack-Hartmann sensor

The accuracy of centroid estimation for Shcak-Hartmann wavefront sensor is highly dependent on noise, especially for the centre of gravity (CoG) method. Therefore, threshold selection is very important. This paper proposes a local adaptive threshold segmentation method based on statistical rank, which can reduce the influence of uneven background noise and decrease the wavefront reconstruction error more effectively, comparing with the traditional global threshold method. An experiment measuring static aberration was conducted, the accuracy of centroid estimation and wavefront reconstruction both testify the effectiveness of this method. Besides, we found that combing the local adaptive threshold method and intensity weighted centroiding (IWC) method can improve the performance of traditional centre of gravity method. It achieves higher centroiding accuracy under SNRp between 10~40 conditions.

The blending interpolation algorithm based on image features

In this paper, we develop a blending interpolation model with help of classical bivariate rational interpolation. The blending model is an organic unity which integrates rational fractal interpolation with bivariate rational interpolation, and it is identified uniquely by the values of scaling factor and shape parameters. Fractal is a powerful tool for the description of the complexity and irregularity of geometric objects, and we introduce the fractal dimension to describe texture. A new local adaptive threshold method which based on local fractal dimension (LFD) is proposed to divide images into texture region and non-texture region. Rational fractal interpolation and rational interpolation are used in texture region and non-texture region respectively. Especially in rational fractal interpolation model, a new method for calculating the scaling factor is proposed, which exploits the relationship between global fractal dimension (GFD) and LFD. Finally, an approach of selecting shape parameters is utilized to further improve the quality of interpolated image. Our extensive experimental results demonstrate that the proposed blending model based image features achieves competitive performance with the state-of-the-art interpolation algorithms.

Efficient local adaptive thresholding for night-time vehicle candidate detection

In the current commercial automotive market, the need for intelligent headlight control systems has increased more and more. Camera-based night-time vehicle detection has become a crucial issue in determining the performance of such control systems. The purpose of this paper is to offer an answer to the question, 'Which thresholding method is suitable in terms of detection performance for a night-time vehicle candidate selection process?' For such purposes, two local adaptive thresholding methods are introduced and tested. One is local maximum-based thresholding, and the other is local mean-based thresholding. Efficient implementation methodologies are also introduced for real-time processing. Through the simulations tested on road image sequences with different exposure times, we prove that local adaptive thresholding methods have better performance than other well-known global thresholding methods. In particular, the simulations show that the proposed mean-based thresholding method has better performance on both long- and short-exposure sequences.

Retinal Blood Vessels Segmentation Using the Curvlet Transform

Retinal image having very vital information. It plays important roles in finding of some diseases in early stages, such as diabetes, and cardiovascular disease. In this proposed system a new algorithm used to detect the blood vessels effectively from the retinal image. The initial image enhancement is carried out by using Adaptive Histogram Equalization, followed by the curvelet Transforms are applied to the equalized image and the curvelet coefficients are obtained. The modifications to the Curvelet transform coefficients are carried out by suppressing all the coefficients of one band. This combined effect of the equalization and the Curvelet Transforms provides a better enhancement to the image. This enhanced image is used for the extraction of blood vessels. Afterward, eliminate the ridges not belonging to the vessels tree by morphological operators by reconstruction while trying to preserve the thin vessels unchanged. In order to increase the efficiency of the morphological operators by reconstruction, they were applied using multi-structure elements and local adaptive thresholding method along with connected components analysis (CCA) indicates the remained ridges belonging to vessels.

A Location Method for Multi-style License Plates Based on Character Classification

Abstract To locate the license plate exactly is a challenging task, especially for the license plates with complex backgrounds or shadows. Firstly, we use a multilevel local adaptive thresholding method instead of global thresholding methods to produce binary images under varying illuminations. Secondly, the connected contour analysis is applied to extract the main characters, and the false character contour regions are filtered out by the SVM classification method using PCA dimensionality reduction. Finally, we merge the remaining characters on the same line to extract candidate regions of the license plate. Experimental results show that the proposed method achieves higher accuracy, more acceptable and precise results. By using the data sets of the license plates in the USA and Europe, the method is shown to be suitable for multi-style license plates.

Movement Detection Based on Dense Optical Flow for Unmanned Aerial Vehicles

In this paper we present a novel computer vision-based movement detection algorithm which can be used for applications, such as human detection with unmanned aerial vehicles. The algorithm uses the deviation of all pixels from the anticipated geometry between 2 or more succeeding images to distinguish between moving and static scenes. This assumption is valid because only pixels which correspond to moving objects can violate the epipolar geometry. For the estimation of the fundamental matrix we present a new method for rejecting outliers which has, contrary to RANSAC, a predictable runtime and still delivers reliable results. To determine movement, especially in difficult areas, we introduce a novel local adaptive threshold method, a combined temporal smoothing strategy and further outlier elimination techniques. All this leads to promising results where more than 60% of all moving persons in our own recorded test set have been detected.

Saliency Map을 이용한 최적 임계값 기반의 객체 추출

이미지로부터 중요 객체를 추출하는 것은 추적, 분할, 적응적 압축, 내용기반 검색과 같은 멀티미디어 처리에 있어서 매우 중요한 부분이며, 현재 이에 관한 많은 연구가 진행 되고 있다. 중요 객체 추출을 위한 방법으로 Saliency Map을 이용한 방법이 있다. 이 방법에서는 일반적으로 이진화된 Saliency Map을 이용하여 어떤 화소가 중요 객체 내부인가 아닌가를 표시한다. 따라서 이 방법은 이진화를 위한 임계값의 선택이 성능에 매우 중요한 영향을 끼친다. 기존 연구에서는 일반적으로 휴리스틱 방법을 이용하여 임계값을 결정하거나 매개변수로 임계값을 조정하는 방법이 사용되었다. 그러나 하나의 임계값 적용은 이미지 안의 다수의 객체가 포함되어 있는 경우 적합하지 않다. 본 논문에서는 이러한 단점을 개선할 수 있는 Otsu 임계값을 이용한 전역적인 최적 임계값을 사용하는 방법을 제안한다. 제안하는 Otsu 임계화 방법은 단일-계층에 적용할 수 있는 Otsu 방법과 이를 확장하여 다중-계층에도 적용할 수 있는 Otsu 방법이다. 제안한 방법을 기존의 Saliency Map 모델에 적용한 결과 성능이 개선되었음을 확인하였다. Salient object attracts more and more attention from researchers due to its important role in many fields of multimedia processing like tracking, segmentation, adaptive compression, and content-base image retrieval. Usually, a saliency map is binarized into black and white map, which is considered as the binary mask of the salient object in the image. Still, the threshold is heuristically chosen or parametrically controlled. This paper suggests using the global optimal threshold to perform saliency map thresholding. This work also considers the usage of multi-level optimal thresholds and the local adaptive thresholds in the experiments. These experimental results show that using global optimal threshold method is better than parametric controlled or local adaptive threshold method.

Local Adaptive Thresholding of Pecan X-Ray Images: Reverse Water Flow Method

Segmentation of food product images by global thresholding methods has met with limited success due to unimodal image histograms and inhomogeneous backgrounds. Local adaptive thresholding methods have been successful in segmenting images with inhomogeneous backgrounds; however, there are limited studies applying them to food products. This article evaluates selected local adaptive thresholding methods and proposes a new faster method. The proposed method consists of modifications to the Oh water flow method. For pecan images, the proposed method and the Oh method were able to segment insect-eaten nutmeat and insect exit paths with orientation parallel to the x-ray beam. By adjusting the threshold, the proposed method was also able to segment insect exit paths with perpendicular orientation. The proposed method required only 38.9% of the computation time required by the Oh method. Two applications of the single Otsu threshold method and the Kim method both worked well for larger defects. Pixel misclassification error and relative foreground area error were used as objective indices to evaluate the segmentation results, and the results by the Oh method and the proposed method were comparable. The proposed method also performed well for sample citrus, metallic structure, and cell images. The proposed method is faster and simpler in approach, yet robust and accurate. Features can be extracted from the segmented images for defect classification, thus providing a step toward on-line non-destructive machine vision inspection. The proposed method should be extendable to images with unimodal histograms and inhomogeneous backgrounds.

Document image binarization based on topographic analysis using a water flow model

This paper proposes a local adaptive thresholding method based on a water flow model, in which an image surface is considered as a three-dimensional (3-D) terrain. To extract characters from backgrounds, we pour water onto the terrain surface. Water flows down to the lower regions of the terrain and fills valleys. Then, the thresholding process is applied to the amount of filled water for character extraction, in which the proposed thresholding method is applied to gray level document images consisting of characters and backgrounds. The proposed method based on a water flow model shows the property of locally adaptive thresholding. Computer simulation with synthetic and real document images shows that the proposed method yields effective adaptive thresholding results for binarization of document images.