Preprocessing of Iris Images for BSIF-Based Biometric Systems: Binary Detected Edges and Iris Unwrapping.

This work presents a novel approach to enhancing iris recognition systems through a two-module approach focusing on low-level image preprocessing techniques and advanced feature extraction. The primary contributions of this paper include: (i) the development of a robust preprocessing module utilizing the Canny algorithm for edge detection and the circle-based Hough transform for precise iris extraction, and (ii) the implementation of Binary Statistical Image Features (BSIF) with domain-specific filters trained on iris-specific data for improved biometric identification. By combining these advanced image preprocessing techniques, the proposed method addresses key challenges in iris recognition, such as occlusions, varying pigmentation, and textural diversity. Experimental results on the Human-inspired Domain-specific Binarized Image Features (HDBIF) Dataset, consisting of 1892 iris images, confirm the significant enhancements achieved. Moreover, this paper offers a comprehensive and reproducible research framework by providing source codes and access to the testing database through the Notre Dame University dataset website, thereby facilitating further application and study. Future research will focus on exploring adaptive algorithms and integrating machine learning techniques to improve performance across diverse and unpredictable real-world scenarios.

Automatic Switching of Electric Locomotive Power in Railway Neutral Sections Using Image Processing.

This article presents a computer vision-based approach to switching electric locomotive power supplies as the vehicle approaches a railway neutral section. Neutral sections are defined as a phase break in which the objective is to separate two single-phase traction supplies on an overhead railway supply line. This separation prevents flashovers due to high voltages caused by the locomotives shorting both electrical phases. The typical system of switching traction supplies automatically employs the use of electro-mechanical relays and induction magnets. In this paper, an image classification approach is proposed to replace the conventional electro-mechanical system with two unique visual markers that represent the 'Open' and 'Close' signals to initiate the transition. When the computer vision model detects either marker, the vacuum circuit breakers inside the electrical locomotive will be triggered to their respective positions depending on the identified image. A Histogram of Oriented Gradient technique was implemented for feature extraction during the training phase and a Linear Support Vector Machine algorithm was trained for the target image classification. For the task of image segmentation, the Circular Hough Transform shape detection algorithm was employed to locate the markers in the captured images and provided cartesian plane coordinates for segmenting the Object of Interest. A signal marker classification accuracy of 94% with 75 objects per second was achieved using a Linear Support Vector Machine during the experimental testing phase.

A highly accurate and semi-automated method for quantifying spherical microplastics based on digital slide scanners and image processing.

Microplastics (MPs), the emerging pollutants appeared in water environment, have grabbed significant attention from researchers. The quantitative method of spherical MPs is the premise and key for the study of MPs in laboratory researches. However, the manual counting is time-consuming, and the existing semi-automated analysis lacked of robustness. In this study, a highly accurate quantification method for spherical MPs, called VS120-MC was proposed. VS120-MC consisted of the digital slide scanner VS120 and the MPs image processing software, MPs-Counter. The full-area scanning photography was employed to fundamentally avoid the error caused by random or partition sampling modes. To accomplish high-performance batch recognition, the Weak-Circle Elimination Algorithm (WEA) and the Variable Coefficient Threshold (VCT) was developed. Finally, lower than 0.6% recognition error rate of simulated images with different aggregated indices was achieved by MPs-Counter with fast processing speed (about 2 s/image). The smallest size for VS120-MC to detect was 1 µm. And the applicability of VS120-MC in real water body was investigated. The measured value of 1 µm spherical MPs in ultra-pure water and two kinds of polluted water after digestion showed a good linear relationship with the Manual measurements (R2 = 0.982,0.987 and 0.978, respectively). For 10 µm spherical MPs, R2 reached 0.988 for ultra-pure water and 0.984 for both of the polluted water. MPs-Counter also showed robustness when using the same set of parameters processing the images with different conditions. Overall, VS120-MC eliminated the error caused by traditional photography and realized an accurate, efficient, stable image processing tool, providing a reliable alternative for the quantification of spherical MPs.

An Interpretable Neonatal Lung Ultrasound Feature Extraction and Lung Sliding Detection System Using Object Detectors.

The objective of this study was to develop an interpretable system that could detect specific lung features in neonates. A challenging aspect of this work was that normal lungs showed the same visual features (as that of Pneumothorax (PTX)). M-mode is typically necessary to differentiate between the two cases, but its generation in clinics is time-consuming and requires expertise for interpretation, which remains limited. Therefore, our system automates M-mode generation by extracting Regions of Interest (ROIs) without human in the loop. Object detection models such as faster Region Based Convolutional Neural Network (fRCNN) and RetinaNet models were employed to detect seven common Lung Ultrasound (LUS) features. fRCNN predictions were then stored and further used to generate M-modes. Beyond static feature extraction, we used a Hough transform based statistical method to detect "lung sliding" in these M-modes. Results showed that fRCNN achieved a greater mean Average Precision (mAP) of 86.57% (Intersection-over-Union (IoU) = 0.2) than RetinaNet, which only displayed a mAP of 61.15%. The calculated accuracy for the generated RoIs was 97.59% for Normal videos and 96.37% for PTX videos. Using this system, we successfully classified 5 PTX and 6 Normal video cases with 100% accuracy. Automating the process of detecting seven prominent LUS features addresses the time-consuming manual evaluation of Lung ultrasound in a fast paced environment. Clinical impact: Our research work provides a significant clinical impact as it provides a more accurate and efficient method for diagnosing lung diseases in neonates.

Deep Network-Assisted Quality Inspection of Laser Welding on Power Battery.

Reliable quality control of laser welding on power batteries is an important issue due to random interference in the production process. In this paper, a quality inspection framework based on a two-branch network and conventional image processing is proposed to predict welding quality while outputting corresponding parameter information. The two-branch network consists of a segmentation network and a classification network, which alleviates the problem of large training sample size requirements for deep learning by sharing feature representations among two related tasks. Moreover, coordinate attention is introduced into feature learning modules of the network to effectively capture the subtle features of defective welds. Finally, a post-processing method based on the Hough transform is used to extract the information of the segmented weld region. Extensive experiments demonstrate that the proposed model can achieve a significant classification performance on the dataset collected on an actual production line. This study provides a valuable reference for an intelligent quality inspection system in the power battery manufacturing industry.

Smart Iterative Analysis Tool for the Size Distribution of Spherical Nanoparticles.

The size of nanoparticles is a critical parameter with regard to their performance. Therefore, precise measurement of the size distribution is often required. While electron microscopy (EM) is a useful tool to image large numbers of particles at once, manual analysis of individual particles in EM images is a time-consuming and labor-intensive task. Therefore, reliable automatic detection methods have long been desired. This paper introduces a novel automatic particle analysis software package based on the circular Hough transform (CHT). Our software package includes novel features to enhance precise particle analysis capabilities. We applied the CHT algorithm in an iterative workflow, which ensures optimal detection over wide radius intervals, to deal with overlapping particles. In addition, smart intensity criteria were implemented to resolve common difficult cases that lead to false particle detection. Implementing these criteria enabled an effective and precise analysis by minimizing detection of false particles. Overall, our approach showed reliable particle analysis results by resolving common types of particle overlaps and deformation with only negligible errors.

Membrane Protein Detection and Morphological Analysis of Red Blood Cells in Hereditary Spherocytosis by Confocal Laser Scanning Microscopy.

In hereditary spherocytosis (HS), genetic mutations in the cell membrane and cytoskeleton proteins cause structural defects in red blood cells (RBCs). As a result, cells are rigid and misshapen, usually with a characteristic spherical form (spherocytes), too stiff to circulate through microcirculation regions, so they are prone to undergo hemolysis and phagocytosis by splenic macrophages. Mild to severe anemia arises in HS, and other derived symptoms like splenomegaly, jaundice, and cholelithiasis. Although abnormally shaped RBCs can be identified under conventional light microscopy, HS diagnosis relies on several clinical factors and sometimes on the results of complex molecular testing. It is specially challenging when other causes of anemia coexist or after recent blood transfusions. We propose two different approaches to characterize RBCs in HS: (i) an immunofluorescence assay targeting protein band 3, which is affected in most HS cases and (ii) a three-dimensional morphology assay, with living cells, staining the membrane with fluorescent dyes. Confocal laser scanning microscopy (CLSM) was used to carry out both assays, and in order to complement the latter, a software was developed for the automated detection of spherocytes in blood samples. CLSM allowed the precise and unambiguous assessment of cell shape and protein expression.

Standardized measurement of mid-surface shift of brain based on deep Hough transform.

The measurement of mid-surface shift (MSS), the geometric displacement between the actual mid-surface and the ideal midsagittal plane (iMSP), is of great significance for accurate diagnosis, treatment and prognosis of patients with intracranial hemorrhage (ICH). Most previous studies are subject to inherent inaccuracy on account of calculating midline shift (MLS) based on 2D slices and ignoring pathological conditions. In this study, we propose a novel standardized measurement model to quantify the distance and the overall volume of mid-surface shift (MSS-D, MSS-V). Our work has four highlights. First, we develop an end-to-end network architecture with multiple sub-tasks including the actual mid-surface segmentation, hematoma segmentation and iMSP detection, which significantly improves the efficiency and accuracy of MSS measurement by taking advantage of the common properties among tasks. Second, an efficient iMSP detection scheme is proposed based on the differentiable deep Hough transform (DHT), which converts and simplifies the plane detection problem in the image space into a keypoint detection problem in the Hough space. Third, we devise a sparse DHT strategy and a weighted least square (WLS) method to increase the sparsity of features, improving inference speed and greatly reducing computation cost. Fourth, we design a joint loss function to comprehensively consider the correlation of features between multi-tasks and multi-domains. Extensive validation on our large in-house dataset (519 patients) and the public CQ500 dataset (491 patients), demonstrates the superiority of our method over the state-of-the-art methods.

C2FTFNet: Coarse-to-fine transformer network for joint optic disc and cup segmentation.

Glaucoma is a leading cause of worldwide blindness and visual impairment, making early screening and diagnosis is crucial to prevent vision loss. Cup-to-Disk Ratio (CDR) evaluation serves as a widely applied approach for effective glaucoma screening. At present, deep learning methods have exhibited outstanding performance in optic disk (OD) and optic cup (OC) segmentation and maturely deployed in CAD system. However, owning to the complexity of clinical data, these techniques could be constrained. Therefore, an original Coarse-to-Fine Transformer Network (C2FTFNet) is designed to segment OD and OC jointly , which is composed of two stages. In the coarse stage, to eliminate the effects of irrelevant organization on the segmented OC and OD regions, we employ U-Net and Circular Hough Transform (CHT) to segment the Region of Interest (ROI) of OD. Meanwhile, a TransUnet3+ model is designed in the fine segmentation stage to extract the OC and OD regions more accurately from ROI. In this model, to alleviate the limitation of the receptive field caused by traditional convolutional methods, a Transformer module is introduced into the backbone to capture long-distance dependent features for retaining more global information. Then, a Multi-Scale Dense Skip Connection (MSDC) module is proposed to fuse the low-level and high-level features from different layers for reducing the semantic gap among different level features. Comprehensive experiments conducted on DRIONS-DB, Drishti-GS, and REFUGE datasets validate the superior effectiveness of the proposed C2FTFNet compared to existing state-of-the-art approaches.

Automated Industrial Composite Fiber Orientation Inspection Using Attention-Based Normalized Deep Hough Network.

Fiber-reinforced composites (FRC) are widely used in various fields due to their excellent mechanical properties. The mechanical properties of FRC are significantly governed by the orientation of fibers in the composite. Automated visual inspection is the most promising method in measuring fiber orientation, which utilizes image processing algorithms to analyze the texture images of FRC. The deep Hough Transform (DHT) is a powerful image processing method for automated visual inspection, as the "line-like" structures of the fiber texture in FRC can be efficiently detected. However, the DHT still suffers from sensitivity to background anomalies and longline segments anomalies, which leads to degraded performance of fiber orientation measurement. To reduce the sensitivity to background anomalies and longline segments anomalies, we introduce the deep Hough normalization. It normalizes the accumulated votes in the deep Hough space by the length of the corresponding line segment, making it easier for DHT to detect short, true "line-like" structures. To reduce the sensitivity to background anomalies, we design an attention-based deep Hough network (DHN) that integrates attention network and Hough network. The network effectively eliminates background anomalies, identifies important fiber regions, and detects their orientations in FRC images. To better investigate the fiber orientation measurement methods of FRC in real-world scenarios with various types of anomalies, three datasets have been established and our proposed method has been evaluated extensively on them. The experimental results and analysis prove that the proposed methods achieve the competitive performance against the state-of-the-art in F-measure, Mean Absolute Error (MAE), Root Mean Squared Error (RMSE).

An automated algorithm for the determination of oil absorption strategy of magnetic nanoparticles from SEM images.

In recent years, the magnetic iron oxide nanoparticles (MNPs) are employed as efficient absorbents for oil removal from water. In this research, the particle size (diameter) obtained from Scanning Electron Microscopy (SEM) images of MNPs, before and after oil-absorption, are utilized to determine the oil-absorption capacity. However, the manual evaluation of the particle size and particle size distribution (PSD) are highly time-consuming and needs expertised people for accurate analysis. Hence, an image processing algorithm is employed for the determination of particle size and PSD from the Scanning Electron Microscopy (SEM) images. The key objective revolves with the preparation of the Maleic Anhydride Grafted Polypropylene anchored Magnetic Nanoparticles (MAPP-a-MNPs) to absorb crude oil from the marine water. The shape, size, and size distribution of MAPP-a-MNPs were assessed by both manual and automated analysis. For this purpose, expertise people help with the manual analysis and Threshold Adaptive-Canny Edge Detection (TA-CED) and Accumulator Updated-Circular Hough Transform (AU-CHT) method is employed for automated analysis. All the automated process were conducted in MATLAB and the measurements were taken for both before and after the oil absorption images. These measurements aid us to determine the quantity of oil absorbed by MAPP-a-MNPs. The results demonstrates excellent oil removal capacity of MAPP-a-MNPs.

Ball Detection Using Deep Learning Implemented on an Educational Robot Based on Raspberry Pi.

RoboCupJunior is a project-oriented competition for primary and secondary school students that promotes robotics, computer science and programing. Through real life scenarios, students are encouraged to engage in robotics in order to help people. One of the popular categories is Rescue Line, in which an autonomous robot has to find and rescue victims. The victim is in the shape of a silver ball that reflects light and is electrically conductive. The robot should find the victim and place it in the evacuation zone. Teams mostly detect victims (balls) using random walk or distant sensors. In this preliminary study, we explored the possibility of using a camera, Hough transform (HT) and deep learning methods for finding and locating balls with the educational mobile robot Fischertechnik with Raspberry Pi (RPi). We trained, tested and validated the performance of different algorithms (convolutional neural networks for object detection and U-NET architecture for sematic segmentation) on a handmade dataset made of images of balls in different light conditions and surroundings. RESNET50 was the most accurate, and MOBILENET_V3_LARGE_320 was the fastest object detection method, while EFFICIENTNET-B0 proved to be the most accurate, and MOBILENET_V2 was the fastest semantic segmentation method on the RPi. HT was by far the fastest method, but produced significantly worse results. These methods were then implemented on a robot and tested in a simplified environment (one silver ball with white surroundings and different light conditions) where HT had the best ratio of speed and accuracy (4.71 s, DICE 0.7989, IoU 0.6651). The results show that microcomputers without GPUs are still too weak for complicated deep learning algorithms in real-time situations, although these algorithms show much higher accuracy in complicated environment situations.

Quantification and Characterization of CTCs and Clusters in Pancreatic Cancer by Means of the Hough Transform Algorithm.

Circulating Tumor Cells (CTCs) are considered a prognostic marker in pancreatic cancer. In this study we present a new approach for counting CTCs and CTC clusters in patients with pancreatic cancer using the IsofluxTM System with the Hough transform algorithm (Hough-IsofluxTM). The Hough-IsofluxTM approach is based on the counting of an array of pixels with a nucleus and cytokeratin expression excluding the CD45 signal. Total CTCs including free and CTC clusters were evaluated in healthy donor samples mixed with pancreatic cancer cells (PCCs) and in samples from patients with pancreatic ductal adenocarcinoma (PDAC). The IsofluxTM System with manual counting was used in a blinded manner by three technicians who used Manual-IsofluxTM as a reference. The accuracy of the Hough-IsofluxTM approach for detecting PCC based on counted events was 91.00% [84.50, 93.50] with a PCC recovery rate of 80.75 ± 16.41%. A high correlation between the Hough-IsofluxTM and Manual-IsofluxTM was observed for both free CTCs and for clusters in experimental PCC (R2 = 0.993 and R2 = 0.902 respectively). However, the correlation rate was better for free CTCs than for clusters in PDAC patient samples (R2 = 0.974 and R2 = 0.790 respectively). In conclusion, the Hough-IsofluxTM approach showed high accuracy for the detection of circulating pancreatic cancer cells. A better correlation rate was observed between Hough-IsofluxTM approach and with the Manual-IsofluxTM for isolated CTCs than for clusters in PDAC patient samples.

Design of Fatigue Driving Behavior Detection Based on Circle Hough Transform.

Chronic fatigue symptoms of jobs are risk factors that may cause errors and lead to occupational accidents. For instance, occupational injuries and traffic accidents stem from overlooking long-term fatigue. According to statistics for fatigue driving, it was found that fatigue driving is one of the main causes of traffic accidents. The resulting decrease in the quality of traffic, as well as impaired traffic flow efficiency and functioning, contributes markedly to the societal costs of fatigue. This article proposes a noninvasive physical method for fatigue detection using a machine vision image algorithm. The main technology was implemented using a software framework based on optimized skin color segmentation and edge detection, as well as eye contour extraction. By integrating machine vision and an optimized Hove transform algorithm, our method mainly identifies fatigue based on the detected target's face, head gestures, mouth aspect ratio (MAR), and eye condition, and then triggers an alarm through an intelligent auxiliary device. Our evaluation results of facial image data analysis showed that with an ideal eye threshold of 0.3, PERCLOS-80 standard, MAR, and head gesture-nod frequency, the method can be used to detect fatigue data accurately and systematically, thereby fulfilling the purpose of alerting a group of high-risk drivers and preventing them from engaging in high-risk activities in an involuntary state.

Low-False-Alarm-Rate Timing and Duration Estimation of Noisy Frequency Agile Signal by Image Homogeneous Detection and Morphological Signature Matching Schemes.

Frequency hopping spread spectrum (FHSS) applies widely to communication and radar systems to ensure communication information and channel signal quality by tuning frequency within a wide frequency range in a random sequence. An efficient signal processing scheme to resolve the timing and duration signature from an FHSS signal provides crucial information for signal detection and radio band management purposes. In this research, hopping time was first identified by a two-dimensional temporal correlation function (TCF). The timing information was shown at TCF phase discontinuities. To enhance and resolve the timing signature of TCF in a noisy environment, three stages of signature enhancement and morphological matching processes were applied: first, computing the TCF of the FHSS signal and enhancing discontinuities via wavelet transform; second, a dual-diagonal edge finding scheme to extract the timing pattern signature and eliminate mismatching distortion morphologically; finally, Hough transform resolved the agile frequency timing from purified line segments. A grand-scale Monte Carlo simulation of the FHSS signals with additive white Gaussian noise was carried out in the research. The results demonstrated reliable hopping time estimation obtained in SNR at 0 dB and above, with a minimal false detection rate of 1.79%, while the prior related research had an unattended false detection rate of up to 35.29% in such a noisy environment.

Comparative study of algorithms for information retrieval of brain puncture targets based on Hough transform circle detection / 军事医学

Objective To compare the principles and performance of three Hough transform algorithms(standard Hough transform,gradient based Hough transform,and random Hough transform)in order to establish a suitable control basis for precise and rapid recognition of targets and acquisition of target center coordinates for craniocerebral puncture robots.Methods A simulation environment in MATLAB software was built to study and analyze image feature recognition,filtering,edge detection,cumulative voting and other processing engineering.Contour recognition and fitting of target circles were achieved in multiple scenarios before their center coordinates were obtained.The recognition and fitting performance of these algorithms was quantitatively compared.Finally,a better detection algorithm based on the actual environment of the craniocerebral puncture robot was determined.Results The standard Hough transform algorithm had the largest error between the mark circle and the target circle,and the running time of this algorithm was the longest due to large computation.The detection speed of the random Hough transform algorithm was lower than that of the gradient-based Hough transform algorithm,but the fitting accuracy was slightly better than that of the standard Hough transform algorithm.The speed and accuracy of circle fitting based on the gradient Hough transform algorithm had significant advantages over the other two.Conclusion The gradient based Hough transform algorithm is more suitable for obtaining the target center coordinates of the craniocerebral puncture robot system.

Cochlear implant electrode array tip-foldover detection by electrode voltage telemetry.

OBJECTIVES: With the introduction of more flexible and thinner electrodes, such as Cochlear's Slim Modiolar Electrode, there is a higher risk of electrode insertion problems, in particular the tip foldover. Timely intraoperative detection of the problem would allow for direct intraoperative correction. This paper describes a non-radiological method for intraoperative tip foldover detection that is applicable in all surgical centers and can quickly deliver accurate results. METHODS: Postoperative radiographs of 118 CI-recipients implanted with Nucleus devices were retrospectively analyzed on the presence of a tip foldover. Electrode Voltage Telemetry (EVT), also called Electric Field Imaging, was performed by means of Cochlear's EVT software tool, which is now integrated into Custom Sound-EP as the Trans-Impedance-Matrix measurement option. Tip foldover detection was automated by using the linear Hough transform for extracting straight-line patterns in the Trans-Impedance Matrix's heatmap. RESULTS: The six cases of electrode tip foldover were accurately identified by the EVT measurements, including two cases with folding location very close to the electrode tip (contact 20). CONCLUSION: Electrode Voltage Telemetry measures the Trans-Impedance Matrix, which can accurately detect tip foldovers of the cochlear implant electrodes within 1 min. This method can be reliably applied in all patients with normal cochlear anatomy and is able to intraoperatively detect foldovers localized even very close to the electrode tip. Application of the linear Hough transform allows for automatic detection of electrode tip foldovers that shows excellent agreement with visual evaluation of the radiological images and the transimpedance matrix's heatmap.

Cascaded Hough Transform-Based Hair Mask Generation and Harmonic Inpainting for Automated Hair Removal from Dermoscopy Images.

Restoring information obstructed by hair is one of the main issues for the accurate analysis and segmentation of skin images. For retrieving pixels obstructed by hair, the proposed system converts dermoscopy images into the L*a*b* color space, then principal component analysis (PCA) is applied to produce grayscale images. Afterward, the contrast-limited adaptive histogram equalization (CLAHE) and the average filter are implemented to enhance the grayscale image. Subsequently, the binary image is generated using the iterative thresholding method. After that, the Hough transform (HT) is applied to each image block to generate the hair mask. Finally, the hair pixels are removed by harmonic inpainting. The performance of the proposed automated hair removal was evaluated by applying the proposed system to the International Skin Imaging Collaboration (ISIC) dermoscopy dataset as well as to clinical images. Six performance evaluation metrics were measured, namely the mean squared error (MSE), the peak signal-to-noise ratio (PSNR), the signal-to-noise ratio (SNR), the structural similarity index (SSIM), the universal quality image index (UQI), and the correlation (C). Using the clinical dataset, the system achieved MSE, PSNR, SNR, SSIM, UQI, and C values of 34.7957, 66.98, 42.39, 0.9813, 0.9801, and 0.9985, respectively. The results demonstrated that the proposed system could satisfy the medical diagnostic requirements and achieve the best performance compared to the state-of-art.

Automated Detection of Corneal Ulcer Using Combination Image Processing and Deep Learning.

A corneal ulcers are one of the most common eye diseases. They come from various infections, such as bacteria, viruses, or parasites. They may lead to ocular morbidity and visual disability. Therefore, early detection can reduce the probability of reaching the visually impaired. One of the most common techniques exploited for corneal ulcer screening is slit-lamp images. This paper proposes two highly accurate automated systems to localize the corneal ulcer region. The designed approaches are image processing techniques with Hough transform and deep learning approaches. The two methods are validated and tested on the publicly available SUSTech-SYSU database. The accuracy is evaluated and compared between both systems. Both systems achieve an accuracy of more than 90%. However, the deep learning approach is more accurate than the traditional image processing techniques. It reaches 98.9% accuracy and Dice similarity 99.3%. However, the first method does not require parameters to optimize an explicit training model. The two approaches can perform well in the medical field. Moreover, the first model has more leverage than the deep learning model because the last one needs a large training dataset to build reliable software in clinics. Both proposed methods help physicians in corneal ulcer level assessment and improve treatment efficiency.

ScatterHough: Automatic Lane Detection from Noisy LiDAR Data.

Lane detection plays an essential role in autonomous driving. Using LiDAR data instead of RGB images makes lane detection a simple straight line, and curve fitting problem works for realtime applications even under poor weather or lighting conditions. Handling scatter distributed noisy data is a crucial step to reduce lane detection error from LiDAR data. Classic Hough Transform (HT) only allows points in a straight line to vote on the corresponding parameters, which is not suitable for data in scatter form. In this paper, a Scatter Hough algorithm is proposed for better lane detection on scatter data. Two additional operations, ρ neighbor voting and ρ neighbor vote-reduction, are introduced to HT to make points in the same curve vote and consider their neighbors' voting result as well. The evaluation of the proposed method shows that this method can adaptively fit both straight lines and curves with high accuracy, compared with benchmark and state-of-the-art methods.