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Automatic Segmentation algorithm of Ultrasound Heart Image Based on Convolutional Neural Network and Image Saliency

IEEE ACCESS 2020年 8卷 104445-104457页

作者： Liu, Hui Chu, Wen Wang, Hua Zhengzhou Univ Luoyang Cent Hosp Luoyang 471000 Peoples R China

The emergence of 4D heart images makes the data volume of the images multiply. It is more urgent to require an effective and fast segmentation algorithm. Therefore, a heart image can be accurately segmented from a large amount of image data and an area of interest can be extracted The segmentation algorithm is very necessary. Based on the segmentation and recognition of medical images, this paper proposes a neural network and image saliency based on the obvious difference between the heart image and other tissues in the slice, and the high similarity between adjacent slices in the CT image sequence. Fully automatic segmentation algorithm and 3D visual reconstruction is the segmented heart image. Convolutional neural network is a special deep neural network model of artificial intelligence. Its connections between neurons are not fully connected. The weights of connections between certain neurons in the same layer are shared, and the network model is reduced. The complexity reduces the number of weights. The use of visual saliency techniques to achieve cardiac segmentation based on CT images. An image saliency detection algorithm is adopted to introduce the image segmentation algorithm based on the saliency technique. In this paper, considering the PET image as grayscale image with low resolution, an improved Itti model and an improved grabcut image segmentation algorithm are proposed to solve the problem of the original algorithm in grayscale image. At the same time, the operation steps of the user division area are cancelled, and the automatic processing is realized, and the running time of the algorithm is improved while optimizing the image segmentation effect. The convolutional neural network is constructed to realize the positioning function of the heart in the image. The original cardiac CT image is cropped by the positioning result, and some non-target areas are removed. A stacking noise reduction self-coding network is constructed, and the network is manuall

关键词： Convolutional neural network Itti model grabcut algorithm heart segmentation image saliency stacking noise reduction self-coding network

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Effective segmentations in white blood cell images using ε-SVR-based detection method

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NEURAL COMPUTING & APPLICATIONS 2019年第10期31卷 6767-6780页

作者： Cao, Feilong Liu, Yuehua Huang, Zhen Chu, Jianjun Zhao, Jianwei China Jiliang Univ Dept Appl Math Coll Sci Hangzhou 310018 Zhejiang Peoples R China Jiashan Jasdaq Med Device Co Ltd Jiashan 314100 Zhejiang Peoples R China

White blood cell (WBC) image detection plays an important role in automatic morphological systems since it can simplify and facilitate WBC segmentation and classification procedures. However, existing WBC detection methods mainly rely on the location of the nucleus, which is found difficult to achieve accurate detection results. This paper proposes a novel WBC detection algorithm through sliding windows with varying sizes to traverse the image for candidates. Three cues are explored to measure the candidates, and a combined cue is used as a single output to distinguish positives from negatives. The epsilon-support vector regression is employed to determine the detection window from the candidates. In this paper, two applications of the proposed WBC detection approach are carried out, including an adaptive thresholding algorithm based on WBC detection for nucleus segmentation from images and target detection to lessen the users' interaction for automatic cytoplasm segmentation.

关键词： White blood cell (WBC) image Detection Segmentation Support vector regression grabcut algorithm

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Segmentation of White Blood Cells Image Using Adaptive Location and Iteration

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2017年第6期21卷 1644-1655页

作者： Liu, Yuehua Cao, Feilong Zhao, Jianwei Chu, Jianjun China Jiliang Univ Coll Sci Hangzhou 310018 Zhejiang Peoples R China Jiashan Jasdaq Med Device Corp Ltd Jiashan 314100 Peoples R China

Segmentation of white blood cells (WBCs) image is meaningful but challenging due to the complex internal characteristics of the cells and external factors, such as illumination and different microscopic views. This paper addresses two problems of the segmentation: WBC location and subimage segmentation. To locate WBCs, a method that uses multiple windows obtained by scoring multiscale cues to extract a rectangular region is proposed. In this manner, the location window not only covers the whole WBC completely, but also achieves adaptive adjustment. In the subimage segmentation, the subimages preprocessed from the location window with a replace procedure are taken as initialization, and the grabcut algorithm based on dilation is iteratively run to obtain more precise results. The proposed algorithm is extensively evaluated using a CellaVision dataset as well as a more challenging Jiashan dataset. Compared with the existing methods, the proposed algorithm is not only concise, but also can produce high-quality segmentations. The results demonstrate that the proposed algorithm consistently outperforms other location and segmentation methods, yielding higher recall and better precision rates.

关键词： Adaptive adjustment grabcut algorithm image segmentation location white blood cell (WBC)

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Supervised Evaluation of Seed-Based Interactive Image Segmentation algorithms 20

Supervised Evaluation of Seed-Based Interactive Image Segmen...

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20th SYMPOSIUM ON SIGNAL PROCESSING, IMAGES AND COMPUTER VISION (STSIVA)

作者： Andrade, Fernanda Carrera, Enrique V. Univ Fuerzas Armadas ESPE Casilla 171-5-231B Sangolqui Ecuador

ISBN: (纸本)9781467394611

Extensive research has been conducted in an effort to evaluate methods and techniques for image segmentation. However, while most literature has focused on evaluating automatic and semi-automatic algorithms, works evaluating interactive segmentation algorithms are less numerous. Note that interactive segmentation can improve results by adding prior knowledge from users into the process. Although this user guidance improves segmentation results, it also makes difficult to conduct objective evaluations. For this reason, some works only present non-canonical evaluations. In this paper, we present an objective and empirical evaluation of seed-based interactive segmentation algorithms. We first compare popular metrics that are employed in image-segmentation evaluations in order to define which one reflects most accurately the performance of segmentation algorithms. Then, in the aim of presenting reproducible results, we introduce a novel seed-based user input dataset that extends the well-known grabcut dataset. In addition, we evaluate and contrast four state-of-the-art interactive segmentation algorithms. The analysis of the results demonstrates that Jaccard coefficient and Precision-Recall curves provide a good insight into the performance of the evaluated algorithms. Finally, the grabcut algorithm presents the most robust and useful segmentation among all the evaluated algorithms.

关键词： image segmentation interactive systems grabcut algorithm grabcut dataset Jaccard coefficient objective evaluations precision-recall curves seed-based interactive image segmentation algorithms seed-based user input dataset segmentation results semiautomatic algorithms supervised evaluation Accuracy algorithm design and analysis Databases Image color analysis Image segmentation Measurement Semantics image segmentation algorithm design and analysis image colour analysis interactive systems image segmentation algorithm Semantics segmentation Database Segmentation Interactive

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A method for measuring the width of welding seam based on machine vision

A method for measuring the width of welding seam based on ma...

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Applied Optics and Photonics China (AOPC) Conference - Optics Ultra Precision Manufacturing and Testing

作者： Jin Wei Sun Hai-bin Adv Laser Technol Lab Anhui Prov Hefei 230037 Peoples R China Natl Univ Def Technol State Key Lab Pulsed Power Laser Technol Hefei 230037 Peoples R China Key Lab Infrared & Low Temp Plasma Anhui Prov Hefei 230037 Peoples R China Chinese Peoples Liberat Army Unit 96871 Baoji 721012 Peoples R China

ISBN: (纸本)9781510639584

Aiming at the problems of low measurement efficiency and large measurement error when manually measuring the width of the welding seam using the welding seam ruler, a method for measuring the width of the weld seam based on machine vision was proposed in this paper. Firstly, the hardware of the measuring device was designed. The hardware included a measurement platform, an image acquisition and control module, a computer. One motor guide, one stepper motor, an industrial camera and a lighting source were installed on the measurement platform. Secondly, based on Matlab + OpenCV, the measurement software was developed. The software included five functions: controlling the stepper motor and the lighting source, receiving weld seam images, preprocessing the images, segmenting images, correcting images and calculating the width of the weld seam. The image segmentation was implemented by grabcut algorithm, which could extract the weld area very well. The calculation of the weld width was based on the imaging size and resolution of the industrial camera and the pixels positions on the upper and lower edges of the weld area. Finally, a measurement experiment was carried out on the width of the weld. According to the measurement results, the measurement accuracy of the width of the welding seam reached to 0.1 mm and the measurement speed reached to 30.7 mm / s. The results showed that measuring the width of the welding seam based on machine vision could replace manual measurement and be applied to industrial automation.

关键词： machine vision the width of welding seam measurement grabcut algorithm

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Classification of Indian Classical Dance Forms 8th

Classification of Indian Classical Dance Forms

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8th International Conference on Intelligent Human Computer Interaction (IHCI)

作者： Shubhangi Tiwary, Uma Shanker Indian Inst Informat Technol Dept Informat Technol Allahabad Uttar Pradesh India

ISBN: (纸本)9783319525037;9783319525020

The algorithm proposed in this paper aims to achieve pose recognition in Indian classical dance domain. Three different dance forms namely Bharatnatyam, Kathak and Odissi, all together with 15 poses have been considered for pose classification problem. An initial database is created consisting of 100 images and split into training and testing dataset. Hu moments have been chosen as the feature extraction technique to describe the shape context of an image since they are scale, translation and rotation invariant. To extract Hu moments from the image, the foreground and the background of the images must be separated. The resultant images are then converted to binary. Since it is a multiclass classification problem, SVM using one vs one approach as well as one vs all approach has been implemented and the results are contrasted with linear and RBF kernels for both the approaches.

关键词： Pose recognition Hu moments Multiclass SVM grabcut algorithm

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A Fast Segmentation algorithm of PET Images Based on Visual Saliency Model 2

A Fast Segmentation Algorithm of PET Images Based on Visual ...

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2nd International Conference on Intelligent Computing, Communication & Convergence (ICCC)

作者： Liu Lu Yu Xiaoting Ding Bo Harbin Univ Sci & Technol HRBUST Sch Comp & Technol Harbin 150080 Peoples R China

Owing to fogged image quality and similar gray scale of PET, the current main segmentation algorithm cannot give much attention to its effects and efficiency. Therefore, this paper presents a segmentation algorithm on the basis of visual saliency model of PET images. Firstly, manual operation substituted by optimized Itti visual saliency model distinguishes PET images in a fast way. Secondly, one should preprocess the salient images acquired, and then initialize the Gaussian mixture model of foreground and background area. Finally, the PET salient images are segmented by optimized grabcut algorithm, thus obtaining results. Compared with the other two algorithms, experimental results show that the proposed algorithm has some advantages in the simple operation, the efficient algorithm and the accurate results. At the same time, it effectively improves the efficiency of PET image segmentation and ensures the segmentation results. (C) 2016 The Authors. Published by Elsevier B.V.

关键词： PET Image Segmentation Itti Model grabcut algorithm

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Indian Sign Language converter using Convolutional Neural Networks 5

Indian Sign Language converter using Convolutional Neural Ne...

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IEEE 5th International Conference for Convergence in Technology (I2CT)

作者： Intwala, Nishi Banerjee, Arkav Meenakshi Gala, Nikhil SVKMs NMIMS Univ Mukesh Patel Sch Technol Management & Engn Mumbai Campus Mumbai 400056 Maharashtra India

ISBN: (纸本)9781538680759

People with hearing and speech impairments have to face a lot of difficulties while communicating with the general public. Being a minority, the sign language used by them is not known to a majority of people. In this paper, an Indian sign language converter was developed using a Convolutional Neural Network algorithm with the aim to classify the 26 letters of the Indian Sign Language into their equivalent alphabet letters by capturing a real time image of that sign and converting it to its text equivalent. First a database was created in various backgrounds and various image pre-processing techniques were used to make the database ready for feature extraction. After feature extraction, the images were fed into the CNN using the python software. Several real time images were tested to find the accuracy and efficiency. The results showed a 96% accuracy for the testing images and an accuracy of 87.69% for real time images.

关键词： Indian sign language Convolutional Neural Networks Transfer learning grabcut algorithm

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Interactive Vision-based Intelligent System for Active Macfouling and Microfouling Detection on Hull

Interactive Vision-based Intelligent System for Active Macfo...

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OCEANS Conference

作者： Guo, J. Y. Chin, C. S. Clare, A. S. Ma, Maode Newcastle Univ Sch Marine Sci & Technol Newcastle Upon Tyne NE1 7RU Tyne & Wear England Nanyang Technol Univ Sch Elect & Elect Engn Nanyang Ave Singapore 639798 Singapore

ISBN: (纸本)9781467397247

A system to detect macfouling and microfouling on the ship hull is always challenging. The proposed system uses computer vision to analyze types and amount of fouling on the hull for subsequent analysis. The graphical-user-interface in a mobile microprocessor can be accessed and remotely controlled using platforms such as Windows and Android phone. Experiments have been tested on 40 different fouling images. The results show a mean accuracy of the fouling detection of approximately 79.97%, median of around 84.20% and a standard deviation of about 18.49% followed by the underwater experiments that give a static error of approximately 0.35% of the test area.

关键词： macrofouling microfouling image processing hull grabcut algorithm RaspberryPi graphical user interface

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Object Segmentation in Images using EEG Signals 14

Object Segmentation in Images using EEG Signals

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ACM Conference on Multimedia (MM)

作者： Mohedano, Eva Healy, Graham McGuinness, Kevin Giro-i-Nieto, Xavier O'Connor, Noel E. Smeaton, Alan F. Dublin City Univ Insight Ctr Data Analyt Dublin 9 Ireland Univ Politecn Cataluna Image Proc Grp Barcelona Spain

ISBN: (纸本)9781450330633

This paper explores the potential of brain-computer interfaces in segmenting objects from images. Our approach is centered around designing an effective method for displaying the image parts to the users such that they generate measurable brain reactions. When an image region, specifically a block of pixels, is displayed we estimate the probability of the block containing the object of interest using a score based on EEG activity. After several such blocks are displayed, the resulting probability map is binarized and combined with the grabcut algorithm to segment the image into object and background regions. This study shows that BCI and simple EEG analysis are useful in locating object boundaries in images.

关键词： Brain-computer interfaces Electroencephalography rapid serial visual presentation Object segmentation Interactive segmentation grabcut algorithm

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