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检索条件"任意字段=2023 IEEE International Conference on Image Processing and Computer Applications, ICIPCA 2023"

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DD-UNet: Densely Dilated U-Net for Curvilinear Structure Segmentation in Fundus image

DD-UNet: Densely Dilated U-Net for Curvilinear Structure Seg...

引用

ieee international conference on Bioinformatics and Biomedicine (BIBM)

作者： Yindong Zhang Jie Chen Li Wang Miaohong Chen Guoming Zhang Jianqiang Li College of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Shenzhen Eye Hospital China

Retinopathy of Prematurity (ROP) is a retina disorder that affects premature infants with lower weights. If the patient cannot get the treatment in time when the illness reaches the last stage, irreversible vision loss will be caused. Nevertheless, there has been relatively little consideration given to the segmentation of the ridge, the key clinical characteristic of the illness. Additionally, existing research has not adequately addressed several segmentation issues, such as fragmentary topology, class imbalance, and false positives. This paper proposes a Densely Dilated U-Net (DD-UNet) improved from U-Net to tackle these challenges. Furthermore, the post-processing techniques based on the spatial relationship between vessels and ridges, along with the relative pixel counts of ridges and false positive results is integrated to mitigate false positive results in the predicted ridge. To enhance the precision of thin vessel, a sliding window sampling method is introduced for refined training. Compared with the state-of-the-art models in medical image segmentation, DD-UNet performs well in curvilinear structure segmentation of fundus image. For instance, our DD-UNet outperforms the Attention U-Net by 6.26% in terms of sensitivity and exhibits a 1.85% higher dice score in ridge segmentation.

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A Parallel and Asynchronous Blob Shape Descriptor Implemented in CMOS Technology

A Parallel and Asynchronous Blob Shape Descriptor Implemente...

引用

international conference on Microelectronics, MIEL

作者： P. Bogacki M. Długosz T. Talaśka R. Długosz Aptiv Services Poland Kraków Poland Institute of Telecommunications Faculty of Computer Science Electronics and Telecommunications AGH University of Science and Technology Kraków Poland Faculty of Control Robotics and Electrical Engineering Institute of Automation and Robotics Division of Signal Processing and Electronic Systems Poznan University of Technology Poznan Poland Faculty of Telecommunication Computer Science and Electrical Engineering Bydgoszcz University of Science and Technology Bydgoszcz Poland

The paper presents a family of novel light blob shape descriptors for use in selected active safety algorithms used in Advanced Driver Assistance Systems (ADAS). One of the motivations was to obtain a descriptor that would ensure low computational complexity. This makes it easy to implement both in software and hardware. One assumption is that the location of the center of a given light spot is approximately known. The principle of its operation is then to count white pixels in selected directions, starting from this central point. A key issue here is an efficient way of determining indexes of particular pixels belonging to the image patch, as well as the location of points representing places where the white area turns into black. In the case of a hardware implementation, this can be done using a parallel circuit operating in asynchronous mode, without the need for a control clock.

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Brain image Segmentation via GLCM Features and CNN Classification for Improved image Retrieval Using Machine Learning

Brain Image Segmentation via GLCM Features and CNN Classific...

引用

international conference on ICT in Business Industry & Government (ICTBIG)

作者： R Sekar Harikrishnan P R Kalyan Acharjya Department of Electronics and Communication Engineering Presidency University Bangalore Karnataka India Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

Improvement methods based on picture segmentation are commonly employed in medical image analysis. Pixels are commonly put together in groupings of varied sizes to generate an image. The available pixel intensities in an image allow for the separation of distinct image regions. Edge-based and boundary-based segmentation methods are the two primary classifications of this type of analysis. The boundary-based segmentation technique is the more widespread of the two. The image of the tumor needs to be segmented into numerous fragments, each of which can then be analyzed separately. It can annotate data and locate locations of interest in medical image processing. The issues with the existing paper are addressed in the suggested study. Feature extraction using GLCM has been used for classification of tumor images. Analysis of data in segments is routine. The CNN classifier's segments are labelled by an analytical procedure. The proposed method boosts information quality and reduces error-prone training in MRI brain tumor pictures by increasing the learning coefficient of the back propagation layer in the neural network. As a result, the current CNN Shallow Net architecture achieves a higher classification rate of 99.61% thanks to its increased specificity (98.56%) and sensitivity (98.12%). The deep web achieves a segmentation accuracy of 99.58 percent, with an additional diagnostic accuracy of 98.5 percent in identifying cancerous spots.

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Steganography and Pixelation Based Privacy Preserving for image

Steganography and Pixelation Based Privacy Preserving for Im...

引用

computer Science, Electronic Information Engineering and Intelligent Control Technology (CEI), ieee international conference on

作者： Wenxiang Huang School of Computer Science and Technology Dongguan University of Technology Guangdong China

ISBN: (数字)9798350372052

ISBN: (纸本)9798350372069

The rapid progression of machine learning has significantly bolstered the potential applications of data utilization, thereby augmenting the inherent value of data. However, the current landscape lacks effective regulatory strategies for data usage across various scenarios, resulting in substantial risks of unauthorized privacy breaches in collected data. Consequently, there is a crucial need to identify and extract private elements from collected data to facilitate both privacy protection and efficient data utilization. This paper introduces a desensitization process aimed at recognizing private elements in image data through semantic segmentation. Additionally, a steganography implementation is incorporated to formulate a splitting and indexing protection scheme for private data. Experimental validation illustrates that the computational cost of the desensitization process in this scheme is comparable to random computational costs. Importantly, the proposed approach accurately restores private information post-authorization, enabling the segmented storage and indexing of data.

关键词： Data privacy Steganography Semantic segmentation Machine learning Privacy breach Computational efficiency image restoration

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Skin Disease Detection based on image processing Technique

Skin Disease Detection based on Image Processing Technique

引用

international conference on ent Computing and Control Systems (ICICCS)

作者： P Sunil Kumar P Sundaresan R Logith N Mathivanan Department of Computer Science and Engineering Karpagam Academy of Higher Education Coimbatore India

People may now more easily obtain current information thanks to the growth of mobile applications. Users are looking for solutions to problems in the virtual world, especially medical issues. More people have skin conditions than other illnesses. Skin conditions can be brought on by viruses, germs, allergies, fungal infections, and more. A skin condition can alter the skin’s tone or texture. Skin conditions are typically persistent, contagious, and occasionally carcinogenic. This research discusses the image-based skin disease diagnosis application for mobile devices. The collection of photos of unhealthy skin is used by the algorithm to examine the ailment. This technique is intended to identify skin conditions from unwholesome photos. By comparing pre-processed photos, the threshold value difference is found. The differential in the given threshold will be used in decision-making when suspicious unpleasant skin is detected. The app was created using the Android Platform and the OpenCV library to implement the Machine learning algorithm. Android-based mobile applications that can diagnose skin infections have indeed been extensively developed.

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Medical Radiology image processing for Pneumonia Detection Using Convolutional Neural Network

Medical Radiology Image processing for Pneumonia Detection U...

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ieee Annual Computing and Communication Workshop and conference (CCWC)

作者： Vinay Singh Aditya Manay Raj Prakash Singh Shaurya Tomer Chandan A Narayana Darapaneni Vishnu Manoj Aashi Rastogi Anwesh Reddy Paduri CSAI Great Learning Mumbai India AIML Great Learning/Northwestern University Illinois USA Great Learning Hyderabad India

With the advent of the digital age, humans have created many ways of assistance in menial and time-consuming tasks. Machine learning and Neural Networks provided a way for us to convert large, assorted data into meaningful patterns and predictions. One of the most imperative applications of this technology is Medical image processing for disease classification and segmentation. In this paper, we attempt to identify Pneumonia in Chest X-Rays, an infection that causes inflammation in the lungs. Bacteria such as Streptococcus pneumonia, among other viruses and fungi cause pneumonia. Due to this reason, the fluids may get filled in the air sacs which causes cough, fever and difficulty in breathing. Convolutional Neural Networks (CNNs) provide an efficient way to detect the disease quickly and have garnered a lot of interest since early diagnosis can prevent the disease from being fatal. Pneumonia claims millions of lives each year and computer-aided classification can help radiologists reduce redundancies and act faster. Our research is focused on using pre-trained CNN models on large datasets as feature extractors such as VGG- 16, InceptionV3, and CheXNet, and then classifying the data into Lung Opacity/Normal, after which we compare and select the best model.

关键词： Training image segmentation Pulmonary diseases Computational modeling Lung Predictive models Feature extraction

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LDE-UNet: A Novel Model for Rapid COVID-19 Diagnosis via CT image Segmentation

LDE-UNet: A Novel Model for Rapid COVID-19 Diagnosis via CT ...

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ieee international conference on Bioinformatics and Biomedicine (BIBM)

作者： QiSong Wang JiaRou Wu YingZhuo Wang HuiLi Qu Shanling Nie Hai Yang Department of Computer Science and Engineering East China University of Science and Technology Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China Faculty of Engineering The University of Sydney Australia

The COVID-19 pandemic has had a profound impact on human society. It has highlighted the need for faster diagnostic methods. Research has shown that combining semantic segmentation with traditional medical approaches can significantly accelerate the process. To address this, leveraging COVID-19 CT images, our team designs a revolutionary semantic segmentation model called Level of Detail Enhancement U-Net (LDE-UNet), which shows the lesion area on CT images. By introducing the LDE block, the model has the unique advantage of overcoming the loss of data details during the downsampling process by emphasizing and transmitting details at the same level. Our SOTA model outperforms the second-best model by at least 0.7% in the most critical indicator precision. Compared with other models, LDE-UNet’s strong reliability determines its ability to be used in the medical field to accelerate the localization and division of lesion areas on CT images by professional doctors, thus completing patient diagnosis faster. In addition, we also propose a standardized method for processing medical images.

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Fan-Net: Fourier-Based Adaptive Normalization for Cross-Domain Stroke Lesion Segmentation

Fan-Net: Fourier-Based Adaptive Normalization for Cross-Doma...

引用

international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Weiyi Yu Yiming Lei Hongming Shan Institute of Science and Technology for Brain-Inspired Intelligence Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai China Shanghai Center for Brain Science and Brain-Inspired Technology Shanghai China

Since stroke is the main cause of various cerebrovascular diseases, deep learning-based stroke lesion segmentation on magnetic resonance (MR) images has attracted considerable attention. However, the existing methods often neglect the domain shift among MR images collected from different sites, which has limited performance improvement. To address this problem, we intend to change style information without affecting high-level semantics via adaptively changing the low-frequency amplitude components of the Fourier transform so as to enhance model robustness to varying domains. Thus, we propose a novel FAN-Net, a U-Net–based segmentation network incorporated with a Fourier-based adaptive normalization (FAN) and a domain classifier with a gradient reversal layer. The FAN module is tailored for learning adaptive affine parameters for the amplitude components of different domains, which can dynamically normalize the style information of source images. Then, the domain classifier provides domain-agnostic knowledge to endow FAN with strong domain generalizability. The experimental results on the ATLAS dataset, which consists of MR images from 9 sites, show the superior performance of the proposed FAN-Net compared with baseline methods.

关键词： image segmentation Fans Semantics Neural networks Magnetic resonance Stroke (medical condition) Signal processing

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Multi-Object Localization and Irrelevant-Semantic Separation for Nuclei Segmentation in Histopathology images

Multi-Object Localization and Irrelevant-Semantic Separation...

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international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Ya Tang Xiongjun Ye Xuanya Li Zhineng Chen Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan China National Cancer Center Cancer Hospital Chinese Academy of Medical Sciences and Peking Union Medical College Beijing China Baidu Inc. Beijing China School of Computer Science Fudan University Shanghai China

Automated segmentation of nuclei in histopathology images is critical for cancer diagnosis and prognosis. Due to the high variability of nuclei morphology, numerous nuclei overlapping, and the wide existence of nuclei clusters, this task still remains challenging. In this paper, we propose an effective nuclei segmentation method for histopathology images based on a novel neural network for multi-object localization and irrelevant-semantic separation (MI-Net), which includes a multi-object localization module (MOLM), a deep boundary awareness module (DBAM), and an irrelevant semantic separation module (ISSM). Specifically, the MOLM is used to calibrate features to capture more comprehensive information about the location of nuclei. It alleviates the problem of cell adhesion in histopathology images. The DBAM is intended to extract boundary information and solve the blurred boundary problem effectively. The ISSM is used to separate foreground features from background features and effectively address the problem of complex backgrounds in histopathology images. It also solves the low contrast problem between the target objects and the background. We evaluate MI-Net on the famous MoNuSeg dataset and compare it with ten state-of-the-art methods. MI-Net surpasses the best-performed method by margins from 1.12% to 2.29% over standard evaluation metrics, showing its effectiveness on nuclei segmentation.

关键词： Location awareness image segmentation Histopathology Semantics Neural networks Signal processing Task analysis

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A Semantics-Aware Normalizing Flow Model for Anomaly Detection

A Semantics-Aware Normalizing Flow Model for Anomaly Detecti...

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ieee international conference on Multimedia and Expo (ICME)

作者： Wei Ma Shiyong Lan Weikang Huang Wenwu Wang Hongyu Yang Yitong Ma Yongjie Ma College of Computer Science Sichuan University China National Key Laboratory of Fundamental Science on Synthetic Vision China Center for Vision Speech and Signal Processing University of Surrey UK

Anomaly detection in computer vision aims to detect outliers from input image data. Examples include texture defect detection and semantic discrepancy detection. However, existing methods are limited in detecting both types of anomalies, especially for the latter. In this work, we propose a novel semantics-aware normalizing flow model to address the above challenges. First, we employ the semantic features extracted from a backbone network as the initial input of the normalizing flow model, which learns the mapping from the normal data to a normal distribution according to semantic attributes, thus enhances the discrimination of semantic anomaly detection. Second, we design a new feature fusion module in the normalizing flow model to integrate texture features and semantic features, which can substantially improve the fitting of the distribution function with input data, thus achieving improved performance for the detection of both types of anomalies. Extensive experiments on five well-known datasets for semantic anomaly detection show that the proposed method outperforms the state-of-the-art baselines. The codes will be available at https://***/SYLan2019/SANF-AD.

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