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Active contours for overlapping cervical cell segmentation

INTERNATIONAL JOURNAL OF BIOMEDICAL ENGINEERING AND TECHNOLOGY 2021年第1期35卷 70-92页

作者： Araujo, Flavio H. D. Silva, Romuere R., V Medeiros, Fatima N. S. Rocha Neto, Jeova Farias Calaes Oliveira, Paulo Henrique Campos Bianchi, Andrea G. Ushizima, Daniela Univ Fed Ceara Dept Teleinformat Engn Fortaleza Ceara Brazil Univ Fed Piaui Informat Syst Picos Piaui Brazil Univ Calif Berkeley Berkeley Inst Data Sci Berkeley CA 94720 USA Lawrence Berkeley Natl Lab Computat Res Div Berkeley CA 94720 USA Brown Univ Brown Sch Engn Providence RI 02912 USA Univ Fed Ouro Preto Comp Dept Ouro Preto MG Brazil

The nuclei and cytoplasm segmentation of cervical cells is a well studied problem. However, the current segmentation algorithms are not robust to clinical practice due to the high computational cost or because they cannot accurately segment cells with high overlapping. In this paper, we propose a method that is capable of segmenting both cytoplasm and nucleus of each individual cell in a clump of overlapping cells. The proposed method consists of three steps: 1) cellular mass segmentation;2) nucleus segmentation;3) cytoplasm identification based on an active contour method. We carried out experiments on both synthetic and real cell images. The performance evaluation of the proposed method showed that it was less sensitive to the increase in the number of cells per image and the overlapping ratio against two other existing algorithms. It has also achieved a promising low processing time and, hence, it has the potential to support expert systems for cervical cell recognition.

关键词： ABSnake active contour cervical cells medical image nuclei and cytoplasm segmentation overlapping cells pap test

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Hair Segmentation and Removal in Dermoscopic images Using Deep Learning

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IEEE ACCESS 2021年 9卷 2694-2704页

作者： Talavera-Martinez, Lidia Bibiloni, Pedro Gonzalez-Hidalgo, Manuel Univ Balearic Isl Dept Math & Comp Sci Soft Comp Image Proc & Aggregat SCOPIA E-07122 Palma De Mallorca Spain

Melanoma and non-melanoma skin cancers have shown a rapidly increasing incidence rate, pointing to skin cancer as a major problem for public health. When analyzing these lesions in dermoscopic images, the hairs and their shadows on the skin may occlude relevant information about the lesion at the time of diagnosis, reducing the ability of automated classification and diagnosis systems. In this work, we present a new approach for the task of hair removal on dermoscopic images based on deep learning techniques. Our proposed model relies on an encoder-decoder architecture, with convolutional neural networks, for the detection and posterior restoration of hair's pixels from the images. Moreover, we introduce a new combined loss function in the network's training phase that combines the L-1 distance, the total variation loss, and a loss function based on the structural similarity index metric. Currently, there are no datasets that contain the same images with and without hair, which is necessary to quantitatively evaluate our model. Thus, we simulate the presence of hair in hairless images extracted from publicly known datasets. We compare our results with six state-of-the-art algorithms based on traditional computer vision techniques by means of similarity measures that compare the reference hairless image and the one with simulated hair. Finally, the Wilcoxon signed-rank test is used to compare the methods. The results, both qualitatively and quantitatively, demonstrate the effectiveness of our model and how our loss function improves the restoration ability of the proposed model.

关键词： Deep neural networks dermoscopy hair removal image processing inpainting skin lesion

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A survey on nonlinear second-order diffusion-based techniques for additive denoising 8th

A survey on nonlinear second-order diffusion-based technique...

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8th International Workshop on Soft Computing Applications, SOFA 2018

作者： Barbu, Tudor Institute of Computer Science of the Romanian Academy Iași Romania

ISBN: (纸本)9783030519919

An overview of additive noise reduction algorithms using nonlinear second-order partial differential equations (PDEs) is provided here. First, the state-of-the-art anisotropic diffusion models that are used for image restoration are described here. The second-order PDE-based denoising approaches using variational schemes are addressed next. Our most important contributions in these image processing fields are also discussed in this work and method comparison examples are provided, too. © Springer Nature Switzerland AG 2021.

关键词： image reconstruction

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An efficient and high quality medical CT image enhancement algorithm

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INTERNATIONAL JOURNAL OF IMAGING systems AND TECHNOLOGY 2020年第4期30卷 939-949页

作者： Li, Zhi Jia, Zhenhong Yang, Jie Kasabov, Nikola Xinjiang Univ Coll Informat Sci & Engn Urumqi 830046 Peoples R China Shanghai Jiao Tong Univ Inst Image Proc & Pattern Recognit Shanghai Peoples R China Auckland Univ Technol Knowledge Engn & Discovery Res Inst Auckland New Zealand

Aiming at the process of medical diagnosis, many problems such as unclear images and low contrast are often caused by noise and interference in the process of medical image acquisition and transmission. This article proposes a new image enhancement method that combines the wavelet domain with the spatial domain. First, we input two identical images (Both of the identical images are original images.) in which the first image is enhanced by histogram equalization. Then, the two images are divided into four sub-images by a two-dimensional wavelet transform. The average of the low-frequency coefficients of the low-frequency sub-images of the two images is taken as the low-frequency coefficients of the final reconstruction. Second, aiming at the problem that the contrast may be too low, the fourth high-frequency sub-image is blurred (sharpened) twice. The fourth high-frequency sub-image after blurring is denoised by median filtering. Finally, the four sub-images are fused to obtain the enhanced image. The experimental results show that the peak signal-to-noise ratio, structural similarity, and processing time of the proposed algorithm are better than those of other contrast algorithms, especially the processing time. These objective indicators show that the proposed algorithm can not only effectively suppress noise but also significantly enhance the contrast. Subjectively, compared with other algorithms, the proposed algorithm achieves a better visual effect and greatly reduces the processing time.

关键词： histogram equalization medical image wavelet transform

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Learned rotationally symmetric diffractive achromat for full-spectrum computational imaging

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OPTICA 2020年第8期7卷 913-922页

作者： Dun, Xiong Ikoma, Hayato Wetzstein, Gordon Wang, Zhanshan Cheng, Xinbin Peng, Yifan Tongji Univ Sch Phys Sci & Engn Inst Precis Opt Engn Shanghai 200092 Peoples R China Stanford Univ Dept Elect Engn Stanford CA 94305 USA MOE Key Lab Adv Microstruct Mat Shanghai 200092 Peoples R China

Diffractive achromats (DAs) promise ultra-thin and light-weight form factors for full-color computational imaging systems. However, designing DAs with the optimal optical transfer function (OTF) distribution suitable for image reconstruction algorithms has been a difficult challenge. Emerging end-to-end optimization paradigms of diffractive optics and processing algorithms have achieved impressive results, but these approaches require immense computational resources and solve non-convex inverse problems with millions of parameters. Here, we propose a learned rotational symmetric DA design using a concentric ring decomposition that reduces the computational complexity and memory requirements by one order of magnitude compared with conventional end-to-end optimization procedures, which simplifies the optimization significantly. With this approach, we realize the joint learning of a DA with an aperture size of 8 mm and an image recovery neural network, i.e., Res-Unet, in an end-to-end manner across the full visible spectrum (429-699 nm). The peak signal-to-noise ratio of the recovered images of our learned DA is 1.3 dB higher than that of DAs designed by conventional sequential approaches. This is because the learned DA exhibits higher amplitudes of the OTF at high frequencies over the full spectrum. We fabricate the learned DA using imprinting lithography. Experiments show that it resolves both fine details and color fidelity of diverse real-world scenes under natural illumination. The proposed design paradigm paves the way for incorporating DAs for thinner, lighter, and more compact full-spectrum imaging systems. (c) 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

关键词： image processing image quality image reconstruction Imaging systems Inverse problems Reconstruction algorithms

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Watershed-Based Superpixels With Global and Local Boundary Marching

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IEEE TRANSACTIONS ON image processing 2020年 29卷 7375-7388页

作者： Yuan, Ye Zhu, Zhiliang Yu, Hai Zhang, Wei Northeastern Univ Software Coll Shenyang 110819 Peoples R China

Superpixels are widely used in computer vision applications, as they conserve the running costs of subsequent processing while preserving the original performance. In most of the existing algorithms, the boundary adherence and the compactness of superpixels are necessarily inter-inhibitive because the color/gradient information is balanced against the position constraints, and the set criteria define all pixels indiscriminately. In this paper, we present a two-phase superpixel segmentation method based on the watershed transformation. After designing a new approach for calculating the flooding priority, we propose a new strategy with two distinct criteria for global and local refinement of the boundary pixels. These criteria reduce the compromise between the boundary adherence and compactness. Unlike the indiscriminate standards, our method applies different treatments to pixels in different environments, preserving the color homogeneity in content-rich areas while improving the regularity of the superpixels in content-plain regions. The superior accuracy and computing time of our proposed method are verified in comparison experiments with several state-of-the-art methods.

关键词： image color analysis image segmentation Optimization Real-time systems Education Clustering algorithms Merging image segmentation superpixel watershed-based real-time boundary marching

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Fast Ship Detection in Remote Sensing images Based on Multi-Attention Mechanism 5

Fast Ship Detection in Remote Sensing Images Based on Multi-...

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5th International Conference on algorithms, Computing and systems (ICACS)

作者： Huang, Jian Chen, Zhuo Xu, Hao Zhang, Xiushan Naval Univ Engn Sch Elect Engn Wuhan Hubei Peoples R China

ISBN: (纸本)9781450385084

With the continuous development of deep learning in computer vision, object detection technology is constantly employed for processing remote sensing images. Especially, ship detection has become a significant and challenging task due to complex environmental factors (strong waves, clouds interference, etc.) and object issues (orientation, scale variety, density, etc.). Current detection methods pay more attention to the detection accuracy while ignoring the detection speed. In contrast with accuracy, detection speed is more important in some cases such as marine rescue and vessel tracking. Aiming at addressing these problems, we propose an enhanced YOLOv4(C-YOLOv4) which contains the feature fusion attention module (FAM) with a channel correlation loss(C-loss). C-loss is proposed to constrain the relations between object classes and channels while maintaining the intra-class and the inter-class separability. To evaluate the effectiveness of the proposed approach, comprehensive experiments are conducted on a public dataset HRSC2016. According to the experimental results, our proposed approach outperforms the baselines.

关键词： Object detection Remote sensing image Convolutional neural networks Ship detection

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An Optimizing Multilevel Thresholding for image Segmentation Based on Hybrid Swarm Computation Optimization 16th

An Optimizing Multilevel Thresholding for Image Segmentation...

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16th International Conference on Intelligent Information Hiding and Multimedia Signal processing, IIH-MSP 2020 in conjunction with the 13th International Conference on Frontiers of Information Technology, Applications and Tools, FITAT 2020

作者： Dao, Thi-Kien Wang, Hong-Jiang Yu, Jie Nguyen, Huu-Quynh Ngo, Truong-Giang Nguyen, Trong-The Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China College of Mechanical and Automotive Engineering Fujian University of Technology Fuzhou350118 China Faculty of Computer Science and Engineering Thuyloi University 175 Tay Son Dong Da Hanoi Viet Nam Department of Information Technology Haiphong University of Management and Technology Haiphong Viet Nam

ISBN: (纸本)9789813367562

This paper suggests a solution for the image segmentation (IS) problem with the multilevel thresholding based on one of the latest hybrid swarm computation optimization algorithms, particle swarms, and gravitational search (PSGA). The experimental results are comparable with other state-of-the-art algorithms that show that the PSGA on selected images is better than the competitors. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

关键词： image segmentation

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A class of nonlinear parabolic systems having standard growth and L¹ data

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NONLINEAR ANALYSIS-REAL WORLD APPLICATIONS 2022年第0期67卷

作者： Atlas, Abdelghafour Karami, Fahd Meskine, Driss Ouakrim, Youssef Oubbih, Omar Univ Cadi Ayyad Ecole Natl Sci Appl Marrakech BP 575 Ave Abdelkrim Khattabi Marrakech Morocco Univ Cadi Ayyad Ecole Superieurede Technol Essaouira Lab Math Informat Modelisat Syst Complexes BP 383 Essaouira Jadida Essaouira Morocco Univ Sidi Mohamed Ben Abdellah Ecole Normale Super Fes Lab Math Modelisat Phys Appliquee Fez city Morocco

In this paper, we are interested in the mathematical and numerical study of a reaction-diffusion system arising in the PDE-constrained optimization problems and image processing. The proposed model extends the recent work (Afraites et al., 2016) by taking L1 data. We prove the existence and uniqueness of entropy solutions on the Orlicz-Sobolev space. Then, we present numerical experiments to illustrate the performance of the L1 data choice and we discuss its efficiency compared to other image denoising algorithms. (c) 2022 Elsevier Ltd. All rights reserved.

关键词： image denoising Staircase effect Nonlinear inverse problems Nonlinear diffusion Entropy solution Orlicz-Sobolev space

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Watermark Detection and Clearance in Video Using Simple Signal and image processing Techniques 27

Watermark Detection and Clearance in Video Using Simple Sign...

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27th International Conference on systems, Signals and image processing (IWSSIP)

作者： Gonzalez Montero, Jose Ramon Diaz, Alberto Reyes Rosillo, Egberto Caballero Conci, Aura Fed Fluminense Univ Comp Inst BR-24210310 Niteroi RJ Brazil

ISBN: (纸本)9781728175393

A digital watermark is an information or signal inserted into a digital content (image, audio, video, document, software) that can be used to determine its ownership, showing who created or sold it, or to ensure its integrity. Typically, the same watermark is added consistently to mark movie collections. Consequently, media with a visible watermark will be eventually susceptible to the necessity of watermark remission, operation that could be an issue. This paper presents two new and very fast algorithms for watermark detection and removal. The here considered watermarks could be blended, overlaid or added in a continuous form to the digital video on remission treatment. The first of such algorithms use a template matching approach to pre-process the blended watermarks frame-to-frame in the Hue-Saturation-Value (HSV) color space, and then improve the detection using a post-processing step. The second algorithm presents two steps as well. Initially, it estimates the structure of a translucent watermark observing the constancy of its gradient magnitude in the area corresponding to the tenth part of each video. Secondly, it uses a sliding window to detect the watermarked in the frames. The results show that watermark detection by both algorithms has high sensitivity. Moreover, they are very fast when compared with other methods especially those based on deep learning.

关键词： watermark steganography digital signal image processing gradient sliding windows

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