To guide surgical and medical treatment X-ray images have been used by physicians in every modern healthcare organization and hospitals. Doctor's evaluation process and disease identification in the area of skeletal system can be performed in a faster and efficient way with the help of X-ray imaging technique as they can depict bone structure painlessly. This paper presents an efficient contrast enhancement technique using morphological operators which will help to visualize important bone segments and soft tissues more clearly. Top-hat and Bottom-hat transform are utilized to enhance the image where gradient magnitude value is calculated for automatically selecting the structuring element (SE) size. Experimental evaluation on different x-ray imaging databases shows the effectiveness of our method which also produces comparatively better output against some existing image enhancement techniques.
New Jersey Institute of Technology（新泽西理工学院）；University of Nebraska at Omaha（内布拉斯加大学奥马哈分校）
Mathematical morphology is a theory and technique to collect features like geometric and topological structures in digital images. Given a target image, determining suitable morphological operations and structuring elements is a cumbersome and time-consuming task. In this paper, a morphological neural network is proposed to address this problem. Serving as a nonlinear feature extracting layer in deep learning frameworks, the efficiency of the proposed morphological layer is confirmed analytically and empirically. With a known target, a single-filter morphological layer learns the structuring e…
Shaanxi University of Science and Technology（陕西科技大学）；Northwestern Polytechnical University（西北工业大学）；The University of Sydney（悉尼大学）；Shaanxi Normal University（陕西师范大学）；Brunel University London（伦敦布鲁内尔大学）；Tongji University（同济大学）
Morphological reconstruction (MR) is often employed by seeded image segmentation algorithms such as watershed transform and power watershed as it is able to filter seeds (regional minima) to reduce over-segmentation. However, MR might mistakenly filter meaningful seeds that are required for generating accurate segmentation and it is also sensitive to the scale because a single-scale structuring element is employed. In this paper, a novel adaptive morphological reconstruction (AMR) operation is proposed that has three advantages. Firstly, AMR can adaptively filter useless seeds while preserving…
Morphological neurons, that is morphological operators such as dilation and erosion with learnable structuring elements, have intrigued researchers for quite some time because of the power these operators bring to the table despite their simplicity. These operators are known to be powerful nonlinear tools, but for a given problem coming up with a sequence of operations and their structuring element is a non-trivial task. So, the existing works have mainly focused on this part of the problem without delving deep into their applicability as generic operators. A few works have tried to utilize mo…
The shapes and morphology of the organs and tissues are important prior knowledge in medical imaging recognition and segmentation. The morphological operation is a well-known method for morphological feature extraction. As the morphological operation is performed well in hand-crafted image segmentation techniques, it is also promising to design an approach to approximate morphological operation in the convolutional networks. However, using the traditional convolutional neural network as a black-box is usually hard to specify the morphological operation action. Here, we introduced a 3D morpholo…
Indian Statistical Institute（印度统计研究所）；The University of Adelaide（阿德莱德大学）
Mathematical morphological methods have successfully been applied to filter out (emphasize or remove) different structures of an image. However, it is argued that these methods could be suitable for the task only if the type and order of the filter(s) as well as the shape and size of operator kernel are designed properly. Thus the existing filtering operators are problem (instance) specific and are designed by the domain experts. In this work we propose a morphological network that emulates classical morphological filtering consisting of a series of erosion and dilation operators with trainabl…
Laboratoire Hubert Curien（于贝尔·居里安实验室）；Université Jean Monnet（让·莫内大学）
Morphological neural networks allow to learn the weights of a structuring function knowing the desired output image. However, those networks are not intrinsically robust to lighting variations in images with an optical cause, such as a change of light intensity. In this paper, we introduce a morphological neural network which possesses such a robustness to lighting variations. It is based on the recent framework of Logarithmic Mathematical Morphology (LMM), i.e. Mathematical Morphology defined with the Logarithmic Image Processing (LIP) model. This model has a LIP additive law which simulates …
We present a novel framework for learning morphological operators using counter-harmonic mean. It combines concepts from morphology and convolutional neural networks. A thorough experimental validation analyzes basic morphological operators dilation and erosion, opening and closing, as well as the much more complex top-hat transform, for which we report a real-world application from the steel industry. Using online learning and stochastic gradient descent, our system learns both the structuring element and the composition of operators. It scales well to large datasets and online settings.
University of Innsbruck（因斯布鲁克大学）；Besi Austria GmbH（Besi奥地利有限公司）
This paper presents innovative algorithms to efficiently compute erosions and dilations of run-length encoded (RLE) binary images with arbitrary shaped structuring elements. An RLE image is given by a set of runs, where a run is a horizontal concatenation of foreground pixels. The proposed algorithms extract the skeleton of the structuring element and build distance tables of the input image, which are storing the distance to the next background pixel on the left and right hand sides. This information is then used to speed up the calculations of the erosion and dilation operator by enabling th…
International Prevention Research Institute（国际预防研究所）
We introduce a simple expression for the map of Asplund's distances with the multiplicative Logarithmic Image Processing (LIP) law. It is a difference between a morphological dilation and a morphological erosion with an additive structuring function which corresponds to a morphological gradient.
University for Health Sciences, Medical Informatics and Technology (UMIT)（健康科学、医学信息与技术大学（UMIT））
Morphological amoebas are image-adaptive structuring elements for morphological and other local image filters introduced by Lerallut et al. Their construction is based on combining spatial distance with contrast information into an image-dependent metric. Amoeba filters show interesting parallels to image filtering methods based on partial differential equations (PDEs), which can be confirmed by asymptotic equivalence results. In computing amoebas, graph structures are generated that hold information about local image texture. This paper reviews and summarises the work of the author and his co…
University of Sydney（悉尼大学）；Shanghai Jiao Tong University（上海交通大学）；UNSW（新南威尔士大学）
Gigapixel medical images provide massive data, both morphological textures and spatial information, to be mined. Due to the large data scale in histology, deep learning methods play an increasingly significant role as feature extractors. Existing solutions heavily rely on convolutional neural networks (CNNs) for global pixel-level analysis, leaving the underlying local geometric structure such as the interaction between cells in the tumor microenvironment unexplored. The topological structure in medical images, as proven to be closely related to tumor evolution, can be well characterized by gr…
Structure and Progress Aware Diffusion for Medical Image Segmentation
School of Artificial Intelligence（人工智能学院）；School of Computer Science and Technology（计算机科学与技术学院）；State Key Laboratory of Opto-Electronic Information Acquisition and Protection Technology（光电信息采集与防护技术国家重点实验室）；Anhui Provincial Key Laboratory of Security Artificial Intelligence（安徽省安全人工智能重点实验室）；Anhui Provincial Key Laboratory of Multimodal Cognitive Computation（安徽省多模态认知计算重点实验室）
Neural networks and particularly Deep learning have been comparatively little studied from the theoretical point of view. Conversely, Mathematical Morphology is a discipline with solid theoretical foundations. We combine these domains to propose a new type of neural architecture that is theoretically more explainable. We introduce a Binary Morphological Neural Network (BiMoNN) built upon the convolutional neural network. We design it for learning morphological networks with binary inputs and outputs. We demonstrate an equivalence between BiMoNNs and morphological operators that we can use to b…
University for Health Sciences, Medical Informatics and Technology (UMIT)（健康科学、医学信息与技术大学（UMIT））
We study the applicability of a set of texture descriptors introduced in recent work by the author to texture-based segmentation of images. The texture descriptors under investigation result from applying graph indices from quantitative graph theory to graphs encoding the local structure of images. The underlying graphs arise from the computation of morphological amoebas as structuring elements for adaptive morphology, either as weighted or unweighted Dijkstra search trees or as edge-weighted pixel graphs within structuring elements. In the present paper we focus on texture descriptors in whic…
Medical University of Vienna（维也纳医科大学）；Loughborough University（拉夫堡大学）；TissueGnostics GmbH（TissueGnostics有限公司）
Foot ulcer is a common complication of diabetes mellitus and, associated with substantial morbidity and mortality, remains a major risk factor for lower leg amputations. Extracting accurate morphological features from foot wounds is crucial for appropriate treatment. Although visual inspection by a medical professional is the common approach for diagnosis, this is subjective and error-prone, and computer-aided approaches thus provide an interesting alternative. Deep learning-based methods, and in particular convolutional neural networks (CNNs), have shown excellent performance for various task…
Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences（中国科学院宁波材料技术与工程研究所慈溪生物医学工程研究所）；Inception Institute of Artificial Intelligence（起源人工智能研究院）；Edge Hill University（边山大学）；UBTech Research, UBTech Robotics Corp Ltd（优必选机器人有限公司优必选研究院）；University of Liverpool（利物浦大学）；Wuhan University of Science and Technology（武汉科技大学）
Automated detection of curvilinear structures, e.g., blood vessels or nerve fibres, from medical and biomedical images is a crucial early step in automatic image interpretation associated to the management of many diseases. Precise measurement of the morphological changes of these curvilinear organ structures informs clinicians for understanding the mechanism, diagnosis, and treatment of e.g. cardiovascular, kidney, eye, lung, and neurological conditions. In this work, we propose a generic and unified convolution neural network for the segmentation of curvilinear structures and illustrate in s…
Noise-Consistent Siamese-Diffusion for Medical Image Synthesis and Segmentation
National University of Singapore（国立新加坡大学）；National University of Singapore Suzhou Research Institute（国立新加坡大学苏州研究院）；Wenzhou-Kean University（温州-凯恩大学）；Soochow University（苏州大学）；City University of Hong Kong（香港城市大学）
University of Paris-Saclay（巴黎萨克雷大学）；CEA（法国原子能和替代能源委员会）；CNRS（法国国家科学研究中心）；WITSEE
Recent studies have demonstrated the superiority of deep learning in medical image analysis, especially in cell instance segmentation, a fundamental step for many biological studies. However, the excellent performance of the neural networks requires training on large, unbiased dataset and annotations, which is labor-intensive and expertise-demanding. This paper presents an end-to-end framework to automatically detect and segment NeuN stained neuronal cells on histological images using only point annotations. Unlike traditional nuclei segmentation with point annotation, we propose using point a…
Test-Time Domain Generalization via Universe Learning: A Multi-Graph Matching Approach for Medical Image Segmentation
Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging, Anhui University（安徽大学安徽省医学影像先进技术国际联合研究中心）；Hunan University（湖南大学）；The Hong Kong University of Science and Technology（香港科技大学）
Survey on deep learning in multimodal medical imaging for cancer detection
Massey University（梅西大学）；Nantong University（南通大学）；Zhejiang Engineering Research Center of Intelligent Medicine（浙江省智能医学工程研究中心）；Jianpei Technology Co. Ltd（建培科技有限公司）
Image-based characterization and disease understanding involve integrative analysis of morphological, spatial, and topological information across biological scales. The development of graph convolutional networks (GCNs) has created the opportunity to address this information complexity via graph-driven architectures, since GCNs can perform feature aggregation, interaction, and reasoning with remarkable flexibility and efficiency. These GCNs capabilities have spawned a new wave of research in medical imaging analysis with the overarching goal of improving quantitative disease understanding, mon…
Medical Resonance Imaging or MRI is a medical image processing technique that used radio waves to scan the body. It is a tomographic imaging technique, principally used in the field of radiology. With the advantage of being a painless diagnostic procedure, MRI allows medical personnel to illustrate clear pictures of the anatomy and the physiological processes occurring in the body, thus allowing early detection and treatment of diseases. These images, combined with image processing techniques may be used in the detection of tumors, difficult to identify with the naked eye. This digital assignm…
Optimized Vessel Segmentation: A Structure-Agnostic Approach with Small Vessel Enhancement and Morphological Correction
Paul C. Lauterbur Research Center for Biomedical Imaging（保罗·C·劳特伯生物医学成像研究中心）；Shenzhen Institutes of Advanced Technology（深圳先进技术研究院）；Chinese Academy of Sciences（中国科学院）
HER-Seg: Holistically Efficient Segmentation for High-Resolution Medical Images
School of Computer Science, University of Nottingham Ningbo China（诺丁汉大学宁波校区计算机科学学院）；University of Nottingham, UK（诺丁汉大学（英国））；Sichuan University Pittsburgh Institute, Sichuan University, China（四川大学匹兹堡研究院，四川大学，中国）；The Chinese University of Hong Kong, Hong Kong SAR（香港中文大学（香港特别行政区））；School of Computer Science, University of Nottingham, UK（诺丁汉大学（英国）计算机科学学院）；School of Computer Science, University of Lincoln, UK（林肯大学（英国）计算机科学学院）；HKISI, Chinese Academy of Sciences, Hong Kong SAR（香港科学院HKISI，香港特别行政区）
Instead of directly utilizing an observed image including some outliers, noise or intensity inhomogeneity, the use of its ideal value (e.g. noise-free image) has a favorable impact on clustering. Hence, the accurate estimation of the residual (e.g. unknown noise) between the observed image and its ideal value is an important task. To do so, we propose an $\ell_0$ regularization-based Fuzzy $C$-Means (FCM) algorithm incorporating a morphological reconstruction operation and a tight wavelet frame transform. To achieve a sound trade-off between detail preservation and noise suppression, morpholog…
StructDiff: Structure-aware Diffusion Model for 3D Fine-grained Medical Image Synthesis
Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, China（新一代人工智能技术及跨学科应用关键实验室（东南大学），中华人民共和国教育部，中国）；Jiangsu Province Joint International Research Laboratory of Medical Information Processing, Southeast University, Nanjing, China（江苏省医疗信息处理联合国际研究实验室（东南大学），南京市，中国）；Univ Rennes, CHU Rennes, Inserm, LTSI– UMR 1099, F-35000 Rennes, France（里昂大学，里昂大学医院，Inserm，LTSI– UMR 1099，法国里昂，法国）；Shanghai AI Laboratory（上海人工智能实验室）；School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China（计算机科学与技术学院，北京理工大学，北京，中国）；Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China（放射科，南京大学附属医院，南京大学，南京，中国）
the University of Hong Kong（香港大学）；China Agricultural University（中国农业大学）；Deepwise Healthcare（深睿医疗）
During clinical practice, radiologists often use attributes, e.g. morphological and appearance characteristics of a lesion, to aid disease diagnosis. Effectively modeling attributes as well as all relationships involving attributes could boost the generalization ability and verifiability of medical image diagnosis algorithms. In this paper, we introduce a hybrid neuro-probabilistic reasoning algorithm for verifiable attribute-based medical image diagnosis. There are two parallel branches in our hybrid algorithm, a Bayesian network branch performing probabilistic causal relationship reasoning a…
IBM Research – Almaden Research Center（IBM研究院阿尔马登研究中心）
Deep learning has shown promising results in medical image analysis, however, the lack of very large annotated datasets confines its full potential. Although transfer learning with ImageNet pre-trained classification models can alleviate the problem, constrained image sizes and model complexities can lead to unnecessary increase in computational cost and decrease in performance. As many common morphological features are usually shared by different classification tasks of an organ, it is greatly beneficial if we can extract such features to improve classification with limited samples. Therefore…
Although spatial information of images usually enhance the robustness of the Fuzzy C-Means (FCM) algorithm, it greatly increases the computational costs for image segmentation. To achieve a sound trade-off between the segmentation performance and the speed of clustering, we come up with a Kullback-Leibler (KL) divergence-based FCM algorithm by incorporating a tight wavelet frame transform and a morphological reconstruction operation. To enhance FCM's robustness, an observed image is first filtered by using the morphological reconstruction. A tight wavelet frame system is employed to decompose …
Pancreatic cancer is one of the deadliest types of cancer, with 25% of the diagnosed patients surviving for only one year and 6% of them for five. Computed tomography (CT) screening trials have played a key role in improving early detection of pancreatic cancer, which has shown significant improvement in patient survival rates. However, advanced analysis of such images often requires manual segmentation of the pancreas, which is a time-consuming task. Moreover, pancreas presents high variability in shape, while occupying only a very small area of the entire abdominal CT scans, which increases …
Metrics that matter: Evaluating image quality metrics for medical image generation
Department of Computer Science, University of York（约克大学计算机科学系）；School of Computer Science, University of Leeds（利兹大学计算机科学学院）；Department of Computing and Mathematics, Manchester Metropolitan University（曼彻斯特 Metropolitan 大学计算与数学系）；Department of Computer Science, School of Engineering, University of Manchester（曼彻斯特大学计算机科学系）；School of Health Sciences, Division of Informatics, Imaging and Data Sciences, University of Manchester（曼彻斯特大学健康科学学院）；Department of Cardiovascular Sciences, KU Leuven（布鲁塞尔自由大学心血管科学系）；Department of Electrical Engineering (ESAT), KU Leuven（布鲁塞尔自由大学电子工程系）；Improvement Academy, Bradford Institute for Health Research, Bradford Royal Infirmary（改进学院，布拉德福德健康研究中心，布拉德福德皇家医院）
University of Toronto（多伦多大学）；The Hospital for Sick Children（病童医院）；Technion（以色列理工学院）
Self-supervised learning methods for computer vision have demonstrated the effectiveness of pre-training feature representations, resulting in well-generalizing Deep Neural Networks, even if the annotated data are limited. However, representation learning techniques require a significant amount of time for model training, with most of the time spent on precise hyper-parameter optimization and selection of augmentation techniques. We hypothesized that if the annotated dataset has enough morphological diversity to capture the diversity of the general population, as is common in medical imaging d…
Frequency-domain Multi-modal Fusion for Language-guided Medical Image Segmentation
School of Computer Science and Technology, Anhui University（安徽大学计算机科学与技术学院）；NLPR, MAIS, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Information Science and Technology, ShanghaiTech University（上海科技大学信息科学与技术学院）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；School of Artificial Intelligence, Anhui University（安徽大学人工智能学院）
