scholarly journals THE LAYERED NET SURFACE PROBLEMS IN DISCRETE GEOMETRY AND MEDICAL IMAGE SEGMENTATION

2007 ◽  
Vol 17 (03) ◽  
pp. 261-296 ◽  
Author(s):  
XIAODONG WU ◽  
DANNY Z. CHEN ◽  
KANG LI ◽  
MILAN SONKA
Keyword(s):  
Image Segmentation ◽  
Polynomial Time ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Simultaneous Detection ◽  
Exact Algorithms ◽  
Medical Image Segmentation ◽  
Efficient Detection ◽  
Column Graph ◽  
Voxel Grid

Efficient detection of multiple inter-related surfaces representing the boundaries of objects of interest in d-D images (d ≥ 3) is important and remains challenging in many medical image analysis applications. In this paper, we study several layered net surface (LNS) problems captured by an interesting type of geometric graphs called ordered multi-column graphs in the d-D discrete space (d ≥ 3 is any constant integer). The LNS problems model the simultaneous detection of multiple mutually related surfaces in three or higher dimensional medical images. Although we prove that the d-D LNS problem (d ≥ 3) on a general ordered multi-column graph is NP-hard, the (special) ordered multi-column graphs that model medical image segmentation have the self-closure structures and thus admit polynomial time exact algorithms for solving the LNS problems. Our techniques also solve the related net surface volume (NSV) problems of computing well-shaped geometric regions of an optimal total volume in a d-D weighted voxel grid. The NSV problems find applications in medical image segmentation and data mining. Our techniques yield the first polynomial time exact algorithms for several high dimensional medical image segmentation problems. Experiments and comparisons based on real medical data showed that our LNS algorithms and software are computationally efficient and produce highly accurate and consistent segmentation results.

scholarly journals Match Feature U-Net: Dynamic Receptive Field Networks for Biomedical Image Segmentation

Symmetry ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 1230
Author(s):  
Xiaofei Qin ◽  
Chengzi Wu ◽  
Hang Chang ◽  
Hao Lu ◽  
Xuedian Zhang
Keyword(s):  
Image Segmentation ◽  
Receptive Field ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Medical Image Segmentation ◽  
Training Data ◽  
Cell Segmentation ◽  
Biomedical Image ◽  
Nuclei Segmentation ◽  
Kernel Convolution

Medical image segmentation is a fundamental task in medical image analysis. Dynamic receptive field is very helpful for accurate medical image segmentation, which needs to be further studied and utilized. In this paper, we propose Match Feature U-Net, a novel, symmetric encoder– decoder architecture with dynamic receptive field for medical image segmentation. We modify the Selective Kernel convolution (a module proposed in Selective Kernel Networks) by inserting a newly proposed Match operation, which makes similar features in different convolution branches have corresponding positions, and then we replace the U-Net’s convolution with the redesigned Selective Kernel convolution. This network is a combination of U-Net and improved Selective Kernel convolution. It inherits the advantages of simple structure and low parameter complexity of U-Net, and enhances the efficiency of dynamic receptive field in Selective Kernel convolution, making it an ideal model for medical image segmentation tasks which often have small training data and large changes in targets size. Compared with state-of-the-art segmentation methods, the number of parameters in Match Feature U-Net (2.65 M) is 34% of U-Net (7.76 M), 29% of UNet++ (9.04 M), and 9.1% of CE-Net (29 M). We evaluated the proposed architecture in four medical image segmentation tasks: nuclei segmentation in microscopy images, breast cancer cell segmentation, gland segmentation in colon histology images, and disc/cup segmentation. Our experimental results show that Match Feature U-Net achieves an average Mean Intersection over Union (MIoU) gain of 1.8, 1.45, and 2.82 points over U-Net, UNet++, and CE-Net, respectively.

EFFICIENT ALGORITHMS FOR THE OPTIMAL-RATIO REGION DETECTION PROBLEMS IN DISCRETE GEOMETRY WITH APPLICATIONS

2009 ◽  
Vol 19 (02) ◽  
pp. 141-159 ◽  
Author(s):  
XIAODONG WU
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Graph Transformation ◽  
Optimal Solution ◽  
Maximum Flow ◽  
Small Region ◽  
Medical Image Segmentation ◽  
Optimal Ratio ◽  
Region Detection ◽  
Voxel Grid

In this paper, we study several interesting optimal-ratio region detection (ORD) problems in d- D (d ≥ 3) discrete geometric spaces, which arise in high dimensional medical image segmentation. Given a d- D voxel grid of n cells, two classes of geometric regions that are enclosed by a single or two coupled smooth heighfield surfaces defined on the entire grid domain are considered. The objective functions are normalized by a function of the desired regions, which avoids a bias to produce an overly large or small region resulting from data noise. The normalization functions that we employ are used in real medical image segmentation. To our best knowledge, no previous results on these problems in high dimensions are known. We develop a unified algorithmic framework based on a careful characterization of the intrinsic geometric structures and a nontrivial graph transformation scheme, yielding efficient polynomial time algorithms for solving these ORD problems. Our main ideas include the following. We observe that the optimal solution to the ORD problems can be obtained via the construction of a convex hull for a set of O(n) unknown 2-D points using the hand probing technique. The probing oracles are implemented by computing a minimum s-t cut in a weighted directed graph. The ORD problems are then solved by O(n) calls to the minimum s-t cut algorithm. For the class of regions bounded by a single heighfield surface, our further investigation shows that the O(n) calls to the minimum s-t cut algorithm are on a monotone parametric flow network, which enables to detect the optimal-ratio region in the complexity of computing a single maximum flow.

scholarly journals Digital Medical Image Segmentation Using Fuzzy C-Means Clustering

2020 ◽  
Vol 4 (1) ◽  
pp. 51 ◽  
Author(s):  
Bakhtyar Ahmed Mohammed ◽  
Muzhir Shaban Al-Ani
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Brain Mri ◽  
Accurate Method ◽  
Medical Image Segmentation ◽  
Fuzzy C Means ◽  
Fcm Clustering ◽  
The Brain ◽  
Digital Medical Image

In the modern globe, digital medical image processing is a major branch to study in the fields of medical and information technology. Every medical field relies on digital medical imaging in diagnosis for most of their cases. One of the major components of medical image analysis is medical image segmentation. Medical image segmentation participates in the diagnosis process, and it aids the processes of other medical image components to increase the accuracy. In unsupervised methods, fuzzy c-means (FCM) clustering is the most accurate method for image segmentation, and it can be smooth and bear desirable outcomes. The intention of this study is to establish a strong systematic way to segment complicate medical image cases depend on the proposed method to share in the decision-making process. This study mentions medical image modalities and illustrates the steps of the FCM clustering method mathematically with example. It segments magnetic resonance imaging (MRI) of the brain to separate tumor inside the brain MRI according to four statuses.

scholarly journals CMM-Net: Contextual Multi-Scale Multi-Level Network for Efficient Biomedical Image Segmentation

2021 ◽  
Author(s):  
Mohammed Al-masni ◽  
Dong-Hyun Kim
Keyword(s):  
Image Segmentation ◽  
Blood Vessels ◽  
Medical Image ◽  
Spatial Scales ◽  
Medical Image Analysis ◽  
Skin Lesions ◽  
Medical Image Segmentation ◽  
Multi Scale ◽  
Retinal Blood Vessels ◽  
Multi Level

Abstract Medical image segmentation of tissue abnormalities, key organs, or blood vascular system is of great significance for any computerized diagnostic system. However, automatic segmentation in medical image analysis is a challenging task since it requires sophisticated knowledge of the target organ anatomy. This paper develops an end-to-end deep learning segmentation method called Contextual Multi-Scale Multi-Level Network (CMM-Net). The main idea is to fuse the global contextual features of multiple spatial scales at every contracting convolutional network level in the U-Net. Also, we re-exploit the dilated convolution module that enables an expansion of the receptive field with different rates depending on the size of feature maps throughout the networks. In addition, an augmented testing scheme referred to as Inversion Recovery (IR) which uses logical “OR” and “AND” operators is developed. The proposed segmentation network is evaluated on three medical imaging datasets, namely ISIC 2017 for skin lesions segmentation from dermoscopy images, DRIVE for retinal blood vessels segmentation from fundus images, and BraTS 2018 for brain gliomas segmentation from MR scans. The experimental results showed superior state-of-the-art performance with overall dice similarity coefficients of 85.78%, 80.27%, and 88.96% on the segmentation of skin lesions, retinal blood vessels, and brain tumors, respectively. The proposed CMM-Net is inherently general and could be efficiently applied as a robust tool for various medical image segmentations.

scholarly journals Optic Disc Segmentation Using Attention-Based U-Net and the Improved Cross-Entropy Convolutional Neural Network

Entropy ◽  
2020 ◽  
Vol 22 (8) ◽  
pp. 844
Author(s):  
Baixin Jin ◽  
Pingping Liu ◽  
Peng Wang ◽  
Lida Shi ◽  
Jing Zhao
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Deep Learning ◽  
Optic Disc ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Medical Image Segmentation ◽  
Attention Mechanism ◽  
Cross Entropy ◽  
Segmentation Task

Medical image segmentation is an important part of medical image analysis. With the rapid development of convolutional neural networks in image processing, deep learning methods have achieved great success in the field of medical image processing. Deep learning is also used in the field of auxiliary diagnosis of glaucoma, and the effective segmentation of the optic disc area plays an important assistant role in the diagnosis of doctors in the clinical diagnosis of glaucoma. Previously, many U-Net-based optic disc segmentation methods have been proposed. However, the channel dependence of different levels of features is ignored. The performance of fundus image segmentation in small areas is not satisfactory. In this paper, we propose a new aggregation channel attention network to make full use of the influence of context information on semantic segmentation. Different from the existing attention mechanism, we exploit channel dependencies and integrate information of different scales into the attention mechanism. At the same time, we improved the basic classification framework based on cross entropy, combined the dice coefficient and cross entropy, and balanced the contribution of dice coefficients and cross entropy loss to the segmentation task, which enhanced the performance of the network in small area segmentation. The network retains more image features, restores the significant features more accurately, and further improves the segmentation performance of medical images. We apply it to the fundus optic disc segmentation task. We demonstrate the segmentation performance of the model on the Messidor dataset and the RIM-ONE dataset, and evaluate the proposed architecture. Experimental results show that our network architecture improves the prediction performance of the base architectures under different datasets while maintaining the computational efficiency. The results render that the proposed technologies improve the segmentation with 0.0469 overlapping error on Messidor.

Medical Image Segmentation based on U-Net: A Review

2020 ◽  
Vol 64 (2) ◽  
pp. 20508-1-20508-12 ◽  
Author(s):  
Getao Du ◽  
Xu Cao ◽  
Jimin Liang ◽  
Xueli Chen ◽  
Yonghua Zhan
Keyword(s):  
Neural Network ◽  
Image Analysis ◽  
Image Segmentation ◽  
Deep Learning ◽  
Medical Imaging ◽  
Medical Image ◽  
Medical Images ◽  
Medical Image Analysis ◽  
Medical Image Segmentation ◽  
Imaging Systems

Abstract Medical image analysis is performed by analyzing images obtained by medical imaging systems to solve clinical problems. The purpose is to extract effective information and improve the level of clinical diagnosis. In recent years, automatic segmentation based on deep learning (DL) methods has been widely used, where a neural network can automatically learn image features, which is in sharp contrast with the traditional manual learning method. U-net is one of the most important semantic segmentation frameworks for a convolutional neural network (CNN). It is widely used in the medical image analysis domain for lesion segmentation, anatomical segmentation, and classification. The advantage of this network framework is that it can not only accurately segment the desired feature target and effectively process and objectively evaluate medical images but also help to improve accuracy in the diagnosis by medical images. Therefore, this article presents a literature review of medical image segmentation based on U-net, focusing on the successful segmentation experience of U-net for different lesion regions in six medical imaging systems. Along with the latest advances in DL, this article introduces the method of combining the original U-net architecture with deep learning and a method for improving the U-net network.

scholarly journals CMM-Net: Contextual multi-scale multi-level network for efficient biomedical image segmentation

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mohammed A. Al-masni ◽  
Dong-Hyun Kim
Keyword(s):  
Image Segmentation ◽  
Blood Vessels ◽  
Medical Image ◽  
Spatial Scales ◽  
Medical Image Analysis ◽  
Skin Lesions ◽  
Medical Image Segmentation ◽  
Multi Scale ◽  
Retinal Blood Vessels ◽  
Multi Level

AbstractMedical image segmentation of tissue abnormalities, key organs, or blood vascular system is of great significance for any computerized diagnostic system. However, automatic segmentation in medical image analysis is a challenging task since it requires sophisticated knowledge of the target organ anatomy. This paper develops an end-to-end deep learning segmentation method called Contextual Multi-Scale Multi-Level Network (CMM-Net). The main idea is to fuse the global contextual features of multiple spatial scales at every contracting convolutional network level in the U-Net. Also, we re-exploit the dilated convolution module that enables an expansion of the receptive field with different rates depending on the size of feature maps throughout the networks. In addition, an augmented testing scheme referred to as Inversion Recovery (IR) which uses logical “OR” and “AND” operators is developed. The proposed segmentation network is evaluated on three medical imaging datasets, namely ISIC 2017 for skin lesions segmentation from dermoscopy images, DRIVE for retinal blood vessels segmentation from fundus images, and BraTS 2018 for brain gliomas segmentation from MR scans. The experimental results showed superior state-of-the-art performance with overall dice similarity coefficients of 85.78%, 80.27%, and 88.96% on the segmentation of skin lesions, retinal blood vessels, and brain tumors, respectively. The proposed CMM-Net is inherently general and could be efficiently applied as a robust tool for various medical image segmentations.

Intuitionistic Level Set Segmentation for Medical Image Segmentation

2020 ◽  
Vol 13 (5) ◽  
pp. 1039-1046
Author(s):  
Jyoti Arora ◽  
Meena Tushir
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Medical Image ◽  
Spatial Information ◽  
Medical Image Analysis ◽  
Primary Objective ◽  
Medical Image Segmentation ◽  
Clinical Images ◽  
Level Set Segmentation ◽  
Segmentation Accuracy

Introduction: Image segmentation is one of the basic practices that involve dividing an image into mutually exclusive partitions. Learning how to partition an image into different segments is considered as one of the most critical and crucial step in the area of medical image analysis. Objective: The primary objective of the work is to design an integrated approach for automating the process of level set segmentation for medical image segmentation. This method will help to overcome the problem of manual initialization of parameters. Methods: In the proposed method, input image is simplified by the process of intuitionistic fuzzification of an image. Further segmentation is done by intuitionistic based clustering technique incorporated with local spatial information (S-IFCM). The controlling parameters of level set method are automated by S-IFCM, for defining anatomical boundaries. Results: Experimental results were carried out on MRI and CT-scan images of brain and liver. The results are compared with existing Fuzzy Level set segmentation; Spatial Fuzzy Level set segmentation using MSE, PSNR and Segmentation Accuracy. Qualitatively results achieved after proposed segmentation technique shows more clear definition of boundaries. The attain PSNR and MSE value of propose algorithm proves the robustness of algorithm. Segmentation accuracy is calculated for the segmentation results of the T-1 weighted axial slice of MRI image with 0.909 value. Conclusion: The proposed method shows good accuracy for the segmentation of medical images. This method is a good substitute for the segmentation of different clinical images with different modalities and proves to give better result than fuzzy technique.

scholarly journals End-to-End Boundary Aware Networks for Medical Image Segmentation

2019 ◽  
Author(s):  
Ali Hatamizadeh ◽  
Demetri Terzopoulos ◽  
Andriy Myronenko
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Medical Image Segmentation ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Anatomical Structures ◽  
Fully Convolutional Neural Networks ◽  
Boundary Information ◽  
End To End

AbstractFully convolutional neural networks (CNNs) have proven to be effective at representing and classifying textural information, thus transforming image intensity into output class masks that achieve semantic image segmentation. In medical image analysis, however, expert manual segmentation often relies on the boundaries of anatomical structures of interest. We propose boundary aware CNNs for medical image segmentation. Our networks are designed to account for organ boundary information, both by providing a special network edge branch and edge-aware loss terms, and they are trainable end-to-end. We validate their effectiveness on the task of brain tumor segmentation using the BraTS 2018 dataset. Our experiments reveal that our approach yields more accurate segmentation results, which makes it promising for more extensive application to medical image segmentation.

scholarly journals ITK Tutorial Presentation Slides

2006 ◽  
Author(s):  
Luis Ibanez ◽  
Lydia Ng ◽  
Josh Cates ◽  
Stephen Aylward ◽  
Bill Lorensen ◽  
...  
Keyword(s):  
Image Processing ◽  
Image Analysis ◽  
Image Segmentation ◽  
Open Source ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Data Representation ◽  
Medical Image Segmentation ◽  
Object Oriented Programming ◽  
Application Development

This course introduces attendees to select open-source efforts in the field of medical image analysis. Opportunities for users and developers are presented. The course particularly focuses on the open-source Insight Toolkit (ITK) for medical image segmentation and registration. The course describes the procedure for downloading and installing the toolkit and covers the use of its data representation and filtering classes. Attendees are shown how ITK can be used in their research, rapid prototyping, and application development.LEARNING OUTCOMES After completing this course, attendees will be able to: contribute to and benefit from open-source software for medical image analysis download and install the ITK toolkit start their own software project based on ITK design and construct an image processing pipeline combine ITK filters for medical image segmentation combine ITK components for medical image registrationINTENDED AUDIENCE This course is intended for anyone involved in medical image analysis. In particular it targets graduate students, researchers and professionals in the areas of computer science and medicine. Attendees should have an intermediate level on object oriented programming with C++ and must be familiar with the basics of medical image processing and analysis.

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