scholarly journals Convolutional Networks Can Learn to Generate Affinity Graphs for Image Segmentation

2010 ◽  
Vol 22 (2) ◽  
pp. 511-538 ◽  
Author(s):  
Srinivas C. Turaga ◽  
Joseph F. Murray ◽  
Viren Jain ◽  
Fabian Roth ◽  
Moritz Helmstaedter ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Ground Truth ◽  
Image Features ◽  
Convolutional Network ◽  
Convolutional Networks ◽  
Machine Learning Approach ◽  
Segmentation Accuracy ◽  
Segmentation Algorithms ◽  
Neuronal Processes ◽  
Segmentation Problem

Many image segmentation algorithms first generate an affinity graph and then partition it. We present a machine learning approach to computing an affinity graph using a convolutional network (CN) trained using ground truth provided by human experts. The CN affinity graph can be paired with any standard partitioning algorithm and improves segmentation accuracy significantly compared to standard hand-designed affinity functions. We apply our algorithm to the challenging 3D segmentation problem of reconstructing neuronal processes from volumetric electron microscopy (EM) and show that we are able to learn a good affinity graph directly from the raw EM images. Further, we show that our affinity graph improves the segmentation accuracy of both simple and sophisticated graph partitioning algorithms. In contrast to previous work, we do not rely on prior knowledge in the form of hand-designed image features or image preprocessing. Thus, we expect our algorithm to generalize effectively to arbitrary image types.

scholarly journals Boundary Loss-Based 2.5D Fully Convolutional Neural Networks Approach for Segmentation: A Case Study of the Liver and Tumor on Computed Tomography

Algorithms ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 144
Author(s):  
Yuexing Han ◽  
Xiaolong Li ◽  
Bing Wang ◽  
Lu Wang
Keyword(s):  
Image Segmentation ◽  
Spatial Information ◽  
Medical Images ◽  
Tumor Segmentation ◽  
Convolutional Networks ◽  
Learning Framework ◽  
Fully Convolutional Networks ◽  
Segmentation Accuracy ◽  
Boundary Information

Image segmentation plays an important role in the field of image processing, helping to understand images and recognize objects. However, most existing methods are often unable to effectively explore the spatial information in 3D image segmentation, and they neglect the information from the contours and boundaries of the observed objects. In addition, shape boundaries can help to locate the positions of the observed objects, but most of the existing loss functions neglect the information from the boundaries. To overcome these shortcomings, this paper presents a new cascaded 2.5D fully convolutional networks (FCNs) learning framework to segment 3D medical images. A new boundary loss that incorporates distance, area, and boundary information is also proposed for the cascaded FCNs to learning more boundary and contour features from the 3D medical images. Moreover, an effective post-processing method is developed to further improve the segmentation accuracy. We verified the proposed method on LITS and 3DIRCADb datasets that include the liver and tumors. The experimental results show that the performance of the proposed method is better than existing methods with a Dice Per Case score of 74.5% for tumor segmentation, indicating the effectiveness of the proposed method.

A New Medical Image Segmentation Method Based on Chan-Vese Model

2014 ◽  
Vol 513-517 ◽  
pp. 3750-3756 ◽  
Author(s):  
Yuan Zheng Ma ◽  
Jia Xin Chen
Keyword(s):  
Image Segmentation ◽  
Mathematical Morphology ◽  
Level Sets ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Experimental Results ◽  
Segmentation Method ◽  
Accuracy Requirement ◽  
Segmentation Accuracy ◽  
Segmentation Problem

The traditional segmentation method for medical image segmentation is difficult to achieve the accuracy requirement, and when the edges of the image are blurred, it will occurs incomplete segmentation problem, in order to solve this problem, we propose a medical image segmentation method which based on Chan-Vese model and mathematical morphology. The method integrates Chan-Vese model, mathematical morphology, composite multiphase level sets segmentation algorithm, first, through iterative etching operation to extract the outline of the medical image, and then the medical image is segmented by the Chan-Vese model based on the complex multiphase level sets, finally the medical image image is dilated iteratively by using morphological dilation to restore the image. The experimental results and analysis show that, this method improves the multi-region segmentation accuracy during the segmentation of medical image and solves the problem of incomplete segmentation.

scholarly journals Pixel Intensity Clustering Algorithm for Multilevel Image Segmentation

2015 ◽  
Vol 2015 ◽  
pp. 1-19 ◽  
Author(s):  
Oludayo O. Olugbara ◽  
Emmanuel Adetiba ◽  
Stanley A. Oyewole
Keyword(s):  
Image Segmentation ◽  
Clustering Algorithm ◽  
Computational Cost ◽  
Computational Time ◽  
Limiting Factor ◽  
Pixel Intensity ◽  
Computational Costs ◽  
Segmentation Algorithms ◽  
Segmentation Problem ◽  
Significant Attention

Image segmentation is an important problem that has received significant attention in the literature. Over the last few decades, a lot of algorithms were developed to solve image segmentation problem; prominent amongst these are the thresholding algorithms. However, the computational time complexity of thresholding exponentially increases with increasing number of desired thresholds. A wealth of alternative algorithms, notably those based on particle swarm optimization and evolutionary metaheuristics, were proposed to tackle the intrinsic challenges of thresholding. In codicil, clustering based algorithms were developed as multidimensional extensions of thresholding. While these algorithms have demonstrated successful results for fewer thresholds, their computational costs for a large number of thresholds are still a limiting factor. We propose a new clustering algorithm based on linear partitioning of the pixel intensity set and between-cluster variance criterion function for multilevel image segmentation. The results of testing the proposed algorithm on real images from Berkeley Segmentation Dataset and Benchmark show that the algorithm is comparable with state-of-the-art multilevel segmentation algorithms and consistently produces high quality results. The attractive properties of the algorithm are its simplicity, generalization to a large number of clusters, and computational cost effectiveness.

scholarly journals AN OVERVIEW OF IMAGE SEGMENTATION ALGORITHMS

2013 ◽  
pp. 150-156
Author(s):  
PUSHPAJIT A. KHAIRE. ◽  
NILESHSINGH V. THAKUR
Keyword(s):  
Image Segmentation ◽  
Ground Truth ◽  
Network Clustering ◽  
Noisy Images ◽  
Segmentation Methods ◽  
Segmentation Algorithms ◽  
Fuzzy Logic Approach ◽  
Logic Approach ◽  
And Performance ◽  
Segmentation Of Images

Image segmentation is a puzzled problem even after four decades of research. Research on image segmentation is currently conducted in three levels. Development of image segmentation methods, evaluation of segmentation algorithms and performance and study of these evaluation methods. Hundreds of techniques have been proposed for segmentation of natural images, noisy images, medical images etc. Currently most of the researchers are evaluating the segmentation algorithms using ground truth evaluation of (Berkeley segmentation database) BSD images. In this paper an overview of various segmentation algorithms is discussed. The discussion is mainly based on the soft computing approaches used for segmentation of images without noise and noisy images and the parameters used for evaluating these algorithms. Some of these techniques used are Markov Random Field (MRF) model, Neural Network, Clustering, Particle Swarm optimization, Fuzzy Logic approach and different combinations of these soft techniques.

scholarly journals Improving Convolutional Network Interpretability with Exponential Activations

2019 ◽  
Author(s):  
Peter K. Koo ◽  
Matt Ploenzke
Keyword(s):  
Dna Sequences ◽  
Ground Truth ◽  
Genomic Sequences ◽  
Sequence Motifs ◽  
Convolutional Network ◽  
Distributed Representations ◽  
Convolutional Networks ◽  
Synthetic Dna

AbstractDeep convolutional networks trained on regulatory genomic sequences tend to learn distributed representations of sequence motifs across many first layer filters. This makes it challenging to decipher which features are biologically meaningful. Here we introduce the exponential activation that – when applied to first layer filters – leads to more interpretable representations of motifs, both visually and quantitatively, compared to rectified linear units. We demonstrate this on synthetic DNA sequences which have ground truth with various convolutional networks, and then show that this phenomenon holds on in vivo DNA sequences.

scholarly journals Malaria parasite segmentation using U-Net: Comparative study of loss functions

2019 ◽  
Vol 4 (2) ◽  
pp. 57-62
Author(s):  
Julisa Bana Abraham
Keyword(s):  
Network Architecture ◽  
Blood Smear ◽  
Mean Squared Error ◽  
Semantic Segmentation ◽  
Loss Functions ◽  
Thin Blood Smear ◽  
Convolutional Network ◽  
Fully Convolutional Network ◽  
Convolutional Networks ◽  
Segmentation Accuracy

The convolutional neural network is commonly used for classification. However, convolutional networks can also be used for semantic segmentation using the fully convolutional network approach. U-Net is one example of a fully convolutional network architecture capable of producing accurate segmentation on biomedical images. This paper proposes to use U-Net for Plasmodium segmentation on thin blood smear images. The evaluation shows that U-Net can accurately perform Plasmodium segmentation on thin blood smear images, besides this study also compares the three loss functions, namely mean-squared error, binary cross-entropy, and Huber loss. The results show that Huber loss has the best testing metrics: 0.9297, 0.9715, 0.8957, 0.9096 for F1 score, positive predictive value (PPV), sensitivity (SE), and relative segmentation accuracy (RSA), respectively.

scholarly journals Insight into 3D micro-CT data: exploring segmentation algorithms through performance metrics

2017 ◽  
Vol 24 (5) ◽  
pp. 1065-1077 ◽  
Author(s):  
Talita Perciano ◽  
Daniela Ushizima ◽  
Harinarayan Krishnan ◽  
Dilworth Parkinson ◽  
Natalie Larson ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Performance Metrics ◽  
Imaging Modality ◽  
Three Dimensional ◽  
Synthetic Data ◽  
Ground Truth ◽  
Material Design ◽  
Material Structure ◽  
Segmentation Algorithms ◽  
Micro Tomography

Three-dimensional (3D) micro-tomography (µ-CT) has proven to be an important imaging modality in industry and scientific domains. Understanding the properties of material structure and behavior has produced many scientific advances. An important component of the 3D µ-CT pipeline is image partitioning (or image segmentation), a step that is used to separate various phases or components in an image. Image partitioning schemes require specific rules for different scientific fields, but a common strategy consists of devising metrics to quantify performance and accuracy. The present article proposes a set of protocols to systematically analyze and compare the results of unsupervised classification methods used for segmentation of synchrotron-based data. The proposed dataflow for Materials Segmentation and Metrics (MSM) provides 3D micro-tomography image segmentation algorithms, such as statistical region merging (SRM),k-means algorithm and parallel Markov random field (PMRF), while offering different metrics to evaluate segmentation quality, confidence and conformity with standards. Both experimental and synthetic data are assessed, illustrating quantitative results through the MSM dashboard, which can return sample information such as media porosity and permeability. The main contributions of this work are: (i) to deliver tools to improve material design and quality control; (ii) to provide datasets for benchmarking and reproducibility; (iii) to yield good practices in the absence of standards or ground-truth for ceramic composite analysis.

Spatial Relational Attention Using Fully Convolutional Networks for Image Caption Generation

2020 ◽  
Vol 19 (02) ◽  
pp. 2050011
Author(s):  
Teng Jiang ◽  
Liang Gong ◽  
Yupu Yang
Keyword(s):  
Spatial Relations ◽  
Image Features ◽  
Attention Mechanism ◽  
Convolutional Network ◽  
Convolutional Networks ◽  
Fully Convolutional Networks ◽  
Benchmark Datasets ◽  
Visual Concepts ◽  
Image Caption Generation ◽  
Image Caption

Attention-based encoder–decoder framework has greatly improved image caption generation tasks. The attention mechanism plays a transitional role by transforming static image features into sequential captions. To generate reasonable captions, it is of great significance to detect spatial characteristics of images. In this paper, we propose a spatial relational attention approach to consider spatial positions and attributes. Image features are firstly weighted by the attention mechanism. Then they are concatenated with contextual features to form a spatial–visual tensor. The tensor is feature extracted by a fully convolutional network to produce visual concepts for the decoder network. The fully convolutional layers maintain spatial topology of images. Experiments conducted on the three benchmark datasets, namely Flickr8k, Flickr30k and MSCOCO, demonstrate the effectiveness of our proposed approach. Captions generated by the spatial relational attention method precisely capture spatial relations of objects.

scholarly journals Mathematical Modelling of Ground Truth Image for 3D Microscopic Objects Using Cascade of Convolutional Neural Networks Optimized with Parameters’ Combinations Generators

Symmetry ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 416
Author(s):  
Omar Bilalovic ◽  
Zikrija Avdagic ◽  
Samir Omanovic ◽  
Ingmar Besic ◽  
Vedad Letic ◽  
...  
Keyword(s):  
Neural Networks ◽  
Image Segmentation ◽  
Mathematical Modelling ◽  
Convolutional Neural Networks ◽  
Data Augmentation ◽  
Ground Truth ◽  
3D Images ◽  
Segmentation Accuracy ◽  
Augmentation Techniques ◽  
Ground Truth Image

Mathematical modelling to compute ground truth from 3D images is an area of research that can strongly benefit from machine learning methods. Deep neural networks (DNNs) are state-of-the-art methods design for solving these kinds of difficulties. Convolutional neural networks (CNNs), as one class of DNNs, can overcome special requirements of quantitative analysis especially when image segmentation is needed. This article presents a system that uses a cascade of CNNs with symmetric blocks of layers in chain, dedicated to 3D image segmentation from microscopic images of 3D nuclei. The system is designed through eight experiments that differ in following aspects: number of training slices and 3D samples for training, usage of pre-trained CNNs and number of slices and 3D samples for validation. CNNs parameters are optimized using linear, brute force, and random combinatorics, followed by voter and median operations. Data augmentation techniques such as reflection, translation and rotation are used in order to produce sufficient training set for CNNs. Optimal CNN parameters are reached by defining 11 standard and two proposed metrics. Finally, benchmarking demonstrates that CNNs improve segmentation accuracy, reliability and increased annotation accuracy, confirming the relevance of CNNs to generate high-throughput mathematical ground truth 3D images.

scholarly journals Improvement of Region-Merging Image Segmentation Accuracy Using Multiple Merging Criteria

2021 ◽  
Vol 13 (14) ◽  
pp. 2782
Author(s):  
Haoyu Wang ◽  
Zhanfeng Shen ◽  
Zihan Zhang ◽  
Zeyu Xu ◽  
Shuo Li ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Satellite Images ◽  
Evaluation Method ◽  
Remote Sensing Image ◽  
Region Merging ◽  
Segmentation Accuracy ◽  
Segmentation Algorithms ◽  
The Stability ◽  
Remote Sensing Image Processing ◽  
Previous Iteration

Image segmentation plays a significant role in remote sensing image processing. Among numerous segmentation algorithms, the region-merging segmentation algorithm is widely used due to its well-organized structure and outstanding results. Many merging criteria (MC) were designed to improve the accuracy of region-merging segmentation, but each MC has its own shortcomings, which can cause segmentation errors. Segmentation accuracy can be improved by referring to the segmentation results. To achieve this, an approach for detecting and correcting region-merging image segmentation errors is proposed, and then an iterative optimization model is established. The main contributions of this paper are as follows: (1) The conflict types of matching segment pairs are divided into scale-expression conflict (SEC) and region-ownership conflict (ROC), and ROC is more suitable for optimization. (2) An equal-scale local evaluation method was designed to quantify the optimization potential of ROC. (3) A regional anchoring strategy is proposed to preserve the results of the previous iteration optimization. Three QuickBird satellite images of different land-cover types were used for validating the proposed approach. Both unsupervised and supervised evaluation results prove that the proposed approach can effectively improve segmentation accuracy. All explicit and implicit optimization modes are concluded, which further illustrate the stability of the proposed approach.

Sign in / Sign up

Export Citation Format

Share Document