Meta-Heuristic Algorithms in Medical Image Segmentation

2018 ◽  
pp. 185-203 ◽  
Author(s):  
Nilanjan Dey ◽  
Amira S. Ashour
Keyword(s):  
Image Segmentation ◽  
Soft Computing ◽  
Heuristic Algorithms ◽  
Similarity Measures ◽  
Memetic Algorithms ◽  
Volumetric Analysis ◽  
Human Mind ◽  
Medical Image Segmentation ◽  
Particle Swarms ◽  
Computed Tomography Images

Artificial intelligence is the outlet of computer science apprehensive with creating computers that perform as humans. It compromises expert systems, playing games, natural language, and robotics. However, soft computing (SC) varies from the hard (conventional) computing in its tolerant of partial truth, uncertainty, imprecision, and approximation, thus, it models the human mind. The most common SC techniques include neural networks, fuzzy systems, machine learning, and the meta-heuristic stochastic algorithms (e.g., Cellular automata, ant colony optimization, Memetic algorithms, particle swarms, Tabu search, evolutionary computation and simulated annealing. Due to the required accurate diseases analysis, magnetic resonance imaging, computed tomography images and images of other modalities segmentation remains a challenging problem. Over the past years, soft computing approaches attract attention of several researchers for problems solving in medical data applications. Image segmentation is the process that partitioned an image into some groups based on similarity measures. This process is employed for abnormalities volumetric analysis in medical images to identify the disease nature. Recently, meta-heuristic algorithms are conducted to support the segmentation techniques. In the current chapter, different segmentation procedures are addressed. Several meta-heuristic approaches are reported with highlights on their procedures. Finally, several medical applications using meta-heuristic based-approaches for segmentation are discussed.

scholarly journals Why Use Position Features in Liver Segmentation Performed by Convolutional Neural Network

2021 ◽  
Vol 12 ◽  
Author(s):  
Miroslav Jiřík ◽  
Filip Hácha ◽  
Ivan Gruber ◽  
Richard Pálek ◽  
Hynek Mírka ◽  
...  
Keyword(s):  
Neural Network ◽  
Computed Tomography ◽  
Image Segmentation ◽  
Convolutional Neural Network ◽  
Medical Image Segmentation ◽  
Position Estimation ◽  
The Body ◽  
Position Information ◽  
Computed Tomography Images ◽  
Distance Fields

Liver volumetry is an important tool in clinical practice. The calculation of liver volume is primarily based on Computed Tomography. Unfortunately, automatic segmentation algorithms based on handcrafted features tend to leak segmented objects into surrounding tissues like the heart or the spleen. Currently, convolutional neural networks are widely used in various applications of computer vision including image segmentation, while providing very promising results. In our work, we utilize robustly segmentable structures like the spine, body surface, and sagittal plane. They are used as key points for position estimation inside the body. The signed distance fields derived from these structures are calculated and used as an additional channel on the input of our convolutional neural network, to be more specific U-Net, which is widely used in medical image segmentation tasks. Our work shows that this additional position information improves the results of the segmentation. We test our approach in two experiments on two public datasets of Computed Tomography images. To evaluate the results, we use the Accuracy, the Hausdorff distance, and the Dice coefficient. Code is publicly available at: https://gitlab.com/hachaf/liver-segmentation.git.

Level-set image processing methods in medical image segmentation

2015 ◽  
Vol 11 (1) ◽  
Author(s):  
Marcin Maciejewski ◽  
Wojciech Surtel ◽  
Barbara Maciejewska ◽  
Teresa Małecka-Massalska
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Level Set ◽  
Medical Image ◽  
Medical Image Processing ◽  
Medical Image Segmentation ◽  
Processing Methods ◽  
Quality Of Results ◽  
Computed Tomography Images

AbstractIn this paper, two image processing methods for use in medical image processing based on the level set method are described. The theoretical bases are described and the methods are applied to a set of sample computed tomography images. The results are then compared. The results indicate that the Chan-Vese method is more useful for image segmentation in medicine than the distance-regulated method owing to both the significant differences in calculation time and the quality of results obtained for noisy images.

Multiresolution Bio-Medical Image Segmentation using Fuzzy C-Means Clustering

2019 ◽  
Vol 8 (4) ◽  
pp. 9548-9551
Keyword(s):  
Image Segmentation ◽  
Medical Image Segmentation ◽  
Experimental Result ◽  
Segmentation Method ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Robust Image ◽  
Multiple Clusters ◽  
Single Pixel ◽  
Popular Image

Fuzzy c-means clustering is a popular image segmentation technique, in which a single pixel belongs to multiple clusters, with varying degree of membership. The main drawback of this method is it sensitive to noise. This method can be improved by incorporating multiresolution stationary wavelet analysis. In this paper we develop a robust image segmentation method using Fuzzy c-means clustering and wavelet transform. The experimental result shows that the proposed method is more accurate than the Fuzzy c-means clustering.

Design of Superpiexl U-Net Network for Medical Image Segmentation

2019 ◽  
Vol 31 (6) ◽  
pp. 1007 ◽  
Author(s):  
Haiou Wang ◽  
Hui Liu ◽  
Qiang Guo ◽  
Kai Deng ◽  
Caiming Zhang
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Segmentation

scholarly journals A Self-Spatial Adaptive Weighting Based U-Net for Image Segmentation

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 348
Author(s):  
Choongsang Cho ◽  
Young Han Lee ◽  
Jongyoul Park ◽  
Sangkeun Lee
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Feature Maps ◽  
Data Set ◽  
Feature Map ◽  
Adaptive Weighting ◽  
Spatially Adaptive ◽  
Wide Range ◽  
Decoder Architecture

Semantic image segmentation has a wide range of applications. When it comes to medical image segmentation, its accuracy is even more important than those of other areas because the performance gives useful information directly applicable to disease diagnosis, surgical planning, and history monitoring. The state-of-the-art models in medical image segmentation are variants of encoder-decoder architecture, which is called U-Net. To effectively reflect the spatial features in feature maps in encoder-decoder architecture, we propose a spatially adaptive weighting scheme for medical image segmentation. Specifically, the spatial feature is estimated from the feature maps, and the learned weighting parameters are obtained from the computed map, since segmentation results are predicted from the feature map through a convolutional layer. Especially in the proposed networks, the convolutional block for extracting the feature map is replaced with the widely used convolutional frameworks: VGG, ResNet, and Bottleneck Resent structures. In addition, a bilinear up-sampling method replaces the up-convolutional layer to increase the resolution of the feature map. For the performance evaluation of the proposed architecture, we used three data sets covering different medical imaging modalities. Experimental results show that the network with the proposed self-spatial adaptive weighting block based on the ResNet framework gave the highest IoU and DICE scores in the three tasks compared to other methods. In particular, the segmentation network combining the proposed self-spatially adaptive block and ResNet framework recorded the highest 3.01% and 2.89% improvements in IoU and DICE scores, respectively, in the Nerve data set. Therefore, we believe that the proposed scheme can be a useful tool for image segmentation tasks based on the encoder-decoder architecture.

Multi-Encoder Parse-Decoder Network for Sequential Medical Image Segmentation

2021 ◽  
Author(s):  
Dachuan Shi ◽  
Ruiyang Liu ◽  
Linmi Tao ◽  
Zuoxiang He ◽  
Li Huo
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Segmentation

Automatic segmentation of medical images using a novel Harris Hawk optimization method and an active contour model

2021 ◽  
pp. 1-19
Author(s):  
Maria Tamoor ◽  
Irfan Younas
Keyword(s):  
Image Segmentation ◽  
Active Contour ◽  
Medical Image ◽  
Active Contour Model ◽  
Heart Diseases ◽  
Medical Image Segmentation ◽  
Gaussian Kernel ◽  
Initial Contour ◽  
Automated Method ◽  
Contour Model

Medical image segmentation is a key step to assist diagnosis of several diseases, and accuracy of a segmentation method is important for further treatments of different diseases. Different medical imaging modalities have different challenges such as intensity inhomogeneity, noise, low contrast, and ill-defined boundaries, which make automated segmentation a difficult task. To handle these issues, we propose a new fully automated method for medical image segmentation, which utilizes the advantages of thresholding and an active contour model. In this study, a Harris Hawks optimizer is applied to determine the optimal thresholding value, which is used to obtain the initial contour for segmentation. The obtained contour is further refined by using a spatially varying Gaussian kernel in the active contour model. The proposed method is then validated using a standard skin dataset (ISBI 2016), which consists of variable-sized lesions and different challenging artifacts, and a standard cardiac magnetic resonance dataset (ACDC, MICCAI 2017) with a wide spectrum of normal hearts, congenital heart diseases, and cardiac dysfunction. Experimental results show that the proposed method can effectively segment the region of interest and produce superior segmentation results for skin (overall Dice Score 0.90) and cardiac dataset (overall Dice Score 0.93), as compared to other state-of-the-art algorithms.

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