Modified Flower Pollination-based Segmentation of Medical Images

2021 ◽  
pp. 2250026
Author(s):  
Kumaran @ Kumar Jayaraman ◽  
Koganti Srilakshmi ◽  
Sasikala Jayaraman
Keyword(s):  
Medical Image ◽  
Medical Images ◽  
Medical Image Segmentation ◽  
Superior Performance ◽  
Problem Space ◽  
Flower Pollination ◽  
Optimal Thresholds ◽  
Segmented Images ◽  
The Given ◽  
Segmentation Problem

This paper presents a modified flower pollination-based method for performing multilevel segmentation of medical images. The flower pollination-based optimization (FPO) models the pollination process of flowers. Bees serve a major role in the pollination activity of flowers and they memorize and recognize the best flowers producing large pollens of nectar. Such memorizing ability of bees is adapted in the FPO for improving the exploration ability of the algorithm. Besides, the mechanism of avoiding predators by pollinators is also included in the modified FPO (MFPO) for getting away from sub-optimal traps. The medical image segmentation problem is transformed into an optimization problem and solved using the modified FPO (MFPO). The method explores for optimal thresholds in the problem space of the given medical image. The segmented images are presented for showing the superior performance of the proposed method.

scholarly journals DMCNN: A Deep Multiscale Convolutional Neural Network Model for Medical Image Segmentation

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Lin Teng ◽  
Hang Li ◽  
Shahid Karim
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Image Segmentation ◽  
Convolutional Neural Network ◽  
Medical Image ◽  
Data Augmentation ◽  
Medical Images ◽  
Medical Image Segmentation ◽  
Related Area ◽  
Segmentation Accuracy

Medical image segmentation is one of the hot issues in the related area of image processing. Precise segmentation for medical images is a vital guarantee for follow-up treatment. At present, however, low gray contrast and blurred tissue boundaries are common in medical images, and the segmentation accuracy of medical images cannot be effectively improved. Especially, deep learning methods need more training samples, which lead to time-consuming process. Therefore, we propose a novelty model for medical image segmentation based on deep multiscale convolutional neural network (CNN) in this article. First, we extract the region of interest from the raw medical images. Then, data augmentation is operated to acquire more training datasets. Our proposed method contains three models: encoder, U-net, and decoder. Encoder is mainly responsible for feature extraction of 2D image slice. The U-net cascades the features of each block of the encoder with those obtained by deconvolution in the decoder under different scales. The decoding is mainly responsible for the upsampling of the feature graph after feature extraction of each group. Simulation results show that the new method can boost the segmentation accuracy. And, it has strong robustness compared with other segmentation methods.

scholarly journals Medical Image Segmentation and Classification Based on Sparse Representation and Dictionary Learnng Algorithms

2021 ◽  
Author(s):  
Mohammadali Julazadeh
Keyword(s):  
Sparse Representation ◽  
Medical Image ◽  
Medical Images ◽  
Medical Image Segmentation ◽  
Accuracy Rate ◽  
Automated Method ◽  
Classification Framework ◽  
Learning Techniques ◽  
Sparse Representation Classification ◽  
Euclidian Distances

In this thesis a novel classification approach based on sparse representation framework is proposed. The method finds the minimum Euclidian distance between an input patch (pattern) and atoms (templates) of a learned-base dictionary for different classes to perform the classification task. A mathematical approach is developed to map the sparse representation vector to Euclidian distances. We show that the highest coefficient of the sparse vector is not necessarily a suitable indicator for classification. The proposed algorithm is compared with the conventional Sparse Representation Classification (SRC) framework as well as non-sparse based methods to evaluate its performance. Taking advantage of the introduced classification framework, we then propose a novel fully automated method for the purpose of segmenting different organs in medical images of the human body. Our results demonstrated an acceptable accuracy rate for both classification and the segmentation frameworks. To our knowledge, no other method utilizes sparse representation and dictionary learning techniques in order to segment medical images.

A Study on Various Image Segmentation Algorithms

2018 ◽  
pp. 272-276
Author(s):  
S. DivyaMeena ◽  
M. Mangaleswaran
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Medical Images ◽  
Large Data ◽  
Performance Measure ◽  
Medical Image Segmentation ◽  
3D Image ◽  
Medical Field ◽  
Segmentation Algorithms ◽  
Mri Image

Medical images have made a great effect on medicine, diagnosis, and treatment. The most important part of image processing is image segmentation. Medical Image Segmentation is the development of programmed or semi-automatic detection of limitations within a 2D or 3D image. In medical field, image segmentation is one of the vital steps in Image identification and Object recognition. Image segmentation is a method in which large data is partitioned into small amount of data. If the input MRI image is segmented then identifying the lump attacked region will be easier for physicians. In recent days, many algorithms are proposed for the image segmentation. In this paper, an analysis is made on various segmentation algorithms for medical images. Furthermore, a comparison of existing segmentation algorithms is also discussed along with the performance measure of each.

Two-Step Modified Nash Equilibrium Method for Medical Image Segmentation Based on an Improved C-V Model

2018 ◽  
Vol 8 (9) ◽  
pp. 1826-1834
Author(s):  
Tian Chi Zhang ◽  
Jian Pei Zhang ◽  
Jing Zhang ◽  
Melvyn L. Smith
Keyword(s):  
Image Segmentation ◽  
Nash Equilibrium ◽  
Level Set ◽  
Medical Images ◽  
Medical Image Segmentation ◽  
Clustering Problem ◽  
Object Contour ◽  
Segmentation Methods ◽  
Maximum Similarity ◽  
Segmentation Problem

One of the most established region-based segmentation methods is the region based C-V model. This method formulates the image segmentation problem as a level set or improved level set clustering problem. However, the existing level set C-V model fails to perform well in the presence of noisy and incomplete data or when there is similarity between the objects and background, especially for clustering or segmentation tasks in medical images where objects appear vague and poorly contrasted in greyscale. In this paper, we modify the level set C-V model using a two-step modified Nash equilibrium approach. Firstly, a standard deviation using an entropy payoff approach is employed and secondly a two-step similarity clustering based approach is applied to the modified Nash equilibrium. One represents a maximum similarity within the clustered regions and the other the minimum similarity between the clusters. Finally, an improved C-V model based on a two-step modified Nash equilibrium is proposed to smooth the object contour during the image segmentation. Experiments demonstrate that the proposed method has good performance for segmenting noisy and poorly contrasting regions within medical images.

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 Multimodal Medical Image Registration Based on an Information-Theory Measure with Histogram Estimation of Continuous Image Representation

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Bicao Li ◽  
Guanyu Yang ◽  
Zhoufeng Liu ◽  
Jean Louis Coatrieux ◽  
Huazhong Shu
Keyword(s):  
Image Registration ◽  
Medical Image ◽  
Medical Images ◽  
Image Representation ◽  
Superior Performance ◽  
Geometric Transformation ◽  
Medical Image Registration ◽  
Continuous Image ◽  
Joint Histogram ◽  
Histogram Estimation

This work presents a novel method for multimodal medical registration based on histogram estimation of continuous image representation. The proposed method, regarded as “fast continuous histogram estimation,” employs continuous image representation to estimate the joint histogram of two images to be registered. The Jensen–Arimoto (JA) divergence is a similarity measure to measure the statistical dependence between medical images from different modalities. The estimated joint histogram is exploited to calculate the JA divergence in multimodal medical image registration. In addition, to reduce the grid effect caused by the grid-aligning transformations between two images and improve the implementation speed of the registration method, random samples instead of all pixels are extracted from the images to be registered. The goal of the registration is to optimize the JA divergence, which would be maximal when two misregistered images are perfectly aligned using the downhill simplex method, and thus to get the optimal geometric transformation. Experiments are conducted on an affine registration of 2D and 3D medical images. Results demonstrate the superior performance of the proposed method compared to standard histogram, Parzen window estimations, particle filter, and histogram estimation based on continuous image representation without random sampling.

scholarly journals Exploiting Global Structure Information to Improve Medical Image Segmentation

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3249
Author(s):  
Jaemoon Hwang ◽  
Sangheum Hwang
Keyword(s):  
Medical Image ◽  
Medical Images ◽  
Feature Space ◽  
Medical Image Segmentation ◽  
Global Structure ◽  
Distance Metrics ◽  
Anatomical Structures ◽  
Structure Information ◽  
Anatomical Priors ◽  
Segmentation Models

In this paper, we propose a method to enhance the performance of segmentation models for medical images. The method is based on convolutional neural networks that learn the global structure information, which corresponds to anatomical structures in medical images. Specifically, the proposed method is designed to learn the global boundary structures via an autoencoder and constrain a segmentation network through a loss function. In this manner, the segmentation model performs the prediction in the learned anatomical feature space. Unlike previous studies that considered anatomical priors by using a pre-trained autoencoder to train segmentation networks, we propose a single-stage approach in which the segmentation network and autoencoder are jointly learned. To verify the effectiveness of the proposed method, the segmentation performance is evaluated in terms of both the overlap and distance metrics on the lung area and spinal cord segmentation tasks. The experimental results demonstrate that the proposed method can enhance not only the segmentation performance but also the robustness against domain shifts.

scholarly journals Medical Images Segmentation Based on Unsupervised Algorithms: A Review

2021 ◽  
Vol 1 (2) ◽  
pp. 71-80
Author(s):  
Revella E. A. Armya Armya ◽  
Adnan Mohsin Abdulazeez
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Medical Imaging ◽  
Medical Image ◽  
Medical Images ◽  
Medical Image Segmentation ◽  
Automatic Process ◽  
X Rays ◽  
Resonance Imaging ◽  
Unsupervised Algorithms

Medical image segmentation plays an essential role in computer-aided diagnostic systems in various applications. Therefore, researchers are attracted to apply new algorithms for medical image processing because it is a massive investment in developing medical imaging methods such as dermatoscopy, X-rays, microscopy, ultrasound, computed tomography (CT), positron emission tomography, and magnetic resonance imaging. (Magnetic Resonance Imaging), So segmentation of medical images is considered one of the most important medical imaging processes because it extracts the field of interest from the Return on investment (ROI) through an automatic or semi-automatic process. The medical image is divided into regions based on the specific descriptions, such as tissue/organ division in medical applications for border detection, tumor detection/segmentation, and comprehensive and accurate detection. Several methods of segmentation have been proposed in the literature, but their efficacy is difficult to compare. To better address, this issue, a variety of measurement standards have been suggested to decide the consistency of the segmentation outcome. Unsupervised ranking criteria use some of the statistics in the hash score based on the original picture. The key aim of this paper is to study some literature on unsupervised algorithms (K-mean, K-medoids) and to compare the working efficiency of unsupervised algorithms with different types of medical images.

scholarly journals Identification of Blood Vessel Clot Region using Fuzzy C Means Clustering Based Artificial Bee Colony Algorithm

2019 ◽  
Vol 9 (1S4) ◽  
pp. 741-746
Keyword(s):  
Medical Image ◽  
Artificial Bee Colony ◽  
Soft Tissues ◽  
Optimization Problems ◽  
Medical Images ◽  
Medical Image Segmentation ◽  
Statistical Parameters ◽  
Fuzzy C Means ◽  
Bee Colony ◽  
Fuzzy C Means Clustering

Medical image segmentation results in the multiple fractioning of an input image for a deeper analysis/insight. Localization of objects and detection of boundaries are the coretheme of using segmentation for medical images. It elucidates the process of finding the anatomic structures in medical images. In this paper, we put forth a technique that has Fuzzy C-Means clustering and Artificial Bee Colony (ABC) Optimization has delivered the segmentation of MRA brain image. Artificial Bee Colony (ABC) has been used by many researchers as it is a population-based stochastic approach that has better search-inspace abilities for various optimization problems. The unsupervised clustering FCM has produced candidate outcomes in medical image processing. FCM is mostly preferable for segmenting the soft tissues in brain model, and it provides better output when compared to some of the competitive clustering techniques like KM, EM and KNN. The output of the suggested techniques is verified by using real MRA brain images. The results of Statistical parameters show that our method is notably better compared to other algorithms.

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