scholarly journals Hippocampus Segmentation Method Based on Subspace Patch-Sparsity Clustering in Noisy Brain MRI

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Xiaogang Ren ◽  
Yue Wu ◽  
Zhiying Cao
Keyword(s):  
Brain Mri ◽  
Coefficient Matrix ◽  
Experimental Results ◽  
Irregular Shape ◽  
Segmentation Method ◽  
Sparse Constraint ◽  
Low Contrast ◽  
Segmentation Accuracy ◽  
Hippocampus Segmentation ◽  
Segmentation Problem

Since the hippocampus is of small size, low contrast, and irregular shape, a novel hippocampus segmentation method based on subspace patch-sparsity clustering in brain MRI is proposed to improve the segmentation accuracy, which requires that the representation coefficients in different subspaces should be as sparse as possible, while the representation coefficients in the same subspace should be as average as possible. By restraining the coefficient matrix with the patch-sparse constraint, the coefficient matrix contains a patch-sparse structure, which is helpful to the hippocampus segmentation. The experimental results show that our proposed method is effective in the noisy brain MRI data, which can well deal with hippocampus segmentation problem.

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 Leukocyte nucleus segmentation method based on enhancing the saliency of saturation component

2019 ◽  
Vol 13 ◽  
pp. 174830261984578 ◽  
Author(s):  
Yapin Wang ◽  
Yiping Cao
Keyword(s):  
Error Rate ◽  
Blood Smear ◽  
Imaging System ◽  
Experimental Results ◽  
Segmentation Method ◽  
Microscopic Imaging ◽  
Distance Error ◽  
Segmentation Accuracy ◽  
Different Color ◽  
Nucleus Segmentation

The accuracy of the leukocyte nucleus segmentation is an important preprocessing step in the leukocyte automatic analysis. However, different dyeing conditions or different illumination conditions may cause capturing different color leukocyte images in microscopic imaging system, which will result in the over-segmentation or under-segmentation of the leukocyte nucleus. A leukocyte nucleus segmentation method based on enhancing the saliency of the saturation component is proposed. While applying the set of calibration offset values [Formula: see text], [Formula: see text], and [Formula: see text] of the red (R), green (G), and blue (B) chrominance value on the blood smear microscopic images, it can enhance the saliency of the saturation component and the saliency of the leukocyte nucleus region increases the most obviously. The leukocyte nuclei are then segmented using Otsu’s histogram thresholding method. The experimental results show that the proposed algorithm outperforms the related algorithms in segmentation accuracy, over-segmentation rate, error rate, and relative distance error. It improves the accuracy, robustness, and universality further.

scholarly journals A New Multistage Medical Segmentation Method Based on Superpixel and Fuzzy Clustering

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Shiyong Ji ◽  
Benzheng Wei ◽  
Zhen Yu ◽  
Gongping Yang ◽  
Yilong Yin
Keyword(s):  
Fuzzy Clustering ◽  
Brain Mri ◽  
Medical Image Segmentation ◽  
Segmentation Method ◽  
Imaging Characteristic ◽  
Multi Stage ◽  
Segmentation Accuracy ◽  
Medical Segmentation ◽  
Influence Of Noise ◽  
Mri Image

The medical image segmentation is the key approach of image processing for brain MRI images. However, due to the visual complex appearance of image structures and the imaging characteristic, it is still challenging to automatically segment brain MRI image. A new multi-stage segmentation method based on superpixel and fuzzy clustering (MSFCM) is proposed to achieve the good brain MRI segmentation results. The MSFCM utilizes the superpixels as the clustering objects instead of pixels, and it can increase the clustering granularity and overcome the influence of noise and bias effectively. In the first stage, the MRI image is parsed into several atomic areas, namely, superpixels, and a further parsing step is adopted for the areas with bigger gray variance over setting threshold. Subsequently, designed fuzzy clustering is carried out to the fuzzy membership of each superpixel, and an iterative broadcast method based on the Butterworth function is used to redefine their classifications. Finally, the segmented image is achieved by merging the superpixels which have the same classification label. The simulated brain database from BrainWeb site is used in the experiments, and the experimental results demonstrate that MSFCM method outperforms the traditional FCM algorithm in terms of segmentation accuracy and stability for MRI image.

Research on Multi-Threshold Color Image Segmentation Based on Rough Set

2011 ◽  
Vol 474-476 ◽  
pp. 771-776
Author(s):  
Guo Quan Zhang ◽  
Zhan Ming Li
Keyword(s):  
Image Segmentation ◽  
Rough Set ◽  
Color Image ◽  
Experimental Results ◽  
Color Image Segmentation ◽  
Segmentation Method ◽  
Illumination Variation ◽  
Threshold Segmentation ◽  
Segmentation Accuracy ◽  
Novel Method

Aims at the problem that the threshold number and value are difficulty to determine automatically existing in multi-threshold color image segmentation method, a novel method of multi-threshold segmentation in HSV is proposed. First of all, the image is pre-processed in HSV, component H and V is projected to S and be quantified at the same time. Secondly, histogram and advanced Histon histogram (AHH) are constructed. According to concept of roughness in the theory of Rough Set, the histogram of roughness (RSH) is constructed. Finally, according to requirement of segmentation accuracy, set a threshold Hn on RSH to determine the number and scope of multi-threshold and the image is segmented with above thresholds. The experimental results show that this method can determine the threshold quantity automatically, segment image efficiently and robust against illumination variation.

CHARACTER SEGMENTATION USING CONVEX-HULL TECHNIQUES

1999 ◽  
Vol 13 (06) ◽  
pp. 833-858 ◽  
Author(s):  
TIEN-CHIEN CHANG ◽  
SHU-YUAN CHEN
Keyword(s):  
Convex Hull ◽  
Shortest Path ◽  
Spatial Organization ◽  
Experimental Results ◽  
Character Segmentation ◽  
Segmentation Method ◽  
Shortest Path Algorithm ◽  
Segmentation Problem ◽  
Detection And Separation

A novel character segmentation method for printed documents is proposed in this paper. It is very difficult to process touching, overlapping and broken characters simultaneously. The strategy of our method is to adjust the binarization parameters such that broken characters can be avoided. On the contrary, adjacent characters may spread into each other seriously. Henceforth, the character segmentation problem can be focused on touching-character detection and separation. In the proposed approach, touching characters can be detected using the topological attributes of characters and the typographical relationship between characters. More specifically, the topological attributes are derived from the spatial organization of concave residua contained in the convex hull enclosing the characters. A shortest-path algorithm together with the convex-hull information is used to separate the composite. Since these features based upon the convex hull are insensitive to character fonts and sizes, the touching-character problem of various fonts and sizes can be managed even for heavily touching characters or italic-type overlapping characters without prior slant correction. The proposed method has been applied to extract isolated characters from the contents of technical journals, which contain characters of various fonts and sizes. The promising experimental results prove the practicality and feasibility of the proposed method.

Segmentation In Medical Resonance images to extract the cancerous nodule for early diagnosis on cancer

2012 ◽  
Vol 3 (2) ◽  
pp. 253-255
Author(s):  
Raman Brar
Keyword(s):  
Human Lung ◽  
Research Work ◽  
Vital Role ◽  
Watershed Algorithm ◽  
Segmentation Method ◽  
Resonance Imaging ◽  
Segmentation Methods ◽  
Segmentation Accuracy ◽  
Background Segmentation ◽  
Lung Mri

Image segmentation plays a vital role in several medical imaging programs by assisting the delineation of physiological structures along with other parts. The objective of this research work is to segmentize human lung MRI (Medical resonance Imaging) images for early detection of cancer.Watershed Transform Technique is implemented as the Segmentation method in this work. Some comparative experiments using both directly applied watershed algorithm and after marking foreground and computed background segmentation methods show the improved lung segmentation accuracy in some image cases.

Recent advancements in Fuzzy C-means based techniques for brain MRI Segmentation

2021 ◽  
Vol 16 ◽  
Author(s):  
Ghazanfar Latif ◽  
Jaafar Alghazo ◽  
Fadi N. Sibai ◽  
D.N.F. Awang Iskandar ◽  
Adil H. Khan
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Brain Mri ◽  
Tumor Segmentation ◽  
Brain Anatomy ◽  
Mri Segmentation ◽  
Brain Tumor Segmentation ◽  
Brain Images ◽  
Low Contrast ◽  
Fuzzy C Means

Background: Variations of image segmentation techniques, particularly those used for Brain MRI segmentation, vary in complexity from basic standard Fuzzy C-means (FCM) to more complex and enhanced FCM techniques. Objective: In this paper, a comprehensive review is presented on all thirteen variations of FCM segmentation techniques. In the review process, the concentration is on the use of FCM segmentation techniques for brain tumors. Brain tumor segmentation is a vital step in the process of automatically diagnosing brain tumors. Unlike segmentation of other types of images, brain tumor segmentation is a very challenging task due to the variations in brain anatomy. The low contrast of brain images further complicates this process. Early diagnosis of brain tumors is indeed beneficial to patients, doctors, and medical providers. Results: FCM segmentation works on images obtained from magnetic resonance imaging (MRI) scanners, requiring minor modifications to hospital operations to early diagnose tumors as most, if not all, hospitals rely on MRI machines for brain imaging. In this paper, we critically review and summarize FCM based techniques for brain MRI segmentation.

Assessment of automatic segmentation accuracy with various point cloud density

2020 ◽  
Vol 961 (7) ◽  
pp. 47-55
Author(s):  
A.G. Yunusov ◽  
A.J. Jdeed ◽  
N.S. Begliarov ◽  
M.A. Elshewy
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Reference Data ◽  
Automatic Segmentation ◽  
Geometric Accuracy ◽  
Segmentation Method ◽  
Segmentation Methods ◽  
Segmentation Accuracy ◽  
Number Of Segments ◽  
Cloud Density

Laser scanning is considered as one of the most useful and fast technologies for modelling. On the other hand, the size of scan results can vary from hundreds to several million points. As a result, the large volume of the obtained clouds leads to complication at processing the results and increases the time costs. One way to reduce the volume of a point cloud is segmentation, which reduces the amount of data from several million points to a limited number of segments. In this article, we evaluated effect on the performance, the accuracy of various segmentation methods and the geometric accuracy of the obtained models at density changes taking into account the processing time. The results of our experiment were compared with reference data in a form of comparative analysis. As a conclusion, some recommendations for choosing the best segmentation method were proposed.

scholarly journals Superpixel Segmentation Based on Anisotropic Edge Strength

2019 ◽  
Vol 5 (6) ◽  
pp. 57 ◽  
Author(s):  
Gang Wang ◽  
Bernard De Baets
Keyword(s):  
Distance Measure ◽  
Saliency Detection ◽  
Experimental Results ◽  
Segmentation Method ◽  
Superpixel Segmentation ◽  
Gaussian Kernels ◽  
First Derivative ◽  
Edge Strength

Superpixel segmentation can benefit from the use of an appropriate method to measure edge strength. In this paper, we present such a method based on the first derivative of anisotropic Gaussian kernels. The kernels can capture the position, direction, prominence, and scale of the edge to be detected. We incorporate the anisotropic edge strength into the distance measure between neighboring superpixels, thereby improving the performance of an existing graph-based superpixel segmentation method. Experimental results validate the superiority of our method in generating superpixels over the competing methods. It is also illustrated that the proposed superpixel segmentation method can facilitate subsequent saliency detection.

scholarly journals A Method of Fast Segmentation for Banana Stalk Exploited Lightweight Multi-Feature Fusion Deep Neural Network

Machines ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 66
Author(s):  
Tianci Chen ◽  
Rihong Zhang ◽  
Lixue Zhu ◽  
Shiang Zhang ◽  
Xiaomin Li
Keyword(s):  
Neural Network ◽  
Execution Time ◽  
Deep Neural Network ◽  
Feature Fusion ◽  
Frame Rate ◽  
Light Conditions ◽  
Segmentation Method ◽  
Segmentation Accuracy ◽  
Automatic Picking ◽  
Changing Light

In an orchard environment with a complex background and changing light conditions, the banana stalk, fruit, branches, and leaves are very similar in color. The fast and accurate detection and segmentation of a banana stalk are crucial to realize the automatic picking using a banana picking robot. In this paper, a banana stalk segmentation method based on a lightweight multi-feature fusion deep neural network (MFN) is proposed. The proposed network is mainly composed of encoding and decoding networks, in which the sandglass bottleneck design is adopted to alleviate the information a loss in high dimension. In the decoding network, a different sized dilated convolution kernel is used for convolution operation to make the extracted banana stalk features denser. The proposed network is verified by experiments. In the experiments, the detection precision, segmentation accuracy, number of parameters, operation efficiency, and average execution time are used as evaluation metrics, and the proposed network is compared with Resnet_Segnet, Mobilenet_Segnet, and a few other networks. The experimental results show that compared to other networks, the number of network parameters of the proposed network is significantly reduced, the running frame rate is improved, and the average execution time is shortened.

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