scholarly journals Image Segmentation for Review of Cerebral Apoplexy

2021 ◽  
Vol 38 (6) ◽  
pp. 1775-1782
Author(s):  
Na Jiang
Keyword(s):  
Feature Selection ◽  
Image Segmentation ◽  
Prior Information ◽  
Segmentation Method ◽  
Lesion Segmentation ◽  
Multiple Lesion ◽  
Rehabilitation Process ◽  
Imaging Tool ◽  
Key Techniques ◽  
Selection Of

Brain computed tomography (CT) provides a medical imaging tool for reviewing cerebral apoplexy. It is of strong clinical significance to study the key techniques for lesion segmentation and feature selection of cerebral apoplexy. Most of the previous research fail to fully utilized the other prior information, or apply to the changing feature analysis on multiple lesion images generated in the rehabilitation process. Therefore, this paper aims to develop an image segmentation method for review of cerebral apoplexy. Based on the correlation between image series, the authors proposed a segmentation method for CT images of cerebral apoplexy, and developed a way to extract and select the changing lesion features, which assists with the diagnosis of cerebral apoplexy rehabilitation. The image segmentation and feature selection results were obtained through experiments, revealing the effectiveness of our method.

Least-Squares Halftoning Algorithm Based on Image Segmentation

2012 ◽  
Vol 263-266 ◽  
pp. 2082-2087
Author(s):  
Zi Fen He ◽  
Zhao Lin Zhan ◽  
Yin Hui Zhang
Keyword(s):  
Image Segmentation ◽  
Least Squares ◽  
Performance Measure ◽  
Distance Metrics ◽  
Digital Halftoning ◽  
Segmentation Method ◽  
Halftone Image ◽  
Printer Model ◽  
Halftone Images ◽  
Selection Of

This work presents a method based on the image content for digital halftoning using K-means clustering theory. Our algorithm applies to both a printer model and a model for the human visual system (HVS). The method strives to minimize the perceived error between the continuous original image and the halftone image. First, the gray image is partitioned into two, three and four regions using K-means image segmentation method, whose performance depends on the selection of distance metrics. Next, the statistics of average gray value of each clustering is calculated. Each clustering uses the least-squares model-based(Lsmb) algorithm to obtain halftone image. Finally, analysis and simulation results show that the proposed algorithm produces better gray-scale halftone image quality when we increase the number of clustering with a certain range. A performance measure for halftone images is used to evaluate our algorithm. The value of MSEv, WSNR and PSNR for two partitions is almost the same as that of the Lsmb algorithm, but for three and four partitions that the proposed algorithm achieves consistently better values of MSEv, WSNR and PSNR than the Lsmb algorithm.

scholarly journals Skin Lesion Segmentation using Deep Hypercolumn Descriptors

2017 ◽  
Vol 3 (1) ◽  
Author(s):  
Dhanesh Ramachandram ◽  
Graham W. Taylor
Keyword(s):  
Image Segmentation ◽  
Skin Lesion ◽  
State Of The Art ◽  
Jaccard Index ◽  
Segmentation Method ◽  
Lesion Segmentation ◽  
Malignant Skin

We present a image segmentation method based on deep hypercolumndescriptors which produces state-of-the-art results for thesegmentation of several classes of benign and malignant skin lesions.We achieve a Jaccard index of 0.792 on the 2017 ISIC SkinLesion Segmentation Challenge dataset.

Study on Otsu Threshold Method for Image Segmentation Based on Genetic Algorithm

2014 ◽  
Vol 998-999 ◽  
pp. 925-928 ◽  
Author(s):  
Zhi Bo Xu ◽  
Pei Jiang Chen ◽  
Shi Li Yan ◽  
Tai Hua Wang
Keyword(s):  
Genetic Algorithm ◽  
Image Processing ◽  
Image Segmentation ◽  
Image Process ◽  
Segmentation Method ◽  
Threshold Method ◽  
Threshold Segmentation ◽  
Maximum Variance ◽  
Segmentation Image ◽  
Selection Of

Threshold segmentation method was widely applied in image process and the selection of threshold affected the final results of image segmentation to a large extent. In order to improve the accuracy and the calculation speed of image segmentation, an Otsu threshold segmentation method based on genetic algorithm was offered. According to the threshold and the gray scale values of pixels, the pixels were divided into two categories, and then the genetic algorithm was used to find the maximum variance between clusters and obtain the optimal threshold of segmentation image. The experimental results show that this method can be used to segment the image effectively, which make the basis for image processing and analysis in the next step.

scholarly journals Prior-based Hierarchical Segmentation Highlighting Structures of Interest

2019 ◽  
Vol 3 (1) ◽  
pp. 29-44
Author(s):  
Amin Fehri ◽  
Santiago Velasco-Forero ◽  
Fernand Meyer
Keyword(s):  
Image Segmentation ◽  
Prior Information ◽  
Spatial Information ◽  
Regions Of Interest ◽  
The Other ◽  
Segmentation Method ◽  
Hierarchical Segmentation ◽  
Other Hand ◽  
Major Trend

AbstractImage segmentation is the process of partitioning an image into a set of meaningful regions according to some criteria. Hierarchical segmentation has emerged as a major trend in this regard as it favors the emergence of important regions at different scales. On the other hand, many methods allow us to have prior information on the position of structures of interest in the images. In this paper, we present a versatile hierarchical segmentation method that takes into account any prior spatial information and outputs a hierarchical segmentation that emphasizes the contours or regions of interest while preserving the important structures in the image. Several applications are presented that illustrate the method versatility and efficiency.

scholarly journals Saliency-Aware Class-Agnostic Food Image Segmentation

2021 ◽  
Vol 2 (3) ◽  
pp. 1-17
Author(s):  
Sri Kalyan Yarlagadda ◽  
Daniel Mas Montserrat ◽  
David Güera ◽  
Carol J. Boushey ◽  
Deborah A. Kerr ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Prior Information ◽  
Dietary Assessment ◽  
Segmentation Method ◽  
Dietary Assessment Methods ◽  
Before And After ◽  
Dietary Study ◽  
Food Image ◽  
Food Images ◽  
Food Consumed

Advances in image-based dietary assessment methods have allowed nutrition professionals and researchers to improve the accuracy of dietary assessment, where images of food consumed are captured using smartphones or wearable devices. These images are then analyzed using computer vision methods to estimate energy and nutrition content of the foods. Food image segmentation, which determines the regions in an image where foods are located, plays an important role in this process. Current methods are data dependent and thus cannot generalize well for different food types. To address this problem, we propose a class-agnostic food image segmentation method. Our method uses a pair of eating scene images, one before starting eating and one after eating is completed. Using information from both the before and after eating images, we can segment food images by finding the salient missing objects without any prior information about the food class. We model a paradigm of top-down saliency that guides the attention of the human visual system based on a task to find the salient missing objects in a pair of images. Our method is validated on food images collected from a dietary study that showed promising results.

An Image Segmentation Method Using Image Enhancement and PCNN with Adaptive Parameters

2012 ◽  
Vol 490-495 ◽  
pp. 1251-1255 ◽  
Author(s):  
Hong Cai ◽  
Xue Yuan Zhang ◽  
Hai Tao Dai ◽  
Dong Ming Zhou
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Image Enhancement ◽  
Adaptive Algorithm ◽  
Experimental Results ◽  
Edge Extraction ◽  
Segmentation Method ◽  
Adaptive Parameters ◽  
Selection Of ◽  
Better Than

PCNN model is particularly suitable for image segmentation and edge extraction, but its effect depends on the selection of parameters in PCNN model and network iteration settings, which needs for a large number of artificial interaction and has limited PCNN image processing practicality. In this paper, through combining statistical properties of images and PCNN model, we present an adaptive algorithm based on the distribution of pixels to replace the artificial interaction. Experimental results show that image segmentation using image enhancement and PCNN with adaptive parameters is significantly better than the traditional PCNN image segmentation and verify the effectiveness of the method.

scholarly journals Investigation of the applicability of the convolutional neural network U-Net to a problem of segmentation of aircraft images

2021 ◽  
Vol 45 (4) ◽  
pp. 575-579
Author(s):  
D.A. Gavrilov
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Convolutional Neural Network ◽  
Segmentation Method ◽  
The Neural Network ◽  
Selection Of

The paper investigates the applicability of the convolutional neural network "U-Net" to a problem of segmentation of aircraft images. The neural network image segmentation method is based on the "Carvana" implementation with the "U-Net" architecture. For orientation recognition, a neural network built in the Keras open neural network library based on the pretrained VGG16 neural network is used. The approach considered allows the image segmentation to be conducted. The results of the experiments have shown the possibility of a fairly accurate selection of the object of interest. The resulting binary masks make it possible to visually classify the aircraft in the image.

Feature selection of the armature winding broken coils in synchronous motor using genetic algorithm and mahalanobis distance

2012 ◽  
Vol 57 (3) ◽  
pp. 829-835 ◽  
Author(s):  
Z. Głowacz ◽  
J. Kozik
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Mahalanobis Distance ◽  
Distance Measure ◽  
Synchronous Motor ◽  
Medical Diagnostics ◽  
Motor Current ◽  
Feature Spaces ◽  
Multidimensional Feature Spaces ◽  
Selection Of

The paper describes a procedure for automatic selection of symptoms accompanying the break in the synchronous motor armature winding coils. This procedure, called the feature selection, leads to choosing from a full set of features describing the problem, such a subset that would allow the best distinguishing between healthy and damaged states. As the features the spectra components amplitudes of the motor current signals were used. The full spectra of current signals are considered as the multidimensional feature spaces and their subspaces are tested. Particular subspaces are chosen with the aid of genetic algorithm and their goodness is tested using Mahalanobis distance measure. The algorithm searches for such a subspaces for which this distance is the greatest. The algorithm is very efficient and, as it was confirmed by research, leads to good results. The proposed technique is successfully applied in many other fields of science and technology, including medical diagnostics.

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