Medical Image Segmentation Algorithm Based on Granular Computing

2012 ◽  
Vol 532-533 ◽  
pp. 1578-1582
Author(s):  
Fang Wang ◽  
Juan Juan Ruan ◽  
Gang Xie
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Granular Computing ◽  
Research Direction ◽  
Medical Image Segmentation ◽  
Image Feature ◽  
Segmentation Method ◽  
Single Image ◽  
Quotient Spaces ◽  
Image Segmentation Algorithm

Granular Computing theory is a interesting research direction in artificial intelligence field. In this paper, granular computing theory is applied to medical image segmentation. Granularity thinking in image segmentation is expounded, and a novel medical image segmentation method is proposed. Firstly, we construct different granularities according to different features that the image contained, secondly, do the attributes combination to the obtained quotient spaces according to the quotient space granularity synthesis principle, and then complete the image segmentation. Compared with the methods adopting single image feature, this method may fully use the image information in a more effective way and may obtain better segmentation effects.

scholarly journals Medical Image Segmentation Algorithm Based on Feedback Mechanism CNN

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Feng-Ping An ◽  
Zhi-Wen Liu
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Convolutional Neural Network ◽  
Medical Image ◽  
Rapid Development ◽  
Feedback Mechanism ◽  
Medical Image Segmentation ◽  
Segmentation Algorithm ◽  
Segmentation Method ◽  
Image Segmentation Algorithm

With the development of computer vision and image segmentation technology, medical image segmentation and recognition technology has become an important part of computer-aided diagnosis. The traditional image segmentation method relies on artificial means to extract and select information such as edges, colors, and textures in the image. It not only consumes considerable energy resources and people’s time but also requires certain expertise to obtain useful feature information, which no longer meets the practical application requirements of medical image segmentation and recognition. As an efficient image segmentation method, convolutional neural networks (CNNs) have been widely promoted and applied in the field of medical image segmentation. However, CNNs that rely on simple feedforward methods have not met the actual needs of the rapid development of the medical field. Thus, this paper is inspired by the feedback mechanism of the human visual cortex, and an effective feedback mechanism calculation model and operation framework is proposed, and the feedback optimization problem is presented. A new feedback convolutional neural network algorithm based on neuron screening and neuron visual information recovery is constructed. So, a medical image segmentation algorithm based on a feedback mechanism convolutional neural network is proposed. The basic idea is as follows: The model for obtaining an initial region with the segmented medical image classifies the pixel block samples in the segmented image. Then, the initial results are optimized by threshold segmentation and morphological methods to obtain accurate medical image segmentation results. Experiments show that the proposed segmentation method has not only high segmentation accuracy but also extremely high adaptive segmentation ability for various medical images. The research in this paper provides a new perspective for medical image segmentation research. It is a new attempt to explore more advanced intelligent medical image segmentation methods. It also provides technical approaches and methods for further development and improvement of adaptive medical image segmentation technology.

Interactive Medical Image Segmentation Method with only Positive Label Examples

2014 ◽  
Vol 989-994 ◽  
pp. 1088-1092
Author(s):  
Chen Guang Zhang ◽  
Yan Zhang ◽  
Xia Huan Zhang
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Segmentation Method ◽  
High Quality ◽  
Neighbor Graph ◽  
Foreground Region ◽  
Training Examples ◽  
Positive Label ◽  
Negative Training

In this paper, a novel interactive medical image segmentation method called SMOPL is proposed. This method only needs marking some pixels on foreground region for segmentation. To do this, SMOPL characterize the inherent correlations among foreground and background pixels as Hilbert-Schmidt independence. By maximizing the independence and minimizing the smoothness of labels on instance neighbor graph simultaneously, SMOPL gets the sufficiently smooth confidences of both positive and negative classes in absence of negative training examples. Then a image segmentation can be obtained by assigning each pixel to the label for which the greatest confidence is calculated. Experiments on real-world medical images show that SMOPL is robust to get a high-quality segmentation with only positive label examples.

SU-GG-I-95: Simpler and Faster Watershed Medical Image Segmentation Algorithm

2008 ◽  
Vol 35 (6Part4) ◽  
pp. 2663-2663
Author(s):  
O Kum ◽  
H Lee ◽  
J Kim ◽  
T Song ◽  
K Park ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Segmentation Algorithm ◽  
Image Segmentation Algorithm

scholarly journals Medical Image Segmentation with Adjustable Computational Complexity Using Data Density Functionals

2019 ◽  
Vol 9 (8) ◽  
pp. 1718
Author(s):  
Chien-Chang Chen ◽  
Meng-Yuan Tsai ◽  
Ming-Ze Kao ◽  
Henry Horng-Shing Lu
Keyword(s):  
Image Segmentation ◽  
Computational Complexity ◽  
Image Compression ◽  
Medical Image ◽  
Density Functional ◽  
Medical Image Segmentation ◽  
Image Feature ◽  
Density Functionals ◽  
Potential Kernel ◽  
Data Density

Techniques of automatic medical image segmentation are the most important methods for clinical investigation, anatomic research, and modern medicine. Various image structures constructed from imaging apparatus achieve a diversity of medical applications. However, the diversified structures are also a burden of contemporary techniques. Performing an image segmentation with a tremendously small size (<25 pixels by 25 pixels) or tremendously large size (>1024 pixels by 1024 pixels) becomes a challenge in perspectives of both technical feasibility and theoretical development. Noise and pixel pollution caused by the imaging apparatus even aggravate the difficulty of image segmentation. To simultaneously overcome the mentioned predicaments, we propose a new method of medical image segmentation with adjustable computational complexity by introducing data density functionals. Under this theoretical framework, several kernels can be assigned to conquer specific predicaments. A square-root potential kernel is used to smoothen the featured components of employed images, while a Yukawa potential kernel is applied to enhance local featured properties. Besides, the characteristic of global density functional estimation also allows image compression without losing the main image feature structures. Experiments on image segmentation showed successful results with various compression ratios. The computational complexity was significantly improved, and the score of accuracy estimated by the Jaccard index had a great outcome. Moreover, noise and regions of light pollution were mostly filtered out in the procedure of image compression.

scholarly journals An Improved FCM Medical Image Segmentation Algorithm Based on MMTD

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Ningning Zhou ◽  
Tingting Yang ◽  
Shaobai Zhang
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Spatial Information ◽  
Clustering Algorithms ◽  
Medical Image Segmentation ◽  
Segmentation Algorithm ◽  
Fuzzy Information ◽  
Spatial Features ◽  
Image Segmentation Algorithm ◽  
Truth Degree

Image segmentation plays an important role in medical image processing. Fuzzy c-means (FCM) is one of the popular clustering algorithms for medical image segmentation. But FCM is highly vulnerable to noise due to not considering the spatial information in image segmentation. This paper introduces medium mathematics system which is employed to process fuzzy information for image segmentation. It establishes the medium similarity measure based on the measure of medium truth degree (MMTD) and uses the correlation of the pixel and its neighbors to define the medium membership function. An improved FCM medical image segmentation algorithm based on MMTD which takes some spatial features into account is proposed in this paper. The experimental results show that the proposed algorithm is more antinoise than the standard FCM, with more certainty and less fuzziness. This will lead to its practicable and effective applications in medical image segmentation.

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