Medical Image Mining Using Fuzzy Connectedness Image Segmentation

2016 ◽  
pp. 184-209
Author(s):  
Amol P. Bhagat ◽  
Mohammad Atique
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Image Feature ◽  
Image Mining ◽  
Imaging Data ◽  
Dicom Image ◽  
Fuzzy Connectedness ◽  
Geometric Moments ◽  
Novel Approach ◽  
Minimum Number

This chapter presents novel approach fuzzy connectedness image segmentation with geometric moments (FCISGM) for digital imaging and communications in medicine (DICOM) image mining. As most of the medical imaging data is exchanged in DICOM format, this chapter focuses on the various methodologies available for DICOM image feature extraction and mining. The comparison of existing medical image mining approaches with the proposed FCISGM approach is provided in this chapter. After carrying out exhaustive results it has been found that proposed FCISGM method gives more precise results and requires minimum number of computations compare to other medical image mining approaches resulting in improved relevant outcomes.

Medical Image Mining Using Fuzzy Connectedness Image Segmentation

Biometrics ◽  
2017 ◽  
pp. 233-258
Author(s):  
Amol P. Bhagat ◽  
Mohammad Atique
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Image Feature ◽  
Image Mining ◽  
Imaging Data ◽  
Dicom Image ◽  
Fuzzy Connectedness ◽  
Geometric Moments ◽  
Novel Approach ◽  
Minimum Number

This chapter presents novel approach fuzzy connectedness image segmentation with geometric moments (FCISGM) for digital imaging and communications in medicine (DICOM) image mining. As most of the medical imaging data is exchanged in DICOM format, this chapter focuses on the various methodologies available for DICOM image feature extraction and mining. The comparison of existing medical image mining approaches with the proposed FCISGM approach is provided in this chapter. After carrying out exhaustive results it has been found that proposed FCISGM method gives more precise results and requires minimum number of computations compare to other medical image mining approaches resulting in improved relevant outcomes.

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.

Medical Image Thresholding Using Genetic Algorithm and Fuzzy Membership Functions

2019 ◽  
Vol 8 (4) ◽  
pp. 39-59
Author(s):  
Shashwati Mishra ◽  
Mrutyunjaya Panda
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Hybrid Technique ◽  
Membership Functions ◽  
Novel Approach ◽  
Fitness Value ◽  
Sensitivity Specificity

Thresholding is one of the important steps in image analysis process and used extensively in different image processing techniques. Medical image segmentation plays a very important role in surgery planning, identification of tumours, diagnosis of organs, etc. In this article, a novel approach for medical image segmentation is proposed using a hybrid technique of genetic algorithm and fuzzy logic. Fuzzy logic can handle uncertain and imprecise information. Genetic algorithms help in global optimization, gives good results in noisy environments and supports multi-objective optimization. Gaussian, trapezoidal and triangular membership functions are used separately for calculating the entropy and finding the fitness value. CPU time, Root Mean Square Error, sensitivity, specificity, and accuracy are calculated using the three membership functions separately at threshold levels 2, 3, 4, 5, 7 and 9. MRI images are considered for applying the proposed method and the results are analysed. The experimental results obtained prove the effectiveness and efficiency of the proposed method.

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.

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