Medical Image Segmentation Using Genetic Algorithms

Maulik, Ujjwal

doi:10.1109/titb.2008.2007301

Cited by 176 publications

(57 citation statements)

References 50 publications

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“…In this section, the undecimated wavelet-based feature vector composed by the energies of the subband coefficients is used. Here we implement three-level wavelet decomposition on each square local area with size of 16 …”

Section: Wavelet Energy Featuresmentioning

confidence: 99%

“…As global optimization techniques, evolutionary algorithms (EAs) are likely to be good tools for image segmentation task. In the past two decades, EAs have been applied to image segmentation with promising results [9][10][11][12][13][14][15][16]. These algorithms exploited the metaphor of natural evolution in the context of image segmentation.…”

Section: Introductionmentioning

confidence: 99%

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Evolutionary-based Image Segmentation Methods

Jiao¹

2011

Image Segmentation

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Section: Wavelet Energy Featuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evolutionary-based Image Segmentation Methods

Jiao¹

2011

Image Segmentation

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“…In the recent literature [6], a number of approaches have been proposed for segmenting medical images effectively. However, most of the existing methods have used thresholding [1, 2, 9 10 and 11] based methods for segmentation.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Segmentation of Medical Images Using Fuzzy Bitplane Thresholding

Khan

Kannan

2014

Measurement Science Review

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The performance of assessment in medical image segmentation is highly correlated with the extraction of anatomic structures from them, and the major task is how to separate the regions of interests from the background and soft tissues successfully. This paper proposes a fuzzy logic based bitplane method to automatically segment the background of images and to locate the region of interest of medical images. This segmentation algorithm consists of three steps, namely identification, rule firing, and inference. In the first step, we begin by identifying the bitplanes that represent the lungs clearly. For this purpose, the intensity value of a pixel is separated into bitplanes. In the second step, the triple signum function assigns an optimum threshold based on the grayscale values for the anatomical structure present in the medical images. Fuzzy rules are formed based on the available bitplanes to form the membership table and are stored in a knowledge base. Finally, rules are fired to assign final segmentation values through the inference process. The proposed new metrics are used to measure the accuracy of the segmentation method. From the analysis, it is observed that the proposed metrics are more suitable for the estimation of segmentation accuracy. The results obtained from this work show that the proposed method performs segmentation effectively for the different classes of medical images.

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“…Genetic algorithms have already been used in medicine, mainly for image segmentation tasks, but also for feature selection and model optimization (13,14,15).…”

Section: Introductionmentioning

confidence: 99%

Final year medical students’ understanding of family medicine

Šter

Švab

Šter

2014

AMA

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Objective. e European Academy of Teachers in General Practice / Family Medicine (EURACT) has developed an educational agenda, the key document for teaching family medicine in Europe. e aim of our study was to nd out how nal year medical students at the beginning of their family medicine clerkship understand the discipline of family medicine. Methods. e attitudes toward family medicine were paraphrased and developed into a 164-item questionnaire, which was administered to 335 nal-year medical students at the beginning of their clerkship. Using combinatorial optimization with genetic algorithms we selected 30 items which yielded the highest Cronbach alpha reliability coe cient. Finally, we performed a factor analysis to nd which dimensions of family medicine were recognised by the students and compared them with the domains de ned in the EURACT de nition. Results. e 30-item questionnaire had a Cronbach alpha reliability coe cient of 0.919. e di erences between male and female students were not very signi cant (p=0.061). With the factor analysis we recognised seven factors, belonging to three out of six domains of the EURACT educational agenda: primary care management, personcenteredness and comprehensive approach. Conclusion. Final-year medical students at the beginning of their family medicine clerkship understand some of the dimensions of family medicine rather well, but they are not aware of some important competences of family doctors. ere is a necessity to teach students about speci c problem solving skills and the importance of balance between the health needs of an individual patient and the community.

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Medical Image Segmentation Using Genetic Algorithms

Cited by 176 publications

References 50 publications

Evolutionary-based Image Segmentation Methods

Evolutionary-based Image Segmentation Methods

Intelligent Segmentation of Medical Images Using Fuzzy Bitplane Thresholding

Final year medical students’ understanding of family medicine

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