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Intelligent Segmentation of Medical Images Using Fuzzy Bitplane Thresholding
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| Zeitschriftentitel: | Measurement Science Review |
|---|---|
| Personen und Körperschaften: | , |
| In: | Measurement Science Review, 14, 2014, 2, S. 94-101 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
Walter de Gruyter GmbH
|
| Schlagwörter: |
| author_facet |
Khan, Z. Faizal Kannan, A. Khan, Z. Faizal Kannan, A. |
|---|---|
| author |
Khan, Z. Faizal Kannan, A. |
| spellingShingle |
Khan, Z. Faizal Kannan, A. Measurement Science Review Intelligent Segmentation of Medical Images Using Fuzzy Bitplane Thresholding Instrumentation Biomedical Engineering Control and Systems Engineering |
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khan, z. faizal |
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Khan, Z. Faizal Kannan, A. 1335-8871 Walter de Gruyter GmbH Instrumentation Biomedical Engineering Control and Systems Engineering http://dx.doi.org/10.2478/msr-2014-0013 <jats:title>Abstract</jats:title> <jats:p> 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.</jats:p> Intelligent Segmentation of Medical Images Using Fuzzy Bitplane Thresholding Measurement Science Review |
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| title |
Intelligent Segmentation of Medical Images Using Fuzzy Bitplane Thresholding |
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Intelligent Segmentation of Medical Images Using Fuzzy Bitplane Thresholding |
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Intelligent Segmentation of Medical Images Using Fuzzy Bitplane Thresholding |
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Intelligent Segmentation of Medical Images Using Fuzzy Bitplane Thresholding |
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Intelligent Segmentation of Medical Images Using Fuzzy Bitplane Thresholding |
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Intelligent Segmentation of Medical Images Using Fuzzy Bitplane Thresholding |
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intelligent segmentation of medical images using fuzzy bitplane thresholding |
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Instrumentation Biomedical Engineering Control and Systems Engineering |
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http://dx.doi.org/10.2478/msr-2014-0013 |
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2014 |
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94-101 |
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<jats:title>Abstract</jats:title>
<jats:p> 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.</jats:p> |
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| description | <jats:title>Abstract</jats:title> <jats:p> 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.</jats:p> |
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| spelling | Khan, Z. Faizal Kannan, A. 1335-8871 Walter de Gruyter GmbH Instrumentation Biomedical Engineering Control and Systems Engineering http://dx.doi.org/10.2478/msr-2014-0013 <jats:title>Abstract</jats:title> <jats:p> 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.</jats:p> Intelligent Segmentation of Medical Images Using Fuzzy Bitplane Thresholding Measurement Science Review |
| spellingShingle | Khan, Z. Faizal, Kannan, A., Measurement Science Review, Intelligent Segmentation of Medical Images Using Fuzzy Bitplane Thresholding, Instrumentation, Biomedical Engineering, Control and Systems Engineering |
| title | Intelligent Segmentation of Medical Images Using Fuzzy Bitplane Thresholding |
| title_full | Intelligent Segmentation of Medical Images Using Fuzzy Bitplane Thresholding |
| title_fullStr | Intelligent Segmentation of Medical Images Using Fuzzy Bitplane Thresholding |
| title_full_unstemmed | Intelligent Segmentation of Medical Images Using Fuzzy Bitplane Thresholding |
| title_short | Intelligent Segmentation of Medical Images Using Fuzzy Bitplane Thresholding |
| title_sort | intelligent segmentation of medical images using fuzzy bitplane thresholding |
| title_unstemmed | Intelligent Segmentation of Medical Images Using Fuzzy Bitplane Thresholding |
| topic | Instrumentation, Biomedical Engineering, Control and Systems Engineering |
| url | http://dx.doi.org/10.2478/msr-2014-0013 |