<abstract xmlns="http://www.w3.org/1999/xhtml"><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.</p></abstract>

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.

Intelligent Segmentation of Medical Images Using Fuzzy Bitplane Thresholding

Anna University Chennai, Chennai-600025, India.

Department of Information Science and Technology, Anna University Chennai, Chennai-600025, India.

Measurement Science Review

This article is distributed under the terms of the Creative Commons Attribution Non-Commercial License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

{"article-title":"Intelligent Segmentation of Medical Images Using Fuzzy Bitplane Thresholding"}

The performance of assessment in medical image segmentation is highly correlated with the extraction of anatomic structures from them, and the major task is...

Intelligent Segmentation of Medical Images Using Fuzzy Bitplane Thresholding

Data publikacji: 08 maj 2014

Zakres stron: 94 - 101

Otrzymano: 22 lip 2013

Przyjęty: 11 kwi 2014

DOI: https://doi.org/10.2478/msr-2014-0013

Słowa kluczowe
fuzzy logic, bitplane method, computed tomography (CT), segmentation, unsupervised method

© by Z. Faizal Khan

This article is distributed under the terms of the Creative Commons Attribution Non-Commercial License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Intelligent Segmentation of Medical Images Using Fuzzy Bitplane Thresholding

Data publikacji: 08 maj 2014

Zakres stron: 94 - 101

Otrzymano: 22 lip 2013

Przyjęty: 11 kwi 2014

DOI: https://doi.org/10.2478/msr-2014-0013

Słowa kluczowefuzzy logic, bitplane method, computed tomography (CT), segmentation, unsupervised method

© by Z. Faizal Khan

This article is distributed under the terms of the Creative Commons Attribution Non-Commercial License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Słowa kluczowe
fuzzy logic, bitplane method, computed tomography (CT), segmentation, unsupervised method