Medical Image Segmentation: Methods and Software

Withey, Daniel; Koles, Z.J.

doi:10.1109/nfsi-icfbi.2007.4387709

Cited by 121 publications

(98 citation statements)

References 34 publications

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“…For the other reviews, readers are referred to the literature (Duncan & Ayache, 2002;Petitjean et al, 2011). Withey and Koles (2007) classified the medical image identification approaches in literature into three generations, while Ma et al (2008) classified the methods in three categories that can be fitted to the three generations model of Witney and Koles.…”

Section: Evolution Of the Methods Along The Last Decadesmentioning

confidence: 99%

“…They also have finite pixel size and, for this reason, are subject to partial volume averaging effect where individual pixel volumes contain a mixture of tissue classes so that the intensity of a pixel in the image may not be consistent with any one structure. The intensity level of a single tissue class can vary over the extent of the image, and internal materials (for instance surgical clips) may cause distortion in the imaged organ (Rani et al, 2011;Withey & Koles, 2007). Some of these effects are depicted in Figure 1.…”

Section: Automatic Identification In Medical Imagesmentioning

confidence: 99%

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Identification of Structures in Medical Images

Rebelo¹,

Furuie²,

Moura³

et al. 2011

Medical Imaging

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Section: Evolution Of the Methods Along The Last Decadesmentioning

confidence: 99%

Section: Automatic Identification In Medical Imagesmentioning

confidence: 99%

Identification of Structures in Medical Images

Rebelo¹,

Furuie²,

Moura³

et al. 2011

Medical Imaging

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“…FCM algorithm is commonly used in the image segmentation clustering method (Hashmi et al, 2013;MacQueen, 1967;Yancang et al, 2010;Sasa et al, 2009;Withey and Koles, 2008). FCM algorithm was selected as an alternative for the typical K-means algorithm to allow each element in the dataset to belong to more than one cluster.…”

Section: Ajasmentioning

confidence: 99%

A Hybrid Firefly Algorithm With Fuzzy-C Mean Algorithm for Mri Brain Segmentation

Alsmadi¹

2014

American Journal of Applied Sciences

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Image processing is one of the essential tasks to extract suspicious region and robust features from the Magnetic Resonance Imaging (MRI). A numbers of the segmentation algorithms were developed in order to satisfy and increasing the accuracy of brain tumor detection. In the medical image processing brain image segmentation is considered as a complex and challenging part. Fuzzy c-means is unsupervised method that has been implemented for clustering of the MRI and different purposes such as recognition of the pattern of interest and image segmentation. However; fuzzy c-means algorithm still suffers many drawbacks, such as low convergence rate, getting stuck in the local minima and vulnerable to initialization sensitivity. Firefly algorithm is a new population-based optimization method that has been used successfully for solving many complex problems. This paper proposed a new dynamic and intelligent clustering method for brain tumor segmentation using the hybridization of Firefly Algorithm (FA) with Fuzzy C-Means algorithm (FCM). In order to automatically segment MRI brain images and improve the capability of the FCM to automatically elicit the proper number and location of cluster centres and the number of pixels in each cluster in the abnormal (multiple sclerosis lesions) MRI images. The experimental results proved the effectiveness of the proposed FAFCM in enhancing the performance of the traditional FCM clustering. Moreover; the superiority of the FAFCM with other state-of-the-art segmentation methods is shown qualitatively and quantitatively. Conclusion: A novel efficient and reliable clustering algorithm presented in this work, which is called FAFCM based on the hybridization of the firefly algorithm with fuzzy c-mean clustering algorithm. Automatically; the hybridized algorithm has the capability to cluster and segment MRI brain images.

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“…The inherent features of medical images are that they have a high-dimensionality, have ill-defined edges, and are corrupted by noise [12,13]. While, this paper does not address the problem of noise reduction, we focus on the high dimensionality of these images (feature content) and illdefined edges.…”

Section: Hcmentioning

confidence: 99%

Region Quad-Tree Decomposition Based Edge Detection for Medical Images

Dua¹

2010

TOMINFOJ

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Edge detection in medical images has generated significant interest in the medical informatics community, especially in recent years. With the advent of imaging technology in biomedical and clinical domains, the growth in medical digital images has exceeded our capacity to analyze and store them for efficient representation and retrieval, especially for data mining applications. Medical decision support applications frequently demand the ability to identify and locate sharp discontinuities in an image for feature extraction and interpretation of image content, which can then be exploited for decision support analysis. However, due to the inherent high dimensional nature of the image content and the presence of ill-defined edges, edge detection using classical procedures is difficult, if not impossible, for sensitive and specific medical informatics-based discovery. In this paper, we propose a new edge detection technique based on the regional recursive hierarchical decomposition using quadtree and post-filtration of edges using a finite difference operator. We show that in medical images of common origin, focal and/or penumbral blurred edges can be characterized by an estimable intensity gradient. This gradient can further be used for dismissing false alarms. A detailed validation and comparison with related works on diabetic retinopathy images and CT scan images show that the proposed approach is efficient and accurate.

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Medical Image Segmentation: Methods and Software

Cited by 121 publications

References 34 publications

Identification of Structures in Medical Images

Identification of Structures in Medical Images

A Hybrid Firefly Algorithm With Fuzzy-C Mean Algorithm for Mri Brain Segmentation

Region Quad-Tree Decomposition Based Edge Detection for Medical Images

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