Fuzzy Spatial Growing for Glioblastoma Multiforme Segmentation on Brain Magnetic Resonance Imaging

Veloz, Alejandro; Chabert, Stéren; Salas, Rodrigo; Orellana, Antonio; Vielma, Juan

doi:10.1007/978-3-540-76725-1_89

Cited by 7 publications

(8 citation statements)

References 6 publications

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“…Some authors developed a way of defining seed points automatically in an efficient way. (Veloz et al, 2007) addressed this problem by the method known as Fuzzy Spatial Growing that was specifically proposed for the segmentation of Glioblastoma Multiforme (GBM) from T 1 -weighted MR images with contrast enhancement agent. Different GBM domains on MRI are captured as the initial step by using the FCM algorithm and mathematical morphology-based methods, whereby intensity patterns are established that reflect specific tumor behavior.…”

Section: Region Growingmentioning

confidence: 99%

Brain Tumors: How Can Images and Segmentation Techniques Help?

Veloz¹,

Orellana²,

Vielma³

et al. 2011

Diagnostic Techniques and Surgical Management of Brain Tumors

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Section: Region Growingmentioning

confidence: 99%

Brain Tumors: How Can Images and Segmentation Techniques Help?

Veloz¹,

Orellana²,

Vielma³

et al. 2011

Diagnostic Techniques and Surgical Management of Brain Tumors

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“…Image segmentation is an active field in medical imaging, which consists in extracting from the image one or more regions forming the area of interest. Various algorithms have been developed in the literature to perform brain tumor detection, including threshold-based methods [ 6 , 7 ], region-based methods [ 8 , 9 ], deformable methods [ 10 – 13 ], classification methods [ 14 , 15 ], and deep learning [ 16 – 18 ]. Deformable models are among the most popular methods used for brain tumor segmentation in MRI images.…”

Section: Introductionmentioning

confidence: 99%

Towards Reinforced Brain Tumor Segmentation on MRI Images Based on Temperature Changes on Pathologic Area

Bousselham

Bouattane

Youssfi

et al. 2019

International Journal of Biomedical Imaging

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Brain tumor segmentation is the process of separating the tumor from normal brain tissues; in clinical routine, it provides useful information for diagnosis and treatment planning. However, it is still a challenging task due to the irregular form and confusing boundaries of tumors. Tumor cells thermally represent a heat source; their temperature is high compared to normal brain cells. The main aim of the present paper is to demonstrate that thermal information of brain tumors can be used to reduce false positive and false negative results of segmentation performed in MRI images. Pennes bioheat equation was solved numerically using the finite difference method to simulate the temperature distribution in the brain; Gaussian noises of ±2% were added to the simulated temperatures. Canny edge detector was used to detect tumor contours from the calculated thermal map, as the calculated temperature showed a large gradient in tumor contours. The proposed method is compared to Chan–Vese based level set segmentation method applied to T1 contrast-enhanced and Flair MRI images of brains containing tumors with ground truth. The method is tested in four different phantom patients by considering different tumor volumes and locations and 50 synthetic patients taken from BRATS 2012 and BRATS 2013. The obtained results in all patients showed significant improvement using the proposed method compared to segmentation by level set method with an average of 0.8% of the tumor area and 2.48% of healthy tissue was differentiated using thermal images only. We conclude that tumor contours delineation based on tumor temperature changes can be exploited to reinforce and enhance segmentation algorithms in MRI diagnostic.

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“…Classification based tumor detection algorithms are widely used in brain tumor detection applications, since multi-modal data sets can easily be handled using these methods. These methods are constrained to the supervised [34,39] or unsupervised [10,50]. Menze et al [34] combined a healthy brain atlas with a tumorous brain atlas to segment brain tumors using a generative probabilistic model and spatial regularization.…”

Section: Introductionmentioning

confidence: 99%

“…Fletcher-Heath et al [10] combined fuzzy clustering and integrated domain knowledge to improve the tumor segmentation applied on T1-, T2-, and PD-weighted images. Veloz et al [50] incorporated the intricate nature of the tumor in MR images into fuzzy cmeans and formulated a fuzzy region growing with an automatic initialization of the seed points. In [36], an algorithm is proposed based on spatial accuracy-weighted hidden Markov random field and expectation maximization approach for both automated tumor and enhanced-tumor segmentation.…”

Section: Introductionmentioning

confidence: 99%

SoBT-RFW: Rough-Fuzzy Computing and Wavelet Analysis Based Automatic Brain Tumor Detection Method from MR Images*

Maji

Roy

2015

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One of the important problems in medical diagnosis is the segmentation and detection of brain tumor in MR images. The accurate estimation of brain tumor size is important for treatment planning and therapy evaluation. In this regard, this paper presents a new method, termed as SoBT-RFW, for segmentation of brain tumor from MR images. It integrates judiciously the merits of rough-fuzzy computing and multiresolution image analysis technique. The proposed method starts with a simple skull stripping algorithm to remove non-cerebral tissues such as skull, scalp, and dura from brain MR images. To extract the scale-space feature vector for each pixel of brain region, the dyadic wavelet analysis is used, while an unsupervised feature selection method, based on maximum relevance-maximum significance criterion, is used to select relevant and significant textural features for brain tumor segmentation. To address the uncertainty problem of brain MR image segmentation, the proposed SoBT-RFW method uses the robust rough-fuzzy c-means algorithm. After the segmentation process, asymmetricity is analyzed by using the Zernike moments of each of the tissues segmented in the brain to identify the tumor. Finally, the location of the tumor is searched by a region growing algorithm based on the concept of rough sets. The performance of the proposed SoBT-RFW method, along with a comparison with related approaches, is demonstrated on a set of synthetic and real brain MR images using standard validity indices.

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Fuzzy Spatial Growing for Glioblastoma Multiforme Segmentation on Brain Magnetic Resonance Imaging

Cited by 7 publications

References 6 publications

Brain Tumors: How Can Images and Segmentation Techniques Help?

Brain Tumors: How Can Images and Segmentation Techniques Help?

Towards Reinforced Brain Tumor Segmentation on MRI Images Based on Temperature Changes on Pathologic Area

SoBT-RFW: Rough-Fuzzy Computing and Wavelet Analysis Based Automatic Brain Tumor Detection Method from MR Images*

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