Automatic localization methodology dedicated to brain tumors based on ICP matching by using axial MRI symmetry

Gargouri, Fatma; Hamida, Ines Njeh Ahmed Ben; Chtourou, K.

doi:10.1109/atsip.2014.6834608

Cited by 5 publications

(3 citation statements)

References 15 publications

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“…The manual image segmentation of the tumor is very time consuming process, and hence we should concentrate on automatic and accurate segmentation image. Automatic localization of brain tumor that can be imaged by Magnetic Resonance Image (MRI) modality can be explored; such methodology could be based on Iterative closest point (ICP) matching technique by using axial MRI symmetry [2].…”

Section: Segmentation Techniquesmentioning

confidence: 99%

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Tumor Detection and Measurement From MR Image Using Segmentation

2015

IJAERD

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Tumor cancer is one of the dangerous cancer diseases and can cause a large a mount of death every year all around the world. Normally tumor cells are of two different types, they are mass and malignant. Their detection is always requires special attention for the researchers in field of image processing. Image segmentation is one of most important and fundamental approaches of digital image processing and wid ely being used for the specific suppose of detecting and measuring the size of tumor in the human body. This paper represents the extensive Segmentation algorithm to tumor Detection and measuring from MR Image using segmentation. First, an input image is converted into fixed size then prepossed by Gaussion law pass filter which is improve the qulity of an image. Then the image is segmented using the thresholding method in which by selecting the threshold value an image is segmented. Then using region of interest the segmented image is cropped and then after measuring the size of segmentation image using pixels for the tretment planning. And also there is post processing for morphological operations. This algorithm is much simpler and less complex in the calculation. Keywords--Magnetic resonance Imaging, brain tumor, Area calculation, ROI, thresholding, Ga u ssi a n l a w p a ss fi l t er, ero si o n and d i l a t i o n .

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Section: Segmentation Techniquesmentioning

confidence: 99%

“…A Pixel becomes: White if its grey level is > T, Black if its grey level is<= T [12]. It will be transformed into image using "imshow" command as shown in below figure (2).…”

Section: A Thresholdingmentioning

confidence: 99%

Tumor Detection and Measurement From MR Image Using Segmentation

2015

IJAERD

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“…Despite of the fact that a wide range of segmentation approaches were introduced namely thresholding [4][5], region-based approaches [6][7], and clustering [8][9], these methods were reported to be complex when adopted for detecting the lesion structures. This is due to the fact that lesions usually have quite similar intensity with respect to some normal tissues.…”

Section: Introductionmentioning

confidence: 99%

A critical appraisal on wavelet based features from brain MR images for efficient characterization of ischemic stroke injuries

Karthik

Menaka

2016

ELCVIA

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Ischemic stroke is a severe neuro disorder typically characterized by a block inside a blood vessel supplying blood to the brain. It remains the third leading cause of death, after a heart attack and cancer. Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) were the major imaging modalities used in diagnosing this disorder. While the CT imaging can be used at the primary stage, MRI proves to be a necessary aid for progressive diagnostic planning in the treatment of stroke injuries. Developing a fully automatic approach for lesion segmentation is a challenging issue due to the complex nature of the lesion structures. This research aims at examining the properties of such complex structures. It analyses the characteristics of the normal brain tissues and abnormal lesion structures using a three-level wavelet decomposition procedure. Four different wavelet functions namely Daubechies, Symlet, Coiflet and De-Meyer were applied to the different datasets and the resulting observations were examined based on their feature statistics. Experiments indicate that the feature statistics obtained using the Daubechies and the De-Meyer wavelets were able to distinguish between normal brain tissues and abnormal lesion structures.

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