Dense deformation field estimation for atlas-based segmentation of pathological MR brain images

Cuadra, M. Bach; Craene, Mathieu De; Duay, Valérie; Macq, Benoı̂t; Pollo, Claudio; Thiran, Jean-Philippe

doi:10.1016/j.cmpb.2006.08.003

Cited by 55 publications

(28 citation statements)

References 32 publications

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“…Atlases have broad application in medical image segmentation and registration and are often used in computer aided diagnosis to measure the shape of an object or detect the morphological differences between patient groups. Various techniques for atlas construction are developed for different human organs, like the heart [25,26,27] and especially the brain [28,29,30,31,32,33,34,35,36,37]. In this paper we use a statistical model as an atlas and an in-silico phantom model for evaluation.…”

Section: Introductionmentioning

confidence: 99%

Image registration and atlas-based segmentation of cardiac outflow velocity profiles

Kalinić

Lončarić

Čikeš

et al. 2012

Computer Methods and Programs in Biomedicine

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Section: Introductionmentioning

confidence: 99%

Image registration and atlas-based segmentation of cardiac outflow velocity profiles

Kalinić

Lončarić

Čikeš

et al. 2012

Computer Methods and Programs in Biomedicine

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“…[1] and [2 -4]. The role of segmentation is to subdivide the objects in an image; in case of medical image segmentation the aim is to: MR imaging is specifically used in brain imaging and thus lot of research work has been done particularly in the areas of MR brain image segmentation [5][6][7][8]. The main goal in brain MR segmentation is to segment gray matter, white matter and cerebrospinal fluid.…”

Section: Segmentation Of Medical Imagesmentioning

confidence: 99%

Medical Image Segmentation Based on Edge Detection Techniques

Salman

Ghafour

Hadi

2015

AIVP

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In this article a new combination of image segmentation techniques including K-means clustering, watershed transform, region merging and growing algorithm was proposed to segment computed tomography(CT) and magnetic resonance(MR) medical images.The first stage in the proposed system is "preprocessing" for required image enhancement, cropped, and convert the images into .mat or png ...etc image file formats then the image will be segmented using combination methods (clustering , region growing, and watershed, thresholding). Some initial over-segmentation appears due to the high sensitivity of the watershed algorithm to the gradient image intensity variations. Here, K-means and region growing with correct thresholding value are used to overcome that over segmentations. In our system the number of pixels of segmented area is calculated which is very important for medical image analysis for diseases or medicine effects on affected area of human body also displaying the edge map.The results show that using clustering method output to region growing as input image, gives accurate and very good results compare with watershed technique which depends on gradient of input image, the mean and the threshold values which are chosen manually. Also the results show that the manual selection of the threshold value for the watershed is not as good as automatically selecting, where data misses may be happen.

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“…The next step involved in calculating maxima and minima of the histogram and last output is represented in the form of brain MR. Researcher also used atlas -based method for automatic segmentation of MR image [40].…”

Section: Related Workmentioning

confidence: 99%

A Framework with OTSU’S Thresholding Method for Fruits and Vegetables Image Segmentation

Tripathi¹,

Maktedar²

2018

IJCA

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An accurate technique for segmentation of fruits and vegetables image is vital and major challenges in computer vision. Various segmentation techniques are available in digital image processing. In this paper, we introduce a framework for fruits and vegetables background subtraction employing Otsu"s algorithm. This method is widely used in various image segmentation applications. The Otsu"s method is useful in subtraction of background under the partial effect of occlusion, cropping, noisy and blurred images. Our proposed method was experimented by employing fruit and vegetable images acquired locally. Our experimental results confirm that, Otsu"s threshold based method is able to extract fruit and vegetable objects with good accuracy.

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Dense deformation field estimation for atlas-based segmentation of pathological MR brain images

Cited by 55 publications

References 32 publications

Image registration and atlas-based segmentation of cardiac outflow velocity profiles

Image registration and atlas-based segmentation of cardiac outflow velocity profiles

Medical Image Segmentation Based on Edge Detection Techniques

A Framework with OTSU’S Thresholding Method for Fruits and Vegetables Image Segmentation

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