3D Segmentation with an Application of Level Set-Method using MRI Volumes for Image Guided Surgery

Bosnjak, Antonio; Montilla, G.; Villegas, Ricardo Astudillo; Jara, I.

doi:10.1109/iembs.2007.4353529

Cited by 9 publications

(5 citation statements)

References 9 publications

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“…The image frames are stacked together to form a 3D image volume [6]. In this way, the path of a moving fluorescent cell forms a "tube" in the image volume.…”

Section: Methodsmentioning

confidence: 99%

Reconstruction of missing cells in fluorescent microscopy

Leung

Wan

2012

2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Fluorescent microscopy is one of the several types of imaging techniques used by biologists to study cell activities. One challenge of tracking cells from fluorescence microscopy is that cells in fluorescent images frequently disappear and reappear. The situation is further complicated by cell divisions, which also occur frequently in an image sequence. In this paper, we apply a level set method to reconstruct cells that disappear in an image sequence and in particular, cells that are undergoing cell division. The image frames are stacked together to form a 3D image volume. The disappearance of a cell leads to a broken cell path. We reconstruct the incomplete cell paths by a level set segmentation of the 3D image volume. If the disappearance happens during cell division, the level set method segments the visible cell paths before and after cell division, and then joins them together by extending the cell paths into the missing gap. We also propose a simple and cost-efficient method similar to inpainting techniques to capture the cell appearance when it disappears by making use of the level set function obtained from the segmentation. The idea is that the intensities of a visible cell on a level set contour are copied to the corresponding contours of a disappeared cell. We will present results for reconstruction of cells undergoing cell division for C2C12 cells in fluorescent images to illustrate the effectiveness of our method.

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“…The image frames are stacked together to form a 3D image volume [6]. In this way, the path of a moving fluorescent cell forms a "tube" in the image volume.…”

Section: Methodsmentioning

confidence: 99%

Reconstruction of missing cells in fluorescent microscopy

Leung

Wan

2012

2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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“…However, IGS is yet to reach the expected level of surgical interventional assistance anticipated by medical professionals, computer engineers, and scientist due to lack of reliable, robust, real-time, and yet cheaper software and hardware that are needed in IGS [2], [11]. As argued by [12], [13], [14], [15], [16], [17], [18], [19], and [20] that IGS is still being confounded by many setbacks as mentioned above, and having studied the current IGS paradigm and found it to have contributed immensely to the relative inefficiency of IGS procedure due to it cumbersomeness and tediousness of the mathematics of image segmentation, registration, and visualization. This paper proposes a more computational effective IGS model by redesigning the whole IGS traditional system spanning image acquisition post-op.…”

Section: Introductionmentioning

confidence: 99%

Towards a computational effective paradigm for Image Guided Surgery (IGS)

Aboaba

Hameed

Khalifa

et al. 2011

2011 4th International Conference on Mechatronics (ICOM)

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The adoption of minimally invasive method in surgical intervention occasioned by Image Guided Surgery (IGS) / Computer Aided Surgery (CAS) has further enhanced the acceptability of IGS into more areas of surgery such as spinal cord surgery, teleguided surgery, robotic controlled surgery, neuro surgery, and even orthopedic surgery and emergency situations which have hitherto least benefitted in IGS for certain obvious impediments. These achievements notwithstanding, IGS is still tied to the traditional procedure which from the perspective of a computer engineer makes it time consuming and irresponsive to patients' needs. In this paper, a paradigm for a computational effective IGS is presented focusing on two aspects of the current IGS model which are believed to be the bottleneck for improvement viz: pre-operative planning, and inter-operative guidance. The proposed scheme is promising in that operation involving IGS would be quicker and safer.

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“…3D medical imaging models structures of the human body. This is important in many fields as image guided surgery, assessment of the quality of bones, and so forth; see, for example, [1][2][3]. The complex geometric structure of some materials (composites, foams, etc.)…”

Section: Introductionmentioning

confidence: 99%

A Doubly Adaptive Algorithm for Edge Detection in 3D Images

Lantarón¹,

López²,

Hitos³

2016

Mathematical Problems in Engineering

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This paper proposes a new algorithm (DA3DED) for edge detection in 3D images. DA3DED is doubly adaptive because it is based on the adaptive algorithm EDAS-1 for detecting edges in functions of one variable and a second adaptive procedure based on the concept of projective complexity of a 3D image. DA3DED has been tested on 3D images that modelize real problems (composites and fractures). It has been much faster than the 1D edge detection algorithm for 3D images derived from EDAS-1.

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3D Segmentation with an Application of Level Set-Method using MRI Volumes for Image Guided Surgery

Cited by 9 publications

References 9 publications

Reconstruction of missing cells in fluorescent microscopy

Reconstruction of missing cells in fluorescent microscopy

Towards a computational effective paradigm for Image Guided Surgery (IGS)

A Doubly Adaptive Algorithm for Edge Detection in 3D Images

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