GPU-based segmentation of cervical vertebra in X-Ray images

Mahmoudi, Sidi Ahmed; Lecron, Fabian; Manneback, Pierre; Benjelloun, M.; Mahmoudi, Saïd

doi:10.1109/clusterwksp.2010.5613102

Cited by 27 publications

(29 citation statements)

References 20 publications

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“…During recent years, many reports on CUDA implementations of a large variety of segmentation algorithms have been published. Some examples are GPU acceleration of graph cuts (Vineet and Narayanan, 2008), expectation maximization and k-means clustering for analysis of histopathological images of neuroblastoma (Ruiz et al, 2008), registration-based segmentation of MRI volumes (Han et al, 2009), liver segmentation based on Markov random fields (Walters et al, 2009), shape models for segmentation of vertebra in X-ray images (Mahmoudi et al, 2010), random walks (Collins et al, 2012), fuzzy connected image segmentation of CT and MRI volumes (Zhuge et al, 2011) and a hybrid approach to segmentation of vessel laminae from confocal microscope images (Narayanaswamy et al, 2010).…”

Section: Image Segmentationmentioning

confidence: 99%

Medical image processing on the GPU – Past, present and future

Eklund

Dufort

Forsberg

et al. 2013

Medical Image Analysis

348

198

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Graphics processing units (GPUs) are used today in a wide range of applications, mainly because they can dramatically accelerate parallel computing, are affordable and energy efficient. In the field of medical imaging, GPUs are in some cases crucial for enabling practical use of computationally demanding algorithms. This review presents the past and present work on GPU accelerated medical image processing, and is meant to serve as an overview and introduction to existing GPU implementations. The review covers GPU acceleration of basic image processing operations (filtering, interpolation, histogram estimation and distance transforms), the most commonly used algorithms in medical imaging (image registration, image segmentation and image denoising) and algorithms that are specific to individual modalities (CT, PET, SPECT, MRI, fMRI, DTI, ultrasound, optical imaging and microscopy). The review ends by highlighting some future possibilities and challenges.

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Section: Image Segmentationmentioning

confidence: 99%

Medical image processing on the GPU – Past, present and future

Eklund

Dufort

Forsberg

et al. 2013

Medical Image Analysis

348

198

View full text Add to dashboard Cite

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“…An improved Mahalanobis distance-based search method has been introduced in [13]. This method has been used for vertebrae segmentation in [3,4]. …”

Section: Asm Searchmentioning

confidence: 99%

“…This method involves a training phase and an optimization step to find the amount of displacement needed to converge the mean shape on the actual object boundary. The method has been shown to work well on cervical vertebra X-ray images in [3,4]. In [15], a conventional binary classifier and a boosted regression predictor has been compared and used to improve the performance of ASM segmentation during image search phase.…”

Section: Introductionmentioning

confidence: 99%

“…Due to the clinical importance of vertebrae segmentation, there is a large body of research in the literature [3][4][5][6][7][8][9][10][11]. Based on this literature, arguably the most successful segmentation method is the statistical shape model (SSM).…”

Section: Introductionmentioning

confidence: 99%

“…Unlike the Mahalanobis distance-based method used in [3,4,13,14], this method predicts the displacement directly without a need of a sliding window-based search technique. Our method uses a multi-class forest in contrast with the binary classification method used in [15].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Improving an Active Shape Model with Random Classification Forest for Segmentation of Cervical Vertebrae

Arif

Gundry

Knapp

et al. 2016

Lecture Notes in Computer Science

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Abstract. X-ray is a common modality for diagnosing cervical vertebrae injuries. Many injuries are missed by emergency physicians which later causes life threatening complications. Computer aided analysis of X-ray images has the potential to detect missed injuries. Segmentation of the vertebrae is a crucial step towards automatic injury detection system. Active shape model (ASM) is one of the most successful and popular method for vertebrae segmentation. In this work, we propose a new ASM search method based on random classification forest and a kernel density estimation-based prediction technique. The proposed method have been tested on a dataset of 90 emergency room X-ray images containing 450 vertebrae and outperformed the classical Mahalanobis distancebased ASM search and also the regression forest-based method.

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Towards a smart selection of resources in the cloud for low‐energy multimedia processing

Mahmoudi

Belarbi

Mahmoudi

et al. 2017

Concurrency and Computation

Self Cite

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Summary Nowadays, image and video processing applications have become widely used in many domains related to computer vision. Indeed, they can come from cameras, smartphones, social networks, or from medical devices. Generally, these images and videos are used for illustrating people or objects (cars, trains, planes, etc) in many situations such as airports, train stations, public areas, sport events, and hospitals. Thus, image and video processing algorithms have got increasing importance, they are required from various computer vision applications such as motion tracking, real time event detection, database (images and videos) indexation, and medical computer‐aided diagnosis methods. The main inconvenient of image and video processing applications is the high intensity of computation and the complex configuration and installation of the related materials and libraries. In this paper, we propose a new framework that allows users to select in a smart and efficient way the computing units (CPU or/and GPU) in a cloud‐based platform, in case of processing one image (or one video in real time) or many images (or videos). This framework enables to affect the local or remote computing units for calculation after analyzing the type of media and the algorithm complexity. The framework disposes of a set of selected CPU and GPU‐based computer vision methods, such as image denoising, histogram computation, features descriptors (SIFT, SURF), points of interest extraction, edges detection, silhouette extraction, and sparse and dense optical flow estimation. These primitive functions are exploited in various applications such as medical image segmentation, videos indexation, real time motion analysis, and left ventricle segmentation and tracking from 2D echocardiography. Experimental results showed a global speedup ranging from 5× to 273×(compared to CPU versions) as result of the application of our framework for the above‐mentioned methods. In addition to these performances, the parallel and heterogeneous implementations offered lower power consumption as result of the fast treatment.

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GPU-based segmentation of cervical vertebra in X-Ray images

Cited by 27 publications

References 20 publications

Medical image processing on the GPU – Past, present and future

Medical image processing on the GPU – Past, present and future

Improving an Active Shape Model with Random Classification Forest for Segmentation of Cervical Vertebrae

Towards a smart selection of resources in the cloud for low‐energy multimedia processing

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