Semiautomatic computer-aided classification of degenerative lumbar spine disease in magnetic resonance imaging

Ruiz-España, Silvia; Arana, Estanislao; Moratal, David

doi:10.1016/j.compbiomed.2015.04.028

Cited by 41 publications

(36 citation statements)

References 43 publications

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“…A semiautomatic classification of spine disorders namely disc herniation, degeneration and spinal stenosis is presented in [4]. For the case of disc degeneration classification, the authors have captured MRI response in T2 weighted images.…”

Section: Literaturementioning

confidence: 99%

Fuzzy Clustering Based Segmentation of Vertebrae in T1-Weighted Spinal MR Images

Athertya¹,

Kumar²

2016

IJFLS

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Image segmentation in the medical domain is a challenging field owing to poor resolution and limited contrast. The predominantly used conventional segmentation techniques and the thresholding methods suffer from limitations because of heavy dependence on user interactions. Uncertainties prevalent in an image cannot be captured by these techniques. The performance further deteriorates when the images are corrupted by noise, outliers and other artifacts. The objective of this paper is to develop an effective robust fuzzy C-means clustering for segmenting vertebral body from magnetic resonance image owing to its unsupervised form of learning. The motivation for this work is detection of spine geometry and proper localisation and labelling will enhance the diagnostic output of a physician. The method is compared with Otsu thresholding and K-means clustering to illustrate the robustness.The reference standard for validation was the annotated images from the radiologist, and the Dice coefficient and Hausdorff distance measures were used to evaluate the segmentation.

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Section: Literaturementioning

confidence: 99%

Fuzzy Clustering Based Segmentation of Vertebrae in T1-Weighted Spinal MR Images

Athertya¹,

Kumar²

2016

IJFLS

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“…Currently, to assist radiologists in the diagnosis task of different abnormalities, computer‐aided diagnosis systems are employed, becoming a part of the routine clinical work . Hence, its demand over the past years has increased, becoming an important research topic in medical imaging and also in diagnostic radiology . However, due to the high number of pathologies affecting the spine, the segmentation of this structure is essential for many research and clinical studies as it is capable of facilitating disease diagnosis, follow‐up assessment, treatment, and statistical analysis.…”

Section: Introductionmentioning

confidence: 99%

“…6 Hence, its demand over the past years has increased, becoming an important research topic in medical imaging and also in diagnostic radiology. 6,7 However, due to the high number of pathologies affecting the spine, the segmentation of this structure is essential for many research and clinical studies as it is capable of facilitating disease diagnosis, follow-up assessment, treatment, and statistical analysis. Therefore, prior to any diagnosis of spinal disorders or in the study of the disorder, a precise detection and segmentation of the vertebrae are the first crucial steps.…”

Section: Introductionmentioning

confidence: 99%

Automatic segmentation of the spine by means of a probabilistic atlas with a special focus on ribs suppression

et al. 2017

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“…Nowadays, spinal imaging studies are increasing worldwide [6], and computer-aided diagnosis (CAD) is beginning to be a part of the routine clinical work, being applied in the detection and differential diagnosis of abnormalities. Hence, its demand over the past decade has increased as a way to assist radiologists in the imaging diagnosis of back pain [7,8]. Automatic reliable methods to quantify and classify spinal disorders, and an early detection of metastatic disease to prevent complications, are an unmet need.…”

Section: Introductionmentioning

confidence: 99%

OncoSpineSeg: A Software Tool for a Manual Segmentation of Computed Tomography of the Spine on Cancer Patients

Ruiz-España¹,

Moratal²

2017

Computed Tomography - Advanced Applications

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The organ most commonly affected by metastatic cancer is the skeleton, and spine is the site where it causes the highest morbidity. Computer-aided diagnosis (CAD) for detecting and assessing metastatic disease in bone or other spine disorders can assist physicians to perform their decision-making tasks. A precise segmentation of the spine is important as a first stage in any automatic diagnosis task. However, it is a challenging problem to segment correctly an affected spine, and it is a crucial step to assess quantitatively the results of segmentation by comparing them with the results of a manual segmentation, reviewed by one experienced radiologist. This chapter presents the design of a MATLAB-based software for the manual segmentation of the spine. The software tool has a simple and easy to use interface, and it works with either computed tomography or magnetic resonance imaging (MRI). A typical workflow includes loading the image volume, creating multi-planar reconstructions, manually contouring the vertebrae, spinal lesions, intervertebral discs and spinal canal with availability of different segmentation tools, classification of the bone into healthy bone, osteolytic metastases, osteoblastic metastases or mixed lesions, being also possible to classify an object as a false-positive and a 3D reconstruction of the segmented objects.

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Semiautomatic computer-aided classification of degenerative lumbar spine disease in magnetic resonance imaging

Cited by 41 publications

References 43 publications

Fuzzy Clustering Based Segmentation of Vertebrae in T1-Weighted Spinal MR Images

Fuzzy Clustering Based Segmentation of Vertebrae in T1-Weighted Spinal MR Images

Automatic segmentation of the spine by means of a probabilistic atlas with a special focus on ribs suppression

OncoSpineSeg: A Software Tool for a Manual Segmentation of Computed Tomography of the Spine on Cancer Patients

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