Automatic active contour segmentation approach via vector field convolution

Bakir, Houda; Charfi, M.; Zrida, Jalel

doi:10.1007/s11760-014-0695-7

Cited by 4 publications

(4 citation statements)

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“…ACM-based methods are efficient and suited for any type of image [41,71,118]. However, they are computationally/memory expensive and sensitive to the initialization [19,52,77]. Similar to the level-set method, ACM works best with 3D MRI images [14,19].…”

Section: Discussionmentioning

confidence: 99%

“…In their formulations, frequently used hybrid models either integrate region edge or region-based (local as well as global) information or both. For instance, a hybrid algorithm consisting of iterative (19)…”

Section: Hybrid Segmentation Methodsmentioning

confidence: 99%

“…In this model, it is necessary for the number of objects to be segmented to be known beforehand. To resolve the problem of initialization, Bakir et al [19] proposed the splitting of the B-snake and automatic initialization based on Distktra's algorithm as well as divergence of the vector field convolution (VFC). In an image region, the negative divergence values are indicative of the presence of the object's contour, while the positive values denote a separation between them.…”

Section: Parametric Active Contourmentioning

confidence: 99%

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Biomedical Image Segmentation: A Survey

Alzahrani

Boufama

2021

SN COMPUT. SCI.

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Medical Image Segmentation is the process of segmenting and detecting boundaries of anatomical structures in various types of 2D and 3D-medical images. The latter come from different modalities, such as Magnetic Resonance Imaging (MRI), X-Rays, Positron Emission Tomography (PET)/Single-Photon Emission Computed Tomography, Computed Tomography (CT), and Ultrasound (US). It is a key supporting technology for medical applications including diagnostics, planning, monitoring, and guidance. Hence, a large number of segmentation methods have been published in past decades. This paper presents a comprehensive review of the current medical segmentation techniques. In particular, we reviewed the most important medical segmentation methods that have been utilized for almost all types of medical images. We grouped these methods into categories and then compared, contrasted, and highlighted their main advantages and limitations.

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Section: Discussionmentioning

confidence: 99%

Section: Hybrid Segmentation Methodsmentioning

confidence: 99%

Section: Parametric Active Contourmentioning

confidence: 99%

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Biomedical Image Segmentation: A Survey

Alzahrani

Boufama

2021

SN COMPUT. SCI.

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“…In recent years, classification and clustering have also been used for medical image segmentation, and the relevant research focuses on the improvement of robustness [ 16 – 18 ]. Methods based on deformable models [ 19 – 25 ] and active shape models [ 26 – 30 ] have become a hot topic. For example, Calder et al [ 20 ]proposed a segmentation method based on level set.…”

Section: Introductionmentioning

confidence: 99%

The segmentation of bones in pelvic CT images based on extraction of key frames

Wang

Shi

et al. 2018

BMC Med Imaging

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BackgroundBone segmentation is important in computed tomography (CT) imaging of the pelvis, which assists physicians in the early diagnosis of pelvic injury, in planning operations, and in evaluating the effects of surgical treatment. This study developed a new algorithm for the accurate, fast, and efficient segmentation of the pelvis.MethodsThe proposed method consists of two main parts: the extraction of key frames and the segmentation of pelvic CT images. Key frames were extracted based on pixel difference, mutual information and normalized correlation coefficient. In the pelvis segmentation phase, skeleton extraction from CT images and a marker-based watershed algorithm were combined to segment the pelvis. To meet the requirements of clinical application, physician’s judgment is needed. Therefore the proposed methodology is semi-automated.ResultsIn this paper, 5 sets of CT data were used to test the overlapping area, and 15 CT images were used to determine the average deviation distance. The average overlapping area of the 5 sets was greater than 94%, and the minimum average deviation distance was approximately 0.58 pixels. In addition, the key frame extraction efficiency and the running time of the proposed method were evaluated on 20 sets of CT data. For each set, approximately 13% of the images were selected as key frames, and the average processing time was approximately 2 min (the time for manual marking was not included).ConclusionsThe proposed method is able to achieve accurate, fast, and efficient segmentation of pelvic CT image sequences. Segmentation results not only provide an important reference for early diagnosis and decisions regarding surgical procedures, they also offer more accurate data for medical image registration, recognition and 3D reconstruction.

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