A Semi-automatic Multi-seed Region-Growing Approach for Uterine Fibroids Segmentation in MRgFUS Treatment

Militello, Carmelo; Vitabile, Salvatore; Russo, G.; Candiano, G.; Gagliardo, Cesare; Midiri, Massimo; Gilardi, M.C.

doi:10.1109/cisis.2013.36

Cited by 11 publications

(7 citation statements)

References 14 publications

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“…For comparison, we developed four different semiautomatic segmentation approaches: edge detection (Canny, ), after bounding region selection; adaptive thresholding (Otsu, ), after bounding region selection; region growing (Militello et al, ), after seed‐point selection; interactive level set (Li et al, ), after initial region selection. …”

Section: Resultsmentioning

confidence: 99%

Gamma Knife treatment planning: MR brain tumor segmentation and volume measurement based on unsupervised Fuzzy C-Means clustering

Militello

Rundo

Vitabile

et al. 2015

Int. J. Imaging Syst. Technol.

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Nowadays, radiation treatment is beginning to intensively use MRI thanks to its greater ability to discriminate healthy and diseased soft-tissues. Leksell Gamma Knife® is a radio-surgical device, used to treat different brain lesions, which are often inaccessible for conventional surgery, such as benign or malignant tumors. Currently, the target to be treated with radiation therapy is contoured with slice-by-slice manual segmentation on MR datasets. This approach makes the segmentation procedure time consuming and operator-dependent. The repeatability of the tumor boundary delineation may be ensured only by using automatic or semiautomatic methods, supporting clinicians in the treatment planning phase. This article proposes a semiautomatic segmentation method, based on the unsupervised Fuzzy C-Means clustering algorithm. Our approach helps segment the target and automatically calculates the lesion volume. To evaluate the performance of the proposed approach, segmentation tests on 15 MR datasets were performed, using both area-based and distance-based metrics, obtaining the following average values: Similarity Index=95.59%, Jaccard Index=91.86%, Sensitivity=97.39%, Specificity=94.30%, Mean Absolute Distance=0.246[pixels], Maximum Distance=1.050[pixels], and Hausdorff Distance=1.365[pixels]

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Section: Resultsmentioning

confidence: 99%

Gamma Knife treatment planning: MR brain tumor segmentation and volume measurement based on unsupervised Fuzzy C-Means clustering

Militello

Rundo

Vitabile

et al. 2015

Int. J. Imaging Syst. Technol.

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“…Therefore, segmentation threshold Θ is a region-dependent parameter that guides the homogeneity decision test [95]. These criteria can be local (i.e., pixel intensity [416]) or region descriptor-based (region shape or size, likeness between a candidate pixel and the current grown region in terms of image features [201]) that are more powerful because they take into account the "history" of region growth when some a priori knowledge about the region is available. As explained in [3], to prevent a poor starting estimate, it is recommended that seed-regions be used instead of single pixels (seed-points).…”

Section: Region Growingmentioning

confidence: 99%

“…In recent years, our research group has already proposed computer-assisted segmentation approaches for uterine fibroids in MRgFUS treatments [415,416,524]. The clinical feasibility of these methodologies was evaluated and critically discussed in [522].…”

Section: Mrgfus Treatments For Uterine Fibroidsmentioning

confidence: 99%

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MedGA: A novel evolutionary method for image enhancement in medical imaging systems

Rundo

Tangherloni

Nobile

et al. 2019

Expert Systems with Applications

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102

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Medical imaging systems often require the application of image enhancement techniques to help physicians in anomaly/abnormality detection and diagnosis, as well as to improve the quality of images that undergo automated image processing. In this work we introduce MedGA, a novel image enhancement method based on Genetic Algorithms that is able to improve the appearance and the visual quality of images characterized by a bimodal gray level intensity histogram, by strengthening their two underlying sub-distributions. MedGA can be exploited as a pre-processing step for the enhancement of images with a nearly bimodal histogram distribution, to improve the results achieved by downstream image processing techniques. As a case study, we use MedGA as a clinical expert system for contrast-enhanced Magnetic Resonance image analysis, considering Magnetic Resonance guided Focused Ultrasound Surgery for uterine fibroids. The performances of MedGA are quantitatively evaluated by means of various image enhancement metrics, and compared against the conventional state-of-the-art image enhancement techniques, namely, histogram

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“…A semiautomatic method of region growing has previously been applied to problem. 8 In region growing, the operator identifies the target by placing a number of seed points that are grown to larger regions by iteratively adding neighboring voxels with similar properties to the region. Although region growing-based techniques are able to label consistent areas with similar properties, the results are generally highly dependent on the seed selection and similarity parameter.…”

Section: Introductionmentioning

confidence: 99%

Automatic segmentation for detecting uterine fibroid regions treated with MR-guided high intensity focused ultrasound (MR-HIFU)

et al. 2014

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A Semi-automatic Multi-seed Region-Growing Approach for Uterine Fibroids Segmentation in MRgFUS Treatment

Cited by 11 publications

References 14 publications

Gamma Knife treatment planning: MR brain tumor segmentation and volume measurement based on unsupervised Fuzzy C-Means clustering

Gamma Knife treatment planning: MR brain tumor segmentation and volume measurement based on unsupervised Fuzzy C-Means clustering

MedGA: A novel evolutionary method for image enhancement in medical imaging systems

Automatic segmentation for detecting uterine fibroid regions treated with MR-guided high intensity focused ultrasound (MR-HIFU)

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