MRI Brain Tumor Segmentation System Based on Hybrid Clustering Techniques

Maksoud, Eman A. Abdel; Elmogy, Mohammed; Al-Awadi, Rashid Mokhtar

doi:10.1007/978-3-319-13461-1_38

Cited by 11 publications

(12 citation statements)

References 12 publications

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“…We detected the brain tumor accurately with minimal time in 2D images by our hybrid clustering technique in our proposed medical system [11,12]. We kept in mind to achieve the same objective in detecting the brain tumor on 3D images.…”

Section: Related Workmentioning

confidence: 99%

“…KPSO approach is applied on the resulting 2D slices as, in the same way that we explained in our paper [11,12]. The KPSO approach is the integration of KM and the optimization clustering technique PSO.…”

Section: • Particle Swarm Optimization (Pso): It Is An Optimizing Clumentioning

confidence: 99%

“…The vast majority of the de-noising algorithms assume additive white Gaussian noise. There are some algorithms that designed for Gaussian noise elimination, such as edge-preserving bilateral filter, total variation, and non-local means [11]. In this chapter, the median filter is used.…”

Section: Pre-processing Stagementioning

confidence: 99%

“…Two segmentation techniques we used: thresholding and active contour level set (for more details see [11,12]). We got only the tumor cells from the brain as the lighting areas in a dark background on the other hand; we got the contouring areas of tumor in the original smoothing image.…”

Section: Segmentation Stagementioning

confidence: 99%

“…Then, the tumor area is extracted by using our proposed KPSO technique [11,12]. After that, the post-processing and segmentation phases are applied.…”

Section: Introductionmentioning

confidence: 99%

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3D Brain Tumor Segmentation Based on Hybrid Clustering Techniques Using Multi-views of MRI

Maksoud

Elmogy

2016

Medical Imaging in Clinical Applications

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3D medical images segmentation is a very difficult task. It may not be accurate and takes extreme time. In this chapter, we accurately detect the brain tumor from 3D MRI image with less time. The 3D image consists of multiple 2D slices. Segmenting each 2D slice by using the 2D techniques gives more accuracy rather than segmenting the whole 3D image. The integration between K-Means and Particle Swarm Optimization was proposed to segment the 2D MRI slices of the 3D MRI image. We solved the time problem of segmenting all 2D slices of the 3D image. The experiments emphasized the effectiveness of our proposed system in segmenting the 2D and 3D medical images. It achieved 100 % accuracy for the tested 3D dataset and 98.75 % average accuracy for all tested 2D and 3D datasets. The proposed integration reduced time by a mean of 10 min for the tested 2D and 3D datasets.

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Section: Related Workmentioning

confidence: 99%

Section: • Particle Swarm Optimization (Pso): It Is An Optimizing Clumentioning

confidence: 99%

Section: Pre-processing Stagementioning

confidence: 99%

Section: Segmentation Stagementioning

confidence: 99%

“…Then, the tumor area is extracted by using our proposed KPSO technique [11,12]. After that, the post-processing and segmentation phases are applied.…”

Section: Introductionmentioning

confidence: 99%

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