3D brain tumor segmentation scheme using K-mean clustering and connected component labeling algorithms

Hossam, Mohamed; Hassanien, Aboul Ella; Shoman, Mohamoud

doi:10.1109/isda.2010.5687244

Cited by 27 publications

(16 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The latter of these are generally preferred. These include northeast and northwest pixels [25][26][27]. A flow chart for segmentation using the CCL algorithm is shown in Figure 3.…”

Section: Methodsmentioning

confidence: 99%

Spinal Cord Based Kidney Segmentation Using Connected Component Labeling and K-Means Clustering Algorithm

Tuncer¹,

Alkan²

2019

View full text Add to dashboard Cite

Abdominal computed tomography (CT) data are often used in the diagnosis and treatment of patients. Segmentation of viscera on abdominal imaging facilitates diagnosis and focus upon the areas of interest. Kidney segmentation by abdominal imaging is complicated by the proximity of various organs and the similarities between abdominal tissues. Here we propose two fully automated approaches to kidney segmentation and discuss their performance. A fully automated approach was preferred to accelerate the decision-making process of the physician to eliminate the disadvantage of manual and semi-automatic segmentations. Each of the proposed methods essentially consist of three stages. Since the spine was used as reference in the study, the images were first treated to define the coordinates of the spine. Second, kidney fields were obtained using the Connected Component Labeling (CCL) and the K-means clustering algorithms. Last, the kidneys were segmented by applying different filters according to the method. A manual segmentation was then performed by specialist physicians. The performance of the tested algorithms was made by comparison to the manual segmentation results, using the Dice Similarity Coefficient, the Figures of Merit and Jaccard Similarity Index. Based on our analyses, acceptable success rates were achieved by the proposed methodologies. These automated systems are expected to be helpful during clinical diagnosis, medical training and future studies on kidney cancer diagnosis.

show abstract

“…The latter of these are generally preferred. These include northeast and northwest pixels [25][26][27]. A flow chart for segmentation using the CCL algorithm is shown in Figure 3.…”

Section: Methodsmentioning

confidence: 99%

Spinal Cord Based Kidney Segmentation Using Connected Component Labeling and K-Means Clustering Algorithm

Tuncer¹,

Alkan²

2019

View full text Add to dashboard Cite

show abstract

“…In this paper, An image segmentation scheme to segment 3D brain tumor from MRI images through the clustering process [11]. The clustering is achieved using K-mean algorithm in conjunction with the connected component labeling algorithm to link the similar clustered objects in all 2D slices and then obtain 3D segmented tissue using the patch object rendering process.…”

Section: Component Labelling Algorithmmentioning

confidence: 99%

Survey Paper on Clustering based Segmentation Approach to Detect Brain Tumour from MRI Scan

Gaikwad¹,

Debbarma²,

Thigale³

2015

IJCA

View full text Add to dashboard Cite

In the last few years, many image processing techniques have been presented in order to perform different brain tumor detection tasks. These cover Content-Based Retrieval Technique, Component Labelling Algorithm, Fuzzy C-Mean Algorithm. There is fast growth of image processing available in last few years; image segmentation have work together to get extraction of meaningful data into useful information. Image processing is used to finding applicable and efficient information from data available. With this survey paper we focus on literature survey of segmentation based image processing model as contain efficient performance.

show abstract

“…Connected components analysis based methods have aided image segmentation for the purpose of text detection [16][17][18], brain tissue analysis [19,20] and colour image segmentation using genetic algorithms [21], whereas in our proposed work, we use connected components analysis to refine the boundary map by removing small, isolated connected components (artefacts) which may yield incorrect depth estimates. Our approach may seem similar to [19], where the authors first roughly distinguish the region of non-brain tissue through connected component labelling, and then try to refine those edges using the morphological operations, dilation and erosion.…”

Section: Related Workmentioning

confidence: 99%

Depth-Based Selective Blurring in Stereo Images Using Accelerated Framework

Mukherjee

Guddeti

2014

3D Res

View full text Add to dashboard Cite

We propose a method for depth-based selective blurring of non-interest regions of stereo images using CPU-GPU accelerated framework based on Java Thread Pool and APARAPI.Our method combines both block and region-based stereo matching approaches and generates depth maps using disparity measurements of only 18% image pixels (left or right). The methodology involves segmenting pixel lightness values using fast K-Means implementation, refining the segment boundaries using morphological filtering and connected components analysis; then determining the boundaries' disparities using SAD cost function. Complete disparity maps are reconstructed from these boundaries' disparities. Gaussian blur is applied to de-focus image regions lying in depth ranges of user's non-interest. Experimental results from Middlebury dataset show that our method outperforms traditional disparity estimation approaches using SAD and NCC by up to 33.6% and some recent methods by up to 6.1%. Further, our acceleration framework achieves speed-up of 5.8 for 250 stereo video frames (4096x2304 resolution).

show abstract

3D brain tumor segmentation scheme using K-mean clustering and connected component labeling algorithms

Cited by 27 publications

References 6 publications

Spinal Cord Based Kidney Segmentation Using Connected Component Labeling and K-Means Clustering Algorithm

Spinal Cord Based Kidney Segmentation Using Connected Component Labeling and K-Means Clustering Algorithm

Survey Paper on Clustering based Segmentation Approach to Detect Brain Tumour from MRI Scan

Depth-Based Selective Blurring in Stereo Images Using Accelerated Framework

Contact Info

Product

Resources

About